Geospatial Data, Information, and Intelligence 9781630819798

Table of contents :
Geospatial Data, Information, and Intelligence
Contents
Foreword
Preface
Acknowledgments
1
Introduction to the Geospatial Mindset, Toolset, and Skill Set
1.1 The Case for Geospatial
1.1.1 Defining Geospatial and Related Terms
1.1.2 Delineating Geospatial Analysis: Spatial and Imagery
1.2 The Information Age
1.3 The Locational Data-to-Information Refinement Process
1.4 The Location Mindset
1.5 The Geospatial Toolset
1.6 The Geospatial Skill Set
1.7 Flourishing in the Information Age
References
2
The Location Mindset
2.1 Introduction to the Location Mindset
2.1.1 Prioritization
2.1.2 Collection
2.1.3 Transformation
2.1.4 Visualization
2.1.5 Locations Are Widely Available
2.1.6 Geospatial Locations Are Universal
2.1.7 Locations Can Be Highly Accurate
2.1.8 The Electronic Grid
2.1.9 Location Initiates Geospatial Observations and Analysis
2.2 Using Spatial and Geospatial Thinking
2.2.1 Spatial Thinking: Definition
2.2.2 Spatial and Geospatial Thinking in History
2.2.3 Spatial Thinking: Purpose and Practice
2.2.4 Geospatial Thinking: Definition
2.2.5 Geospatial Thinking: Purpose and Practice
2.2.6 Improving Spatial and Geospatial Thinking Through Reasoning
2.3 Conclusion
References
3
The Geospatial Toolset
3.1 Introduction to the Geospatial Toolset
3.2 Geospatial Data
3.2.1 Geospatial Data Background
3.2.2 Global Emphasis on Geospatial Data
3.2.3 Geospatial Data Categories
3.2.4 Geospatial Data: Embedded in Our Everyday
3.2.5 The Geospatial Data Setup
3.3 Geospatial Sensors
3.3.1 Machines: Remote Sensors
3.3.2 Machines: Direct Sensors
3.3.3 Human Collection of Location Data
3.4 Geospatial Systems
3.4.1 Geospatial Systems: A Recipe for Success
3.5 Geospatial Hardware
3.6 Geospatial Software
3.7 The Importance of People in the Geospatial Toolset
3.8 Conclusion
References
4
The Geospatial Skill Set: Observation Principles
4.1 Introduction to Geospatial Observations
4.2 Defining Geospatial Observations
4.3 Geospatial Observations: Purpose and
General Practice
4.4 Geospatial Observation Principles
4.4.1 Directed Observations: Collection Driven by Target Understanding
4.4.2 The Importance of Visualization
4.4.3 Optimizing Conditions: Focused Attention Improves Refinement
4.4.4 The Importance of Pairing Locations and Visualizations
4.4.5 Observational Uncertainty as a Default Position
4.4.6 Reference to Resolve
4.5 The Pitfalls of Visualization
4.5.1 Pitfalls of Geospatial Data: Imagery
4.5.2 Pitfalls of Geospatial Data on Maps
4.6 Conclusion
References
5
The Geospatial Skill Set: Observation Practices
5.1 Introduction to Geospatial Observation
Practices
5.2 SGOT
5.2.1 The Four Cornerstones for Observations
5.2.2 Slow Observations
5.2.3 Observational Perspective
5.2.4 Focal Point Control
5.2.5 Observational Reasoning
5.2.6 Observational Notations and Communications
5.2.7 Observation of Process Flows
5.2.8 Observable Keys
5.3 External Versus Internal Observations
5.4 Tradecraft Examples for Observation
5.4.1 Imagery-Based BAS
5.4.2 Geospatial Change Observation
5.5 Conclusion
References
6 The Geospatial Skill Set: Analysis Principles
6.1 Introduction to Geospatial Analysis Principles
6.2 Defining Geospatial Analysis
6.3 The Purpose of Geospatial Analysis
6.4 Foundational Principles of Geospatial Analysis
6.4.1 Identification
6.4.2 Relation
6.4.3 Context
6.4.4 Uncertainty
6.5 Geospatial Analytic Methodologies
6.5.1 Imagery Analysis
6.5.2 Spatial Analysis
6.6 Conclusion
References
7 The Skill Set: Geospatial Analysis Practices
7.1 Introduction to Geospatial Analysis Practices
7.2 Geospatial Analysis as a Profession: Imagery and Spatial Analysis Tradecraft
7.2.1 Imagery Analysis Tradecraft
7.2.2 Spatial Analysis Tradecraft
7.2.3 Merging Imagery and Spatial Analysis Tradecraft
7.3 SGATs
7.3.1 Find, Link, and Layer Locations
7.3.2 Analyzing Entities Using the Four Cornerstones
7.3.3 Analyzing for Relationships
7.3.4 Geospatial Analytic Reasoning
7.3.5 Analysis: Creating Observable Keys
7.3.6 Analysis for Geospatial Collection
7.3.7 Analytic Communications and Review
7.4 Conclusion
References
8 The Geospatial Skill Set: Communication Principles
8.1 Introduction to Geospatial Communications Principles
8.2 Defining Geospatial Communication
8.3 Purpose of a Geospatial Communication
8.4 Geospatial Communication Principles
8.4.1 Knowing One’s Audience and Purpose
8.4.2 Unfinished Versus Finished Geospatial Communications
8.4.3 Distillation of Communications
8.4.4 Communication Through Visualizations
8.4.5 Presentation
8.5 Foundations of a Finished Geospatial Communication
8.5.1 Location
8.5.2 Time
8.5.3 Entity
8.5.4 Sourcing
8.6 Conclusion
9 The Geospatial Skill Set: Communication Practices
9.1 Introduction to Geospatial Communications Practices
9.2 Structured Geospatial Communication Techniques
9.2.1 Distilling the Geospatial Communiction
9.2.2 Assessing the Audience
9.2.3 Writing
9.2.4 The Four Cornerstones for Geospatial Text
9.2.5 Graphics
9.2.6 Presentations
9.2.7 Communicating Uncertainty
9.2.8 Geospatial Confidence Communication
9.2.9 Building the Product
9.2.10 Multilayered Peer Review for Communication
9.3 Conclusion
References
10
Outlook
10.1 Geospatial Advancement
10.2 Visualizing the Next Geospatial Horizon
10.3 Location: A Central Feature of Our Future
About the Authors
Index

Citation preview

Geospatial Data, Information, and Intelligence

For a complete listing of titles in the Artech House Intelligence and Information Operations Library, turn to the back of this book.

Geospatial Data, Information, and Intelligence Aaron Jabbour Renny Babiarz

Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the U.S. Library of Congress. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library. Cover design by Andy Meaden

ISBN 13: 978-1-63081-979-8

© 2023 ARTECH HOUSE 685 Canton Street Norwood, MA 02062

All rights reserved. Printed and bound in the United States of America. No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the publisher.   All terms mentioned in this book that are known to be trademarks or service marks have been appropriately capitalized. Artech House cannot attest to the accuracy of this information. Use of a term in this book should not be regarded as affecting the validity of any trademark or service mark.

10 9 8 7 6 5 4 3 2 1

Aaron Jabbour would like to dedicate this book to the memory of his father, Alan Jabbour, who inspired in him a thirst for knowledge and a love for geography and the natural world. His mother, Karen Jabbour, who inspired in him a love for the spoken and written word, and the visual world. His wife, Adelita Vucetic, whose love and patience completes the perfect partnership. And his loving daughters, Lily and Adira, who refresh the spirit of exploration and discovery in him each day and keep him young at heart.

Renny Babiarz would like to dedicate this book to his family, especially his wife Amanda for all her unflagging support and his children who remind him to get outside, roam, and play. Renny also dedicates this book to geospatial analysts everywhere, including his coauthor, Aaron Jabbour, and other members of the original “A Team,” who inspire others to explore the world and to study our most important problems. Their work makes all of us safer and more secure.

Contents

Foreword

xiii



Preface



Acknowledgments

1

Introduction to the Geospatial Mindset, Toolset, and Skill Set

1

1.1 1.1.1 1.1.2

The Case for Geospatial Defining Geospatial and Related Terms Delineating Geospatial Analysis: Spatial and Imagery

1 3 4

1.2

The Information Age

5

1.3

The Locational Data-to-Information Refinement Process 5

1.4

The Location Mindset

6

1.5

The Geospatial Toolset

7

1.6

The Geospatial Skill Set

7

1.7

Flourishing in the Information Age

8

xv

References

xix

8

vii

viii

Geospatial Data, Information, and Intelligence

2

The Location Mindset

11

2.1 2.1.1 2.1.2 2.1.3 2.1.4 2.1.5 2.1.6 2.1.7 2.1.8 2.1.9

Introduction to the Location Mindset Prioritization Collection Transformation Visualization Locations Are Widely Available Geospatial Locations Are Universal Locations Can Be Highly Accurate The Electronic Grid Location Initiates Geospatial Observations and Analysis

11 12 13 13 13 14 15 16 17 18

2.2 2.2.1 2.2.2 2.2.3 2.2.4 2.2.5 2.2.6

Using Spatial and Geospatial Thinking Spatial Thinking: Definition Spatial and Geospatial Thinking in History Spatial Thinking: Purpose and Practice Geospatial Thinking: Definition Geospatial Thinking: Purpose and Practice Improving Spatial and Geospatial Thinking Through Reasoning

20 20 20 21 22 22

2.3

Conclusion

28

References

23

29

3

The Geospatial Toolset

31

3.1

Introduction to the Geospatial Toolset

31

3.2 3.2.1 3.2.2 3.2.3 3.2.4 3.2.5

Geospatial Data Geospatial Data Background Global Emphasis on Geospatial Data Geospatial Data Categories Geospatial Data: Embedded in Our Everyday The Geospatial Data Setup

31 32 33 34 38 41

3.3 3.3.1 3.3.2 3.3.3

Geospatial Sensors Machines: Remote Sensors Machines: Direct Sensors Human Collection of Location Data

42 43 44 45

3.4 3.4.1

Geospatial Systems Geospatial Systems: A Recipe for Success

46 46



Contents

ix

3.5

Geospatial Hardware

47

3.6

Geospatial Software

47

3.7

The Importance of People in the Geospatial Toolset

48

3.8

Conclusion

49

References

49

4

The Geospatial Skill Set: Observation Principles

51

4.1

Introduction to Geospatial Observations

51

4.2

Defining Geospatial Observations

51

4.3

Geospatial Observations: Purpose and General Practice

52

4.4 4.4.1 4.4.2 4.4.3 4.4.4 4.4.5 4.4.6

Geospatial Observation Principles Directed Observations: Collection Driven by Target Understanding The Importance of Visualization Optimizing Conditions: Focused Attention Improves Refinement The Importance of Pairing Locations and Visualizations Observational Uncertainty as a Default Position Reference to Resolve

53

4.5 4.5.1 4.5.2

The Pitfalls of Visualization Pitfalls of Geospatial Data: Imagery Pitfalls of Geospatial Data on Maps

60 62 64

4.6

Conclusion

65

References

55 55 56 56 58 60

66

5

The Geospatial Skill Set: Observation Practices

67

5.1

Introduction to Geospatial Observation Practices

67

5.2 5.2.1 5.2.2 5.2.3 5.2.4 5.2.5

SGOT The Four Cornerstones for Observations Slow Observations Observational Perspective Focal Point Control Observational Reasoning

67 68 78 80 82 84

x

Geospatial Data, Information, and Intelligence

5.2.6 5.2.7 5.2.8

Observational Notations and Communications Observation of Process Flows Observable Keys

88 90 91

5.3

External Versus Internal Observations

94

5.4 5.4.1 5.4.2

Tradecraft Examples for Observation Imagery-Based BAS Geospatial Change Observation

95 96 100

5.5

Conclusion

103

References

105

6

The Geospatial Skill Set: Analysis Principles

107

6.1

Introduction to Geospatial Analysis Principles

107

6.2

Defining Geospatial Analysis

108

6.3

The Purpose of Geospatial Analysis

108

6.4 6.4.1 6.4.2 6.4.3 6.4.4

Foundational Principles of Geospatial Analysis Identification Relation Context Uncertainty

109 110 111 112 113

6.5 6.5.1 6.5.2

Geospatial Analytic Methodologies Imagery Analysis Spatial Analysis

115 116 117

6.6

Conclusion

117

References

118

7

The Skill Set: Geospatial Analysis Practices

119

7.1

Introduction to Geospatial Analysis Practices

119

7.2 7.2.1 7.2.2 7.2.3

Geospatial Analysis as a Profession: Imagery and Spatial Analysis Tradecraft Imagery Analysis Tradecraft Spatial Analysis Tradecraft Merging Imagery and Spatial Analysis Tradecraft

120 120 133 140

7.3

SGATs

141



Contents

xi

7.3.1 7.3.2 7.3.3 7.3.4 7.3.5 7.3.6 7.3.7

Find, Link, and Layer Locations Analyzing Entities Using the Four Cornerstones Analyzing for Relationships Geospatial Analytic Reasoning Analysis: Creating Observable Keys Analysis for Geospatial Collection Analytic Communications and Review

141 146 155 159 162 163 164

7.4

Conclusion

167

References

168

8

The Geospatial Skill Set: Communication Principles

171

8.1

Introduction to Geospatial Communications Principles 171

8.2

Defining Geospatial Communication

172

8.3

Purpose of a Geospatial Communication

172

8.4 8.4.1 8.4.2 8.4.3 8.4.4 8.4.5

Geospatial Communication Principles 173 Knowing One’s Audience and Purpose 174 Unfinished Versus Finished Geospatial Communications 174 Distillation of Communications 175 Communication Through Visualizations 175 Presentation 176

8.5 8.5.1 8.5.2 8.5.3 8.5.4

Foundations of a Finished Geospatial Communication Location Time Entity Sourcing

176 177 177 177 177

8.6

Conclusion

178

9

The Geospatial Skill Set: Communication Practices

179

9.1

Introduction to Geospatial Communications Practices

179

9.2 9.2.1 9.2.2 9.2.3 9.2.4 9.2.5

Structured Geospatial Communication Techniques Distilling the Geospatial Communication Assessing the Audience Writing The Four Cornerstones for Geospatial Text Graphics

180 180 181 183 186 189

xii

Geospatial Data, Information, and Intelligence

9.2.6 9.2.7 9.2.8 9.2.9 9.2.10

Presentations Communicating Uncertainty Geospatial Confidence Communication Building the Product Multilayered Peer Review for Communication

193 197 203 206 212

9.3

Conclusion

213

References

214

10

Outlook

215

10.1

Geospatial Advancement

215

10.2

Visualizing the Next Geospatial Horizon

216

10.3

Location: A Central Feature of Our Future

217



About the Authors

219



Index

221

Foreword This book is the first to connect three important aspects of geospatial intelligence. While other books have addressed the history and context of this industry and profession, Aaron Jabbour and Renny Babiarz have created the first book that leads students and anyone else interested in geospatial intelligence analysis through the essential concepts into the practice of geospatial analysis and toward better intelligence reporting. For those interested in developing geospatial analysts in a workplace, improving their own analysis, or teaching analysis in an academic or training setting, this book fills a pedagogical and professional gap in the literature on geospatial intelligence analysis. Jack O’Connor Program Director of MS in Geospatial Intelligence Advanced Academic Programs Johns Hopkins University Baltimore, Maryland May 2023

xiii

Preface Immediately upon beginning our careers with the U.S. government, we shared a similar “come down to earth” moment. We were amazed at the importance that the organization and all of its employees placed on location for their work. At a broad level, we were required to “bring the Earth in” as a general variable for assessments of important research questions. Yet more specifically, analysts also fretted over the smallest observations at pinpoint locations on the Earth’s surface and engaged in heated debates about myriad possibilities emerging from each. We learned that minute locations contain outsized importance, for each harbored an important object, event, or entity and was surrounded by concentric circles of relationships and context. These locations catalyzed a complex cascade of analytic depth, the findings of which rippled out to the highest levels of government. After all, if a faint plume emitting from a stack in the Yongbyon Nuclear Research Center was located anywhere else on Earth, would it make headline news and cause global leaders to scramble?1 The importance of location had a powerful effect on both of us, as we had recently emerged from academic social science disciplines that favored different, more abstract approaches. In such disciplines, locale variation and geographic visualizations were deemphasized in favor of top-down economic, political, technical, and cultural convergence theories rife with qualitative judgments and subjective interpretations. Such overarching theories had left us parched, thirsting for a mechanism for meaning with a more solid starting point, one that was grounded in more objective observations, tangible visualizations, and scientific measurements that could provide us with higher resolution and confidence in 1. Mitchell, A., et al., “Satellite Image Shows Renewed Activity at North Korean Nuclear Lab,” NBS News, March 30, 2021. https://www.nbcnews.com/news/world/satellite-image-showsrenewed-activity-north-korean-nuclear-lab-n1262530. xv

xvi

Geospatial Data, Information, and Intelligence

our sense-making and lift us from subjective perspectives towards a more objective understanding of the world. Location awakened our innate desire for concrete accuracy and provided a foundation on which to build knowledge. It was time to put our research questions on the grid, grounding them with an inside-out and bottom-up approach that leads with empirical, objective data and contains a compelling visual component. From the starting point of location, we could deliver more meaningful assessments of global issues that began with simple points and then radiated outwards with relationships and context. It is from this idea that we decided to share our findings with the world in this book, which provides organizing principles and introductory practices that guide the reader through the mindset, toolset, and skill set that can enhance sense-making in an increasingly disorienting world. Chapter 1 outlines the benefits and drawbacks of the Information Age and the solution of geospatial data, information, and intelligence. It starts by defining terms and introducing frameworks for further study. For example, the term “geospatial” means Earth-referenced and is the cornerstone of the vital and emergent field of geospatial analysis. Geospatial analysis is dedicated to understanding precise locations on Earth, the entities in those places, and what it all means. Much of the work done in geospatial analysis begins with geospatial data and involves transforming it into useful information for a variety of customers. To help acquaint and equip the reader, the chapter introduces the geospatial mindset, toolset, and skill set as a framework for conducting the required locational data-to-information refinement process. In Chapters 2 and 3, the book examines the geospatial mindset and toolset. In Chapter 2, the book reveals the efficacy of a location mindset for approaching research inquiries in the Information Age. It informs the reader how to prioritize location and center it in one’s research efforts. Part of prioritizing and centering location is more deliberately conceptualizing and organizing a research project with the intention of finding new locations missing from data, affixing locations to images, converting relative locations in datasets and on pictures to absolute locations, and presenting them in a visual environment that is capable of furthering the data-to-information refinement process. Chapter 3 focuses on the geospatial toolset: the sensors, systems, software, hardware, and people that collect, process, and present the data to the practitioner for visualization and analysis. While Chapter 3 highlights many technical aspects of sensors, systems, software, and hardware, it also focuses on the least appreciated but most important category of the toolset, the people. Beginning in Chapter 4, the book examines the geospatial skill set, a category that will be explored for the remainder of the book through the lens of observations, analysis, and communications (OAC). To further dissect OAC, the book explores principles and then practices of each category in a pattern



Preface

xvii

that repeats until Chapter 10. Chapter 4 defines geospatial observations, states their purpose, and then introduces the principles that should guide practitioners when approaching, conducting, and collecting them. It also examines the brain-eye connection, the position of the human eye atop the sensory hierarchy, and how humanity should approach visual data that is rich in information but rife with pitfalls. Chapter 5 provides practitioners with the practices to conduct and collect geospatial observations that will become the basis of follow-on geospatial analysis. Chapter 6 defines geospatial analysis, states its purpose, and then introduces the principles that should guide practitioners when approaching analytic inquiry that involves locations, entities, relations, and context. Chapter 7 provides the practices of geospatial analysis, complete with visual and technical processes, that will help practitioners to succeed in transforming data into useful information for customers. Chapter 8 defines geospatial communications, provides the purpose for it, and then lays out the principles that practitioners can use to best connect the result of their analysis to a customer. Chapter 9 closes by outlining the best practices for effectively communicating results. Finally, Chapter 10 serves as an outlook that attempts to predict the future of geospatial data, information, and intelligence as the Information Age progresses and the speed, accuracy, and availability of geospatial elements increase. Our vision is to transform laypeople into citizen scientists and practitioners into professionals by introducing and examining a concept that combines location and visualization: geospatial. Our strategy is to use elements of psychology and geography as an effective geospatial data-to-information refinement process to create accurate, high-quality, objective assessments in the increasingly disorienting landscape of the Information Age. In order to effectively transform geospatial data into useful information, laypeople and practitioners can explore knowledge in three categories: the geospatial mindset, toolset, and skill set. This book focuses broadly on the geospatial skill set and examines the three most prevalent skills: observations, analysis, and communications. Each contains principles and practices that require special attention in order to effectively refine data into useful information. It also focuses narrowly on innovative practices such as the Four Cornerstones, which helps a practitioner to systematically examine an entity in order to identify and understand it. Altogether, this book serves as a practical resource for students, practitioners, and seasoned professionals who use location and visualization to improve meaning and mitigate uncertainty in the world. Geospatial solves for where. Our journey begins here.

Acknowledgments We would like to thank our colleagues Andrew McLaren and William Caban, who inspired, reviewed, and contributed to this book in ways that are overtly and covertly reflected on each page. We would also like to thank Jack O’Connor, who helped inspire this project and encouraged us throughout the writing process with thoughtful feedback and veteran advice. We would also like to thank Dr. Barbara Tversky, who was extraordinarily generous with her time and attention. Dr. Tversky’s positive review of our content on spatial thinking provided expert feedback, and her work provides a novel thread that connects the chapters of the book and, more broadly, the fields of psychology, geography, and geospatial analysis. We further thank Charles Herring at AllSource Analysis for supporting our use of various analytic examples, and Anne Pellegrino at Planet for help with obtaining permissions to use Planet imagery in our graphics. In addition, Aaron Jabbour would like to acknowledge Dr. Richard Kohn for the inspiration, advice, and wisdom that would set his career path in motion. Aaron would also like to acknowledge the superb geospatial analytic tradecraft of Josh Pickens and Cory Schleyer, whose expert work contributed to key components of the book.

xix

1 Introduction to the Geospatial Mindset, Toolset, and Skill Set 1.1  The Case for Geospatial On a chilly March morning in Danville, Virginia, a gun-wielding assailant surveilled an unsuspecting victim while sitting in his car in a concealed location. Once the victim appeared in the target area, the assailant opened fire. Multiple shots rang out, and the victim fell to the ground, along with tiny shards of glass from the assailant’s windshield, broken from the fired shots. The assailant might have gotten away with it, except for the digital footprint that he left behind and the persistent law enforcement practitioner who used the geospatial mindset, toolset, and skill set to solve the case. The analyst recognized that the investigation would greatly benefit from geospatial analysis and began collecting tabular datasets representing the suspect’s most frequented fixed (such as dwelling) and moving (such as vehicle and cell phone) locations. The analyst layered that data on a map, which solved for where by revealing the suspect’s locations in space and time. Figure 1.1 presents a map of the suspect’s cell phone and vehicle location at the time of the crime [1]. Finally, the analyst identified on satellite imagery a windshield glass repair shop that the suspect visited shortly after the crime occurred. Figure 1.2 shows an image of the vehicle glass repair shop that the suspect visited shortly after the homicide was committed [2].1 The Commonwealth Attorney of Virginia decided to proceed with the case based on this 1. Both Figures 1.1 and 1.2 are similar to graphics shown to the jury during the Danville murder case. 1

2

Geospatial Data, Information, and Intelligence

Figure 1.1  Map of the suspect’s cell phone and vehicle location at the time of the crime [1].

Figure 1.2  Image from Danville case showing the suspect’s vehicle at windshield glass repair shop after the homicide [2].

evidence and eventually called the analyst to testify how the combination of imagery and maps helped to relate the suspect’s locations to key events in the case timeline, including the murder itself. Based on the presented evidence, the jury convicted the suspect of first-degree murder.



Introduction to the Geospatial Mindset, Toolset, and Skill Set

3

The following day, a local newspaper covered the case and provided a glimpse into the role geospatial analysis played in solving the case [3]: “It took less than two hours following a two-day trial for a jury to find (the suspect) guilty of first-degree murder in the March 4, 2020, fatal shooting of (the victim) on Summit Road in Danville. The jury recommended a 33-year prison sentence for (the suspect) after a painstaking presentation of cell phone and vehicle location tracking data to prove (the suspect’s) guilt.” The Danville murder case presents just one of the many use cases for geospatial data, information, and intelligence in the Information Age. It demonstrates how geospatial principles and practices can be flexibly applied to a diverse and growing number of research inquiries and career fields. Those principles and practices are best understood by examining the modern-day mindset, toolset, and skill set required to transform geospatial data into information and deliver assessments to customers across large swaths of career fields and endeavors. This book introduces geospatial principles, practices, and sample workflows for anyone interested in mobilizing the power of geospatial data and analysis in their industry. To begin this introduction, the following sections provide definitions for key geospatial terms and an overview of the geospatial mindset, toolset, and skill set. Finally, it concludes by demonstrating how these elements can help practitioners to successfully transform locational data into useful information and assessments that will allow their organization and industry to thrive. 1.1.1  Defining Geospatial and Related Terms

The word geospatial means “Earth-referenced,” and refers to a flourishing grouping of principles and practices that use precise locations to better understand the world.2 While the word geospatial emphasizes the importance of location, it is the practice of geospatial analysis that examines the locations and entities on the Earth’s surface and relies on data-driven visualizations to power research, drive assessments, and compel audiences. Geospatial analysis is a field that includes numerous subdisciplines and professional trades, but this book will provide introductory principles and practices for two of them: imagery analysis and spatial analysis. Imagery analysis is the examination of literal visual data (for example, satellite imagery) to gain 2. The word geospatial is a combination of the prefix geo and the word spatial. The prefix geo means Earth and is commonly found in words such as geography, which is the study of the Earth. Spatial is an adjective that means space, or more specifically, relating to or occupying space. The term spatial is commonly used in academic fields such as psychology and geography to describe objects, entities, and phenomena with reference to the space in which they are located and occupy. A simple combination of geo and spatial therefore means Earth space. This construct hints at a hidden concept: reference. Adding the prefix geo to spatial emphasizes the priority of a reference on Earth (in space). This underscores the aforementioned broader concept that geospatial means: understood from an Earth-based perspective (noun).

4

Geospatial Data, Information, and Intelligence

greater understanding of an issue. Spatial analysis is the examination of nonliteral location-based data (for example, a map) to gain greater understanding of an issue. Both disciplines incorporate geospatial data as input that is collected from various sources. Geospatial data are the Earth-referenced facts, figures, and raw materials that make up the building blocks of information. Once the data is acquired, practitioners use manual and technical tools during the course of geospatial observation and analysis to transform that data into useful geospatial information that can be communicated as assessments to broader audiences. Manual tools that aid in geospatial analysis include the human eye and mind, and technical tools include computer software such as Geographic Information Systems (GIS) and Electronic Light Tables (ELT), all of which will be explored in more detail later in the book. Geospatial intelligence is a term that describes specialized collection, processing, analysis, production, and dissemination of Earth-referenced entities, events, and phenomena, usually by government entities. Geospatial intelligence is a field that crosscuts most other disciplines and brings them together under the umbrella of analyzing and visualizing people and things through the lens of place and time. Recently, as cost has decreased and demand has increased, private organizations that support governmental and nongovernmental objectives have increasingly adopted geospatial data, information, and intelligence priorities. 1.1.2  Delineating Geospatial Analysis: Spatial and Imagery

This book refers to geospatial analysis as a field that combines two subordinate geospatial disciplines: imagery analysis and spatial analysis.3 While the similarities in imagery analysis and spatial analysis are in their reliance on location and visualization, the difference is their starting point and directional flow. When conducting imagery analysis, the practitioner’s starting point is the observation of literal data derived from entities on Earth. Discovery, in imagery analysis, lies in observing and identifying entities to establish what is. The pathway towards greater understanding by use of imagery analysis begins with an entity, then flows to location for grounding, and then ripples outward in search of relationships and context. When conducting spatial analysis, the practitioner’s starting point is the acquisition and processing of location-enriched data with predefined attributes and values. The discovery element in spatial analysis involves revealing the measurements and relations of what already is, or at least what is recorded in the dataset, for further understanding. The pathway towards understanding by use of spatial analysis begins by grounding data in locations, then flows 3. This book refers to imagery analysis and spatial analysis both as methodologies for conducting research and as professional trades. Chapter 7 expands upon imagery and spatial analysis from a tradecraft perspective.



Introduction to the Geospatial Mindset, Toolset, and Skill Set

5

to visualization for examination of the entities and their attributes, and then ripples outward in search of relationships and context. Although the two methodologies do not always follow this chronological construct and can be practiced separately, it should now be more clear how this marriage of methodologies combining the strengths of imagery analysis with those of spatial analysis yielded the powerful resulting field of study known as geospatial analysis. Practitioners can use geospatial analysis as an effective practice to better understand the complex and challenging issues presented in the Information Age.

1.2  The Information Age This book presents the case for geospatial analysis as one viable solution to the modern-day inundation of overwhelming amounts of data, especially when that data contains locations. The modern era is characterized as an Information Age dominated by sensors, systems, computers, and devices that are collecting and creating a seemingly ever-increasing amount of data. Satellites and cameras record pictures and videos that are shared across a huge array of platforms from social media to secret screens. Sensors and devices capture droves of records and send them to giant databases for storage. This data, while vast and often unstructured, is capable of refinement in many ways. Once the data integrates absolute locations, making it geospatial data, it can be further transformed through geospatial observations, analysis, and communication, into useful information and assessments that will advance humanity’s knowledge. Yet how does humanity handle increases in data when time and attention span are fixed limitations? Further, what are the required geospatial analysis principles and practices that promise to transform geospatial data into useful information? To address these questions, this book presents the locational datato-information refinement process as an overarching concept that laypeople and practitioners can use to transform large volumes of data into meaningful informational products for customers.

1.3  The Locational Data-to-Information Refinement Process The locational data-to-information refinement process integrates concepts of location from psychology, neuroscience, and geography and then provides practitioners with a series of principles and practices that guide them through an occupationally oriented process. In the fields of psychology and neuroscience, recent research and publications by Dr. Barbara Tversy [4] and Nobel Prize winning researchers John O’Keefe, May-Britt Moser, and Edvard Moser

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[5] identified location as a foundational element of human consciousness and discovered a neuron-based positioning system that guides human movement and decision-making. In the field of geography, geospatial analysis has emerged as an important subdiscipline that uses location as an objective measure on the Earth’s surface to derive more meaning from the world and its entities. This book integrates insights from these disciplines with geospatial analysis principles and practices, uniting the fields and aiding in the transformation of more obscure data into more useful information. To frame the locational data-to-information refinement process, this book presents three categories of location-based principles and practices: mindset, toolset, and skill set, presented in Figure 1.3.

1.4  The Location Mindset Locations are a foundation from which to answer deeper research questions in space and time, enhance understanding, and reduce uncertainty. The location mindset is a starting point for inquiry in which practitioners combine the power of innate and learned mental skills to unlock meaning in the world’s locations. It is a broadening mindset that integrates insights from psychology and geography to frame deliberation about the prioritization, collection, transformation, and visualization of locations during research. This includes prioritizing locations over other more abstract data points as anchors for meaning, considering the collection of both relative and absolute locational data, and considering

Figure 1.3  Practitioners can achieve a thorough understanding of a geospatial workflow by separating the process into geospatial mindset, toolset, and skill set principles and practices [1, 6].



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how the visualization of locational data will enhance research agendas.4 The strength of the location mindset is derived from location’s most beneficial elements: its innateness, universality, accessibility, and utility. It can be used to engage both innate and learned aspects of spatial thinking and reasoning, and it is a platform on which to build observations, analysis, and communications for shared geospatial assessments.

1.5  The Geospatial Toolset The geospatial toolset consists of the sensors, systems, hardware, software, and people who come together to support the practitioner. The hardware, software, sensors, and systems are the engine of geospatial analysis that allow practitioners to collect, compute, and communicate at the speed of technology. This toolset includes sensors that collect geospatial data, servers and systems that ingest, structure, and store this data according to a certain architecture, and computer hardware and software that drive processing capability. However, humans remain the most valuable tool in the set, as they are necessary for input, innovation, and creativity. Humans working together facilitate objectivity through peer review and provide mentorship that facilitates knowledge transfer within and between organizations. The geospatial toolset is the second element that can help to transform data into useful information.

1.6  The Geospatial Skill Set The geospatial skill set is the third element in which practitioners conduct the bulk of the locational data-to-information refinement process, and focuses on principles and practices of geospatial observations, analysis, and communications (OAC), as shown in Figure 1.4. These principles and practices help practitioners to transform the ambiguity and subjectivity of new data into useful, more objective information and assessments. Observations are sensory experiences and discoveries that form the basis of empirical, objective research. Analysis involves the further scrutiny, organization, and technical processing of observations to reach an assessment. Communication is used throughout the process in unfinished forms to aid in observations and analysis and, finally, in a finished form to connect assessments to an audience. Together, this skill set allows practitioners to derive accurate, original insights from a world full of important locations and powerful visualizations. Best of all, the geospatial skill set requires no prerequisites: a citizen scientist with inquisitiveness and tenacity 4. Relative locational data refers to culturally dependent locations, such as place names or building address systems. Absolute locational data contains geographic coordinate system data (i.e., latitude and longitude).

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Figure 1.4  The locational data-to-information refinement process is primarily achieved through geospatial observations, analysis, and communications.

can train themselves at home and in nature, or a student or practitioner can learn it in a classroom or workplace.

1.7  Flourishing in the Information Age The Danville murder case proved that a practitioner armed with the geospatial mindset, toolset, and skill set can quickly gain the information edge on some of the world’s most difficult challenges. That use case underscores the broader case that this book makes for geospatial analysis as a viable solution to many of the data-driven challenges of the Information Age. The rest of this book provides introductory principles and practices to laypeople and practitioners so that they might also use geospatial data, information, and intelligence to conduct more deliberate observations, deeper analysis, and meaningful communications, for flourishing in the Information Age often begins by placing the importance of location as central to understanding of the world.

References [1] ESRI, ArcGIS Software with Streets (Night) basemap, https://pro.arcgis.com/en/pro-app/ latest/help/mapping/map-authoring/author-a-basemap.htm. [2] ESRI, ArcGIS Software with Imagery basemap, https://pro.arcgis.com/en/pro-app/latest/ help/mapping/map-authoring/author-a-basemap.htm. [3] Crane, J., “Jury Convicts Danville Man in 2020 Deadly Shooting,” Danville Register and Bee, March 11, 2021, https://godanriver.com/news/local/crime-and-courts/juryconvicts-danville-man-in-2020-deadly-shooting/article_a90c7444-82b8-11eb-80b2d7f32728d3e8.html. Accessed December 20, 2022. [4] Tversky, B., Mind in Motion: How Actions Shape Thought, New York: Basic Books, 2019, pp. 68–69.



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[5] Moser, E., E. Kropff, and M. -B. Moser, “Place Cells, Grid Cells, and the Brain’s Spatial Representation System,” Annual Review of Neuroscience, Vol. 31, 2008, pp. 69–89. [6] Planet Explorer. Online imagery streaming platform, https://account.planet.com/.

2 The Location Mindset 2.1  Introduction to the Location Mindset Everything that happens or exists on Earth has a location. This simple principle yields a near endless potential for analysis, as each location harbors not only the object, event, or entity, but is also surrounded by concentric circles of relationships and context. Locations are points, lines, and areas on Earth that are building blocks for accurate and objective research. They may be referred to in relative terms through culturally dependent place names or building address systems or through absolute geospatial measurements using geographic coordinate systems such as latitude and longitude. Relative locations are not geospatial because they are not universally measured to the Earth’s surface; instead, they are assigned to man-made features and understood in a cultural context. However, relative locations such as street addresses can be transformed into absolute geospatial locations through the linking of geographic coordinates. Then geographic coordinates can be projected onto maps, which will immediately provide the practitioner with contextual data and information. Geographic coordinates can further be referenced on imagery to geo-enrich a visualization for the well-prepared practitioner. Location powers cell phone applications for navigation, ride sharing, dating, and real estate; humanity now relies on it for business processes including logistics, operations, and security, and it is vital to weather and traffic reports. Because location is ubiquitous, it should be systematically integrated into research, starting with the adoption of a location mindset. A mindset is akin to a lens through which one perceives the world. A location mindset is a 11

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foundational way of thinking that prioritizes location and uses spatial and geospatial thinking as a foundation for geospatial observation and analysis. Figure 2.1 is an icon of the location mindset that reminds the practitioner to consider the importance of location when conducting research. The location mindset is a starting point for geospatial inquiry in which practitioners bring their research questions “down to earth,” get them on the grid, and initiate the locational data-to-information refinement process.1 More specifically, it trains a practitioner to use the spatial concepts of location from psychology and the geospatial concepts of location from geography to more deeply and deliberately consider the prioritization, collection, transformation, and visualization of locations for a research project. 2.1.1  Prioritization

The most important principle of the location mindset is also the most general: prioritize location across a spectrum of events during the research process. Practitioners should prioritize the discovery of locations at the same level as or higher than the discovery of other factors. Many research endeavors stall as the practitioner attempts to discover who, what, and why. Still others may center on vague, subjective, or abstract foundations for research. Instead, consider first solving for where by prioritizing location in one’s research.

Figure 2.1  The location mindset underscores the importance of location in research endeavors by training practitioners to consider, collect, prioritize, and transform locations.

1. “On the grid” is shorthand for “on the geographic grid,” a concept defined and examined in Section 2.2.2.



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2.1.2  Collection

Cast a wide net to find data with locations and collect as much locational data as possible. Further consider how secondary or tertiary locations can be collected from disparate sources to amplify the primary existing data, which can then help to identify, relate, and contextualize the original locations. Consider the source systems, archives, databases, and software tools that will be needed to fully explore and exploit the locational data. The location mindset requires practitioners to additionally consider collecting data with relative or missing locations, and then develop solutions for finding, improving, and joining locations to items when they are missing from a dataset, image, or document. Practitioners should also collect relative data in which locations are imprecise, consisting of only broad geographic areas such as cities, counties, or other municipalities, and then consider how to improve these locations to make them more precisely reflect a point location such as an address or a geographic coordinate. 2.1.3  Transformation

The location mindset requires an understanding of how to transform locations from relative to absolute and from text and integers to visualizations. This transformation brings the data down to earth, grounding them with more objectivity and context.2 Some of these locations that require transformation may be relative and cultural, such as street addresses that reference mailboxes or houses. Relative locations are best transformed into absolute locations by geocoding them or processing them to link place names and/or street addresses to geographic coordinates. This process can be understood as getting them on the grid. Some location data already contains absolute or geospatial locational data, yet practitioners should continue to link other relative data to them and then layer this in a visual environment. 2.1.4  Visualization

Practitioners should consider how to most efficiently get the data into the preferred visual environment for discovery and further transformation. Because the human eye sits atop the sensory hierarchy, visualizations provide humanity with large amounts of data that can be observed and subsequently transformed into useful information. Two popular visual environments for visual data are Geographic Information Systems (GIS) and Electronic Light Tables (ELT), as seen in Figure 2.2. 2. Georeferencing is the process of relating locations on a digital map or aerial photo to geographic coordinates.

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Figure 2.2  Geospatial software: GIS for mapping and spatial analysis, and ELT for photographs and imagery analysis [1, 2].

The location mindset works because locations are widely available and universally understood, can be highly accurate, and are the initiators of geospatial observations and analysis. Next is an overview of these characteristics that make the location mindset successful. 2.1.5  Locations Are Widely Available

Locations are widely available to laypeople and practitioners alike. Practitioners can collect locations from people who provide them during conversations and debriefings. People can extract locations from sensors that recently collected them during field research. One can collect geospatial locations in tabular datasets that can then be visualized in a GIS. This data can be improved through the application of additional attributes, which are the fields of data attributed to locations that provide context. Practitioners can also download locations



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from open data websites that offer the public municipal, legal, and public safety information. Employees can access locations by querying their organization’s systems for detailed reports of recent incidents. Photographs and videos from security cameras, cell phones, and digital cameras are widely available in various media. Indeed, everyone with computer or smartphone access can view and interact with publicly available mapping and imagery interfaces that allow users to hover over a location and click to access the corresponding geographic coordinates. These geo-enabled interfaces are universally available on the internet and even allow users to conduct rudimentary geospatial analysis. 2.1.6  Geospatial Locations Are Universal

The geographic grid is an internationally recognized scientific system of measurements of the Earth’s surface that is constantly improved by cartographers and mathematicians.3 This grid is a system of horizontal and vertical lines, known as parallels and meridians, respectively, projected onto the sphere of the Earth and then measured. These measurements deliver accurate, universal locations in the form of latitudes and longitudes. Latitudes and longitudes, also known as lat/longs, form the basis of geospatial data and are the measurements that powers geospatial analysis. Figure 2.3 shows the geographic grid, the universally accepted system of location measurements resulting in geographic coordinates. This system provides locations with a common understanding, and practitioners can count on the universality of the geographic grid to measure objective and accurate locations. As dates provide humanity with a bookmark

Figure 2.3  The geographic grid is the universally accepted system of parallels, meridians, and location measurements resulting in geographic coordinates. 3. For a history of the development of coordinate reference systems, see Robert Clark’s Geospatial Intelligence: Origins and Evolution; for a philosophic treatment of the effect of emergent Industrial Age measurement systems, see Benedict Anderson’s Imagined Communities, Chapter 10.

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in time, the geographic grid allows practitioners to bookmark locations on the Earth’s surface. Satellite-based Global Positioning Systems (GPS) also provide universal locations by orbiting the Earth, measuring precise points, and delivering those to customers worldwide. The universality of the geographic grid and GPS allows allied countries, mission partners, and global scientists to collaborate on a variety of joint endeavors, some of which include geospatial analysis. It also enables much broader efforts such as global travel and logistics, which rely on the geographic grid and GPS’s universal and objective measurements of geographic coordinates to guide efforts. This absolute locational data can be measured, recorded, and revisited over time. Figure 2.4 shows GPS and its ability to provide precise locational measurements on the geographic grid. 2.1.7  Locations Can Be Highly Accurate

Practitioners can also benefit from the accuracy of locations measured from GPS. The ability of GPS to measure precise locations on the Earth’s surface and deliver those to practitioners increases demand for geospatial analysis. By 2022, there were four constellations of GPS satellites in orbit that provided coverage for the United States, the European Union, the Russian Federation, and China [3].4 According to a U.S. government website, by 2022, the United States was committed to operating at least 24 GPS satellites 95% of the time, and between 2012 and 2022, it operated 31 GPS satellites. On Earth, a GPS signal receiver uses three or four satellites to compute latitude, longitude, altitude, and time [4]. This allows users to receive two types of radio signals, L1 and L2. Recreational grade receivers are less accurate and use only the L1 signals that offer approximately 50 ft of accuracy. Mapping grade receivers are more accurate: some use only L1 and can achieve 10 ft of accuracy, and some use both L1 and L2 and can achieve 3 ft of accuracy. Survey grade receivers are the most accurate, and use L1 and L2 signals to achieve between 1 and 2 cm in accuracy [5]. Examples of GPS signal receivers include survey equipment, handheld GPS devices, cell phones, vehicles, and even watches. All of these devices are capable of generating geospatial datasets and contributing locations for geospatial analysis.

4. The Indian Space Research Organization (ISRO) developed the Indian Regional Navigation Satellite System (IRNSS) or the Navigation with Indian Constellation (NavIC), which went online in 2018. NavIC differs slightly from GPS because it consists of eight geostationary satellites that provide regional navigation assistance at higher Earth orbit, which provides less accuracy, while GPS provides geosynchronous worldwide coverage with greater accuracy. For further reading, please see: Tech2. “NavIC: How Is India’s Very Own Navigation Service Different from US-Owned GPS?” December 21, 2022, www.firstpost.com/tech/news-analysis/ navic-how-is-indias-very-own-navigation-service-different-from-us-owned-gps-11342771. html.



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Figure 2.4  GPS provide precise locational measurements on the geographic grid.

2.1.8  The Electronic Grid

The location mindset is increasingly effective due to the electronic grid.5 The electronic grid consists of GPS and their receivers on Earth that have created a new domain in which practitioners can derive universal and accurate locations. To exploit this absolute locational data, people invented the technology to use cell phones, vehicles, watches, and other items as receivers of GPS signals and to broadcast their locations on the internet. While the twentieth-century internet consisted mainly of computers connected to each other, the twentyfirst-century internet has added the connection of GPS-enabled devices such as cell phones, vehicles, and watches that also broadcast locations and attribute data to other devices worldwide. This phenomenon is known as the Internet of Things (IoT), and it represents a new frontier in geospatial analysis. The IoT is enabled by GPS locations and devices connected in an electronic grid, as shown in Figure 2.5. As devices are continually upgraded and new devices come online, the IoT will increase in size and scope and provide even more geospatial data for the next generation. A practitioner with a location mindset upgraded for the Information Age knows that this is a new frontier in which to find and 5. We present the term “electronic grid” to describe the worldwide system of precise locations created by GPS satellites and other electronic devices that measure and record accurate points on the geographic grid and make them digitally available to consumers. For a history of the technical aspects of these systems, see Robert Clark’s Geospatial Intelligence: Origins and Evolution.

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Figure 2.5  The IoT is enabled by GPS locations and devices connected in an electronic grid.

understand the locations of customers, people in need of assistance, and even suspects trying to hide from them. Indeed, the electronic grid is the newest location generator for successful geospatial observations and analysis. 2.1.9  Location Initiates Geospatial Observations and Analysis

Locational data enabled the geospatial observations and analysis necessary to communicate an assessment that led to the arrest, charging, and sentencing of the murderer in the Danville homicide case. The practitioner in that case prioritized location and used spatial and geospatial thinking to orient themself to the spaces, times, proxies, and grids needed to move forward with geospatial observations and analysis.6 Finally, the practitioner used the assessments from the geospatial analysis to create compelling communications including text, graphics, and verbal presentations that acted as a stand-in for the absent eyewitness. Locations initiate observation and analysis because they are often an indicator, even a signature that helps to orient practitioners, identify objects, and understand circumstances. Practitioners of geospatial analysis use the term “indicator” to describe observables or signs that help them interpret entities, events, or phenomena, including their identity or function. Practitioners can use location as an indicator of specific gang involvement when mapping certain 6. “Proxies” refer to entities that are associated with people. Examples of proxies are cell phones, vehicles, residences, and other items that aid in the identification of a person.



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crimes in an area controlled by a specific criminal organization. They can also use location as an indicator on imagery to evaluate the operational status and direction of travel of a vehicle. Practitioners further use the term “signature” to describe an observable, or grouping of observables, that are unique to a specific entity, event, or phenomenon. Practitioners can use relative location as a signature when analyzing imagery of specific military and civil equipment; for example, the locations of equipment parts on an entity may reveal a signature for its identity, such as the wings, engine, fuselage, and tail of an airplane. Absolute geospatial locations may reveal a signature for an entity’s identity, such as a hydroelectric power plant’s location alongside a river. Examining the relative locations of parts of an object and its absolute geospatial location on Earth can initiate geospatial observations and lead to more complex discoveries that enhance geospatial analysis. Figure 2.6 shows how location can act as an indicator or a signature of the identity of an entity. Locations further initiate geospatial observations and analysis because animate and inanimate objects share a connection to their locations on Earth. The location mindset requires contemplation of humans and their immediate physical connections to locations on Earth to improve follow-on geospatial analysis. It also requires an understanding of the longer-term cultural connections to locations on Earth manifested in shared languages, belief systems, customs, traditions, and mannerisms. Understanding the importance of locations and their effects on entities in those locations is foundational to the location mindset, and to geospatial analysis.

Figure 2.6  Location can act as an indicator or a signature of the identity of an entity [6, 7].

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2.2  Using Spatial and Geospatial Thinking Finally, the location mindset includes spatial and geospatial ways of thinking that are both innate and learned. Practitioners can capitalize on the innate elements by prioritizing and practicing them. Practitioners can also learn new elements and practice those to further improve research. Whether innate or learned, both spatial and geospatial ways of thinking are foundational to a location mindset and can be used iteratively during geospatial analysis. Practitioners naturally use spatial thinking to orient, navigate, and reason through space in one’s mind. This entails using basic spatial orientation to get one’s bearings, conceptualizing the role that space plays in research, and using more advanced spatial reasoning, which will be examined later in the chapter. The location mindset engages the innateness of spatial thinking and then further requires practitioners to use geospatial thinking to systematically bind space to the Earth’s surface and reference it for entities, events, and phenomena. The following sections will define and examine the origins and purpose of spatial and geospatial thinking in the human brain. They will also provide examples of how practitioners can improve them in order to excel at geospatial analysis. 2.2.1  Spatial Thinking: Definition

Spatial thinking is a term used mostly in the fields of psychology and cognitive sciences that describes the body of knowledge, skills, and habits of mind to use concepts of space, tools of representation, and processes of reasoning to organize and solve problems [8]. A 2005 report from the National Academy of Sciences Multidisciplinary Committee on Spatial Thinking published by the National Research Council also provided examples of concepts of space as measurements of time, distance, and dimension; tools of representation as thoughts, images, maps, and models; and processes of reasoning as navigation and decision-making [8]. While spatial thinking is primarily examined and defined in psychology and cognitive sciences, this book also identifies spatial thinking as foundational ways of thinking needed to form a location mindset and conduct geospatial analysis. 2.2.2  Spatial and Geospatial Thinking in History

Aspects of spatial and geospatial thinking are innate within the human brain and have a rich history within human evolution and development. Spatial thinking was born and evolved in human ancestors and has great adaptive importance that is vital to everyday orientation. Upper paleolithic Homo sapiens’ ability to make and use tools such as harpoons, needles, or spear-throwers represented advances in local applications of spatial thinking. More recent humans’ ability to create maps and sailing ships represented a leap forward in geospatial



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thinking towards a global scale. Modern humans have advanced spatial thinking to never-before-seen heights with the exploration of space (both outer and cyber). As the next generation struggles to adapt to the modern big data deluge, enhanced screen time, and attention competition, the importance of spatial and geospatial thinking will continue to grow as the human brain learns to comprehend and navigate these ever-changing domains.� 2.2.3  Spatial Thinking: Purpose and Practice

The purpose of spatial thinking is to allow for orientation and movement in the world, as defined in Section 2.2.1. In practice, a practitioner can use spatial thinking as part of a location mindset that initiates research by simply conceptualizing and prioritizing space and locations. Practitioners can conceptualize the spaces and the locations in those spaces that will bound and define a research inquiry. To prioritize locations, one should relentlessly apply the mindset that locations will enrich the basis of one’s research. To prioritize space, one should think more broadly about how these locations relate to their surroundings. A practitioner can also use spatial thinking as part of a location mindset during geospatial analysis to interpret and assess objects, entities, events, and phenomena. Elements of spatial thinking that are most relevant to the practices of geospatial analysis are spatial orientation, object and attribute differentiation, object recall, mental rotation, and mental construction. These elements will be examined in detail later in this chapter. 2.2.3.1  Innateness: The Cerebral Grid

Scientific researchers recently discovered the importance of location in the most minute and foundational human thought processes, underscoring the innateness and importance of a location mindset. The 2014 Nobel Prize in Physiology or Medicine was awarded to John O’Keefe, May-Britt Moser, and Edvard I. Moser for their discoveries of those cells that use location to create a positioning system in the brain [9]. The specific neural structures that underpin spatial thinking and anchor location in the human brain are place and grid cells in the hippocampus and adjacent entorhinal cortex. Place cells fire in response to specific locations. Grid cells then relate places to each other through firing across multiple fields, periodically triangulating to orient places to one another [10]. The brain uses this neural code and pattern as a coordinate system for spatial navigation [11]. Figure 2.7 demonstrates how place and grid cells act as a GPS in the brain to map locations. Crucially, place and grid cells use location to anchor mental representations of objects not available for visualization and also to process visual location-based information and create similar internal representations. Further, there is evidence that this capacity is preconfigured within the human mind, that humans are born with the propensity to create spatial

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Figure 2.7  Place cells in the hippocampus map pinpoint locations, and grid cells in the entorhinal cortex map out contextual locations, acting as the brain’s GPS.

representations in the mind through an evolved and inherited neural network [10]. This research suggests the existence of a cerebral grid system that forms the basis of spatial thinking and is a foundation of geospatial analysis. 2.2.4  Geospatial Thinking: Definition

This book defines geospatial thinking as a way of thinking that: (1) connects aspects of spatial thinking to absolute locations on Earth, (2) conceptualizes the Earth as a grid on which to reference and measure locations, (3) prioritizes the use of those locations in research inquiries, and (4) transitions from more subjective mental processes to more objective measurements and visualizations. In this way, geospatial thinking is the part of the location mindset that brings thought processes down to earth. 2.2.5  Geospatial Thinking: Purpose and Practice

The purpose of geospatial thinking is to systematically integrate absolute locational data and information into research inquiries. This includes using geospatial thinking to contemplate the local, regional, national, and international scale and implications of one’s research scope. From this purpose, a variety of Earthreferencing analytic practices follow, including geolocation, measurement, and analysis of object-based mediums such as images and maps, all of which are discussed in more detail in Chapter 7. Geolocation refers to linking absolute locational data (i.e., latitude and longitude) to relative locational data to improve its accuracy. This, in turn, enables measuring locations on the geographic grid in terms of points, lines, and areas. Building more accurate locational data and documenting Earthreferenced measurements transitions spatial thinking from subjective mental constructs towards object-based mediums such as images and maps. Images and maps present and represent locations and entities for visualization and analysis.



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In this way, systematic application of geographic coordinates creates more objective building blocks for research that can be shared and understood worldwide. Further, geospatial thinking initializes the mental processes required to eventually visualize the geo-enabled imagery and geo-enriched maps that will guide and answer the research inquiry.7 2.2.6  Improving Spatial and Geospatial Thinking Through Reasoning

Practitioners can improve spatial and geospatial thinking by practicing elements that include reasoning. Reasoning is the act of thinking about something in a logical, sensible way. With respect to most scientific and analytic endeavors, reasoning is the generation or evaluation of claims in relation to their supporting arguments and evidence. Processes of reasoning for spatial and geospatial thinking include methods for differentiating things, recalling things that one has seen before, mentally manipulating spatial representations to develop new insights, and mentally constructing that which cannot be seen from that which can [12].8 Examples of spatial and geospatial thinking that incorporate reasoning and are greatly beneficial to the practices of geospatial analysis are object and attribute differentiation, object recall, mental rotation, and mental construction. 2.2.6.1  Object and Attribute Differentiation

Object and attribute differentiations are the mechanisms by which humans understand that objects are both distinct from and related to one another [13]. Object differentiation entails locating an object’s shape or boundaries in relation to other objects. An example of object differentiation is identifying two similar coins located adjacent to one another as distinct objects, as seen in Figure 2.8. Attribute differentiation entails recognizing separate, detailed features of an object in order to identify it or to further differentiate it from other objects.9 An example of attribute differentiation is demonstrated in Figure 2.8 by the two coins marked with different dates. To put this into practice using geospatial analysis, a practitioner who received a report of a silver vehicle belonging to a crime suspect with a sunroof 7. The term “geo-enabled” refers to a system or database capability to handle geospatial data, and means “sufficiently capable of handling geospatial data so practitioners can use it to conduct follow-on geospatial analysis.” The term “geo-enriched” refers to the extent to which geospatial data is made available in that environment (map or imagery), and means “populated with enough geospatial data so as to be able to optimize geospatial analysis.” 8. This relates to the Penn State Learner’s Guide to Spatial Analysis, which states that “an expert spatial thinker visualizes relations, imagines transformations from one scale to another, mentally rotates an object to look at its other sides, creates a new viewing angle or perspective, and remembers images in places and spaces.” 9. An attribute is a quality or feature regarded as a characteristic or inherent part of someone or something.

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Figure 2.8  Object and attribute differentiation demonstrated by two pennies.

could use object differentiation to identify two vehicles adjacent to one another at specific geographic coordinates on imagery. Then the practitioner could practice attribute differentiation by identifying distinct parts of each vehicle to attempt to match one to a description of a suspect’s vehicle. Figure 2.9 presents an example of object and attribute differentiation. Object and attribute differentiation further entails identifying objects in classification systems (i.e., relationships) and recognizing them in different contexts, explored in more depth in subsequent chapters [14]. Practitioners of geospatial analysis rely on object and attribute differentiation to identify, relate, and even contextualize entities in locations on Earth. The more one practices the subtleties of this skill, the eas-

Figure 2.9  Geospatial example of object and attribute differentiation demonstrated by vehicles on imagery [6].



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ier it is for an imagery analyst to determine minute differences between entities such as vehicles, equipment, and buildings and change over time. 2.2.6.2  Object Recall

Object recall is the ability to recall entities in one’s mind that one has previously seen. It is synonymous with having a photographic memory. It is a partially innate skill, but one that can also be practiced to improve. Great geospatial analysts excel at this skill, as it is one of the keys to quickly identifying entities on Earth during imagery analysis and requires constant interpretation of many subtle differences in colors, shapes, and sizes of entities. An example of object recall is a practitioner observing a vehicle in a driveway on imagery and remembering that same vehicle being parked there on past images. To improve object recall, one can spend more time focusing and slowly absorbing the details of an object. This should help increase the library of recallable entities in the practitioner’s brain, which greatly benefits geospatial analysis and overall wisdom over time. 2.2.6.3  Mental Rotation

Mental rotation is a process of imagining an entity in different positions (i.e., locations). It requires some level of object recall. Mental rotation is a great skill for practitioners of geospatial analysis because images and maps can often present themselves in ways that lack proper orientation, requiring the viewer to rotate such visualizations in their mind. Mental rotation further includes rotating two-dimensional (2-D) figures in three-dimensional (3-D) space. It is a partially innate element of spatial thinking, but can be improved with practice. Many exercises, such as those frequently found in IQ tests and in Figure 2.10, provide practitioners with an opportunity to sharpen their spatial thinking and mental rotation skills. These skills will become important during geospatial analysis when a practitioner must match similar shapes seen on a map and an image to identify a location and entity, as seen in Figure 2.11. In this example, an analyst used mental rotation to correlate an image on a wall map in a worker’s office to a port location on satellite imagery. 2.2.6.4  Mental Construction

Mental construction is an element of spatial and geospatial thinking that involves building an image of something in one’s mind that cannot be seen otherwise. For example, when practitioners can see one part of an entity or process, they may construct the remainder in their minds. Mental construction can also be a step-by-step reorganization of individually imagined, spatially located features such as a puzzle (usually connecting individual objects together). For example, imagine an assembly line at a motor vehicle plant. The frame moves down the line as it accumulates doors from one location, then a hood and trunk

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Figure 2.10  This exercise tests practitioners in their mental rotation ability [15].

Figure 2.11  A practitioner must match a shape on a map on a wall from an open source report, mentally rotate the shape of a port, and then match it to imagery of a location [16]. (Inset: After: [17].)

from another location, then wheels from another location, and then an engine. One can imagine all of those parts in separate locations and then mentally construct them into a fully assembled car in a cognitive, location-based experience without any visual input. Mental construction is also partially innate and partially learned and can be improved with practice. Mental construction has great applicability in geospatial analysis, as many objects on imagery and videos, and even in nature, are partially obscured or cloud-covered. It can also be conducted with datasets on maps and graphics that have missing or cutoff data.



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Practitioners should practice mental construction to improve their skills by comparing clear and partially cloud-covered or cutoff images and imagining the missing components of entities that are partially obscured. In Figure 2.12, a practitioner can use mental construction to infer the presence of a vessel in a port on satellite imagery [18]. Despite the cloud cover that obstructs observation of much of the image, one can see certain shapes and tones adjacent to the dock that indicate the presence of the vessel. The analyst could use object recall to remember previous unobscured images of that location or compare it to a nearby similar vessel to mentally construct the obscured portion. With practice and time on target, one can improve these mental construction skills in order to see in the mind’s eye what the untrained eye cannot. For the final example, imagine a puzzle of the United States with 50 pieces mixed up and disoriented, as seen in Figure 2.13. Then a practitioner must use object recall to first remember their proper orientation and then mental rotation to properly orient the pieces. An outline is formed as the practitioner mentally migrates the pieces to their correct positions. Using mental construction, the practitioner fills in the remaining pieces to complete the puzzle from disparate and disoriented pieces to a single, cohesive, completed story. Solving this simple puzzle in one’s mind is a building block towards solving the larger and more complex puzzles that geospatial data deliver to practitioners each day. These puzzles require geospatial analysis to solve and often deliver the visually compelling assessments that modern audiences require for decision-making.

Figure 2.12  A practitioner can use mental construction to infer the presence of a vessel in a port on satellite imagery [18].

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Figure 2.13  Mental construction is a key element of spatial thinking.

2.3  Conclusion The Danville murder case example presented in Chapter 1 demonstrated how practitioners can place the location mindset into action. At the outset, the practitioner used the geospatial principle “everything that happens or exists on Earth does so in a location” as a starting point. Prioritizing location, the practitioner collected vehicle, cell phone, residential, and commercial locations of the suspect and transformed the spatial and imagery data for visualization on a GIS and ELT. The practitioner used geospatial thinking to envision the regional extent of the geography that would be involved and collect the point data that would dominate the evidence. The practitioner then used spatial thinking and reasoning to differentiate the suspect’s cell phone from others, construct the suspect’s travel route with specific cell tower locations and buildings, and recall the travel route of the vehicle and compare it to the suspect’s testimony. With location as a foundation, a practitioner can conduct a locational data-toinformation refinement process consisting of sensing functions such as observations, cognitive functions such as analysis, and delivery mechanisms such as communications. The location mindset also acts as a great unifier for fields of study and their domains of research. It unites portions of cognitive sciences, psychology, and geography with the shared priority of examining location in order to improve orientation and understanding of the world. A practitioner of geospatial analysis, as demonstrated in the Danville murder case, can further unite the cerebral, geographic, and electronic grids to execute more effective research. As the Information Age progresses, one can expect technology to increase the accuracy, frequency, and amount of the locations that present themselves for geospatial analysis. Although this increase in technology will demand a loca-



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tion mindset to frame research inquiries, it will also require a toolset worthy of progressing through iterations of geospatial analysis.

References [1] ESRI. ArcGIS Software Streets (Night) basemap, https://pro.arcgis.com/en/pro-app/latest/help/mapping/map-authoring/author-a-basemap.htm. [2] Planet Explorer, Online imagery streaming platform, https://account.planet.com/. [3] United States Department of Transportation, Federal Aviation Administration, “Satellite Navigation - GPS - How It Works,” www.faa.gov/about/office_org/headquarters_offices/ato/service_units/techops/navservices/gnss/gps/howitworks#:~:text=To%20accomplish%20this%2C%20each%20of,that%20provide%20extremely%20accurate%20time. Accessed December 11, 2022. [4] GPS.GOV. “Space Segment,” June 28, 2022, www.gps.gov/systems/gps/space. Accessed December 22, 2022. [5] Asmus, R., “The Difference Between Handheld GPS Receivers & Surveying Grade GPS Receivers,” ItStillWorks, https://itstillworks.com/difference-between-handheld-gps-receivers-surveying-grade-gps-receivers-17869.html. Accessed December 11, 2022. [6] ESRI, ArcGIS Software with Imagery basemap, https://pro.arcgis.com/en/pro-app/latest/ help/mapping/map-authoring/author-a-basemap.htm. [7] Balkan Green Energy News, “45 MW Brežice Hydropower Plant on River Sava Inaugurated,” Balkangreenenergynews.com, October 2, 2017, https://balkangreen energynews.com/45-mw-brezice-hydropower-plant-on-river-sava-inaugurated. Accessed December 13, 2022. [8] National Research Council, Learning to Think Spatially, Washington, D.C.: National Academies Press, 2006, https://nap.nationalacademies.org/read/11019/chapter/1. Accessed December 11, 2022. [9] The Nobel Prize, “Press Release for the Nobel Prize in Physiology or Medicine in 2014,” October 6, 2014, www.nobelprize.org/prizes/medicine/2014/press-release/. Accessed December 11, 2022. [10] Moser, E., E. Kropff, and M. -B. Moser, “Place Cells, Grid Cells, and the Brain’s Spatial Representation System,” Annual Review of Neuroscience, Vol. 31, 2008, pp. 69–89. [11] Abbott, A., and E. Callaway, “Nobel Prize for Decoding Brain’s Sense of Place,” Nature Vol. 514, No. 153, 2014. [12] Bacastow, T., et al., “The Learner’s Guide to Geospatial Analysis (V1.1),” Penn State University Department of Geography, 2010, www.e-education.psu.edu/sgam/node/25. [13] Hawkins, J., et al., “A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex,” Frontiers in Neural Circuits, Vol. 11, January 2019, pp. 3–4, www. frontiersin.org/articles/10.3389/fncir.2018.00121/full. [14] Tversky, B., Mind in Motion: How Actions Shape Thought, New York: Basic Books, 2019, pp. 68–69.

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[15] Shepard, R. N., and J. Metzler, “Mental Rotation of Three-Dimensional Objects,” Science, Vol. 171, No. 3972, February 19, 1971, pp. 701–703. [16] Planet SkySat, Satellite image from October 6, 2017, Scene ID: 20171006_020444_ssc3_ u0001. [17] Berger, S., “Corée du Nord: le charbon est en rade mais les affaires russes sont florissantes,” Le Point International, April 12, 2017, https://www.lepoint.fr/monde/coree-du-nord-lecharbon-est-en-rade-mais-les-affaires-russes-sont-florissantes-04-12-2017-2177043_24. php. [18] Maxar, Satellite image from May 3, 2020, Catalog ID: 1020010091DBE100.

3 The Geospatial Toolset 3.1  Introduction to the Geospatial Toolset The geospatial toolset is at the center of the geospatial mindset, toolset, and skill set and consists of the data, sensors, systems, hardware, software, and people who support and conduct geospatial analysis. These elements are integrated in different ways throughout geospatial analysis. Sensors collect geospatial data of varying types. Systems ingest, structure, and store geospatial data in a particular architecture. Computer hardware and software are the engine that drives processing, visualization, and the eventual transformation of geospatial data into information and assessments. People bring it all together and act as the most valuable tool in the set; they are the practitioners that are necessary for input, innovation, and production. They also create objectivity, conduct peer review, and provide training and mentorship for each other. This chapter provides an overview of the geospatial toolset from data to the people who drive geospatial analysis.

3.2  Geospatial Data Geospatial data are facts, figures, files, pixels, and other resources that contain locational data referenced to the Earth via geographic coordinates. They are captured and collected by sensors and people, stored in systems, and exposed in hardware and software in which practitioners can conduct geospatial analysis. Geospatial data contains latitude and longitude coordinates for locations, hereafter referred to as “geocoordinates.” Once a research topic is established, 31

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the practitioner prioritizes, collects, transforms, and then visualizes the geospatial data associated with the research topic. This geospatial data becomes part of the research inquiry, and is eventually transformed through the data-toinformation refinement process into useful information for an audience as seen in Figure 3.1.1 3.2.1  Geospatial Data Background

For most of human history, collecting Earth-referenced data involved using the human eye and hand to visualize entities such as water, flora, fauna, Earth formations, and celestial bodies with respect to relative locations. Representations of locations on the Earth’s surface evolved from artistic renderings and terrain models to complex maps as technology improved. By the seventeenth century, remote sensors such as the telescope were invented, and the word data was first used in England to refer to parts of the resulting flow of new information [1, 2]. Photographic cameras, invented in the nineteenth century, brought to the world a new form of data and recordation [3]. During this period, advances in human observation of the stars, the Earth’s surface, mathematics, and communications allowed humans to refine a geographic grid of absolute locations to enhance geospatial orientation. The twentieth century saw massive technological leaps forward that included cameras, computing, and communications. Camera technology evolved and became much more widely used and refined. People innovated and mounted cameras on satellites, aircraft, and street corners, revolutionizing how humanity could stop time and motion to review data from the past. Motion pictures were invented; this recorded movement, allowing for deeper examination of activity patterns. These inventions would affect humanity’s ability to record

Figure 3.1  The locational data-to-information refinement process commences with locational data as the input, moves through observations, analysis, and communications, and terminates with the creation of geospatial information. 1. The term spatial data is commonly referred to as data that can be mapped and is often used interchangeably with geospatial data when referring to the portion of geospatial data that can be mapped. However, the overall category of geospatial data includes both spatial data that can be mapped and imagery data (georeferenced literal photographs of the earth and its features), which is not generally referred to as spatial data.



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the visualization of the Earth and objects, and lead to the ability to further scrutinize objects, entities, and phenomena. Computer hardware further revolutionized humanity’s collection and analysis of data. In the 1940s, the term data was adopted to describe transferable and storable computer information [2]. Computers quickly became a prized tool for business, science, and technology as its data management function was vital for a variety of research endeavors. Later in the twentieth century, the term big data emerged in government and business to describe the massive speed and volume with which data was accumulating as a result of these technical advances in remote sensing and data storage. Because of its volume, variety, and velocity, big data required practitioners to seek new software tools to manage data and conduct analysis, which led to the development of GIS for managing geospatial data, ELT for imagery, and other technologies. By the twenty-first century, the explosion of cell phones brought geospatial data collection and analysis into the hands and pockets of users worldwide. Cell phone-based consumption of geospatial data and information, including images and maps, is now ubiquitous via applications used by millions each day. The resulting explosion of geospatial data has been both a boon to geospatial analysis and a stumbling block for governments, private companies, and citizens grappling with the big data deluge. Private companies have begun hiring specialized practitioners to conduct geospatial analysis on data in order to transform it into useful information for their leadership. Governments and global organizations have expanded geospatial efforts by collecting more geospatial data, prioritizing more geospatial analysis, and even publishing papers and passing laws on the publication, employment, and sharing of geospatial data and information. 3.2.2  Global Emphasis on Geospatial Data

The United Nations, the United States, and allied partners have emphasized production and analysis of geospatial data and information as a priority. In 2011, the United Nations established the Committee of Experts on Global Geospatial Information Management (UN-GGIM) as the lead intergovernmental mechanism for making joint decisions and setting directions on the production, availability, and application of geospatial information within national, regional, and global policy frameworks. By 2020, the UN-GGIM had produced numerous documents including “BLUEPRINT Geospatial for a Better World: Transforming the Lives of People, Places and Planet,” which underscored the importance of geospatial data and information for helping the United Nations achieve its mandates and missions [4]. In 2018, the U.S. government passed the Geospatial Data Act (GDA) [5]. In it, the U.S. Congress underscored the importance of geospatial data and information and enshrined in law the need

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to foster better collection, processing, sharing, and collaboration. The GDA established a robust apparatus, the need for data standards, and a website to house and share geospatial data and information. This website, www.geoplatform.gov, allows public and private organizations to have access to the vital geospatial data and information needed to understand the Earth, conduct research, build models, and engage in scientific endeavors. Because of humanity’s increasing dependence on all types of data, many have argued for more prominent principles to govern data management and sharing, such as FAIR [6]. The four foundational principles of FAIR are findability, accessibility, interoperability, and reusability, and this is intended to guide data producers and maximize the added value of publishing and sharing [6]. Although these principles apply to all data, they are especially apt for geospatial data due to its inherent ability to deliver accurate, visualizable location information to customers, especially in tabular, raster, and vector formats. 3.2.3  Geospatial Data Categories

Geospatial data comprises three main data categories: tabular, raster, and vector. Many types of specialized datasets exist within these categories, such as sonar, radar, lidar, terrain, and voxels. However, these categories capture most geospatial data types as outlined next. 3.2.3.1  Tabular Data

Tabular data is data in a table, usually a system or a spreadsheet made up of columns and rows, similar to the parallels and meridians of the geographic grid. Some tabular datasets contain relative locations such as street addresses presented along with additional attribute data, as seen in Figure 3.2. In a tabular dataset, the horizontal data are referred to as records or rows and vertical data are referred to as fields or columns. To become a geospatial dataset, all relative locations must be linked to geocoordinates, as seen in Figure 3.3. Geospatial datasets may also contain additional attribute data that provides context. The more attribute data the practitioner can connect to specific locations, the more relations and context will be revealed during geospatial analysis. Locational data are the key fields that allow the practitioner to solve for where, while additional attribute fields provide context that may help solve for who, what, why, when, and how. Tabular data can also be extracted from a larger source system that houses many tables, and then separately joined to create a customized, coherent dataset. These datasets take a larger level of effort to assemble and then either connect to or extract. However, this tabular data, especially when created into an automated map service, can be especially valuable for organizations that need



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Figure 3.2  Tabular dataset with relative locations and attribute data.

Figure 3.3  Geospatial dataset with locations and attributes.

to visualize and analyze large volumes of their own data, partner data, and other open data on a recurring basis. 3.2.3.2  Raster Data

Raster data consists of a matrix of cells (or pixels) organized into rows and columns (or a grid), where each cell contains a value representing data such as temperature or tone [7]. For example, raster data is represented by a grid of pixels of varying tone in Figure 3.4. Geospatial raster data origins include satellites, airplanes, drones, sensors, and people with any form of digital cameras. Examples

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Figure 3.4  Raster data contains pixels with differing cell values and colors.

of raster data are digital photographs and images, videos, and heat maps. Figure 3.5 shows a grid of pixels on the left and an aerial image zoomed in revealing the corresponding pixels. Figure 3.6 shows the same image zoomed out at two distances revealing entities, geographic coordinates, and contextual elements. Raster data can be exploited on a GIS or ELT. For example, a sensor can collect raster data and send it for processing to a computer that will reorganize it into a matrix of cells that contain various tonal differences, elevations, and locations. The resulting digital image can be analyzed by a practitioner on an ELT. In a second example, a practitioner can create raster data on a GIS by converting a vector dataset of points into a heat map or hot spot analysis showing

Figure 3.5  Raster data contains pixels with differing cell values and colors [8].



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Figure 3.6  Georeferenced imagery zoomed to two distances revealing entities, geographic coordinates, and contextual elements [8].

the density of those vector points in a raster format, with varying colored and valued pixels. Figure 3.7 shows a raster base map with raster data (heat map) overlaid. 3.2.3.3  Vector Data

Vector data is a coordinate-based data model representing geographic features as points, lines, and polygons (also called areas) [9]. Points are locations on Earth represented by a single geographic coordinate, such as a latitude/longitude. Lines are representations of the distance between points that connect them to each other. Polygons are enclosed lines that form a contiguous boundary or the outline of an area around points. Figure 3.8 shows a diagram of points, lines, and polygons. Vector data are most commonly seen on maps, although vector files can be created on imagery and shared between imagery and

Figure 3.7  Raster base map with raster heat map overlayed [8].

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Figure 3.8  Vector data contains points, lines, and polygons (or areas).

GIS environments. Figure 3.9 presents a vector base map depicting nonliteral representations of Earth, roads, and features, overlaid with a vector point layer of features depicting entities in specific locations. A common example of a vector dataset, often referred to as spatial data, is one made up of features, which are single entities on a GIS that contain geometry and attributes. Geometry refers to only the spatial aspects of the feature such as the measurement of the points, lines, and polygons in spatial data.2 Attributes are the descriptive data and information that is linked to the spatial data, such as count of events, identification of an entity, data source, or date and time. The attribute data, as seen in Figure 3.10, provides the observer with more detailed information and context. 3.2.4  Geospatial Data: Embedded in Our Everyday

The exposure and accessibility of geospatial data from individual users are some of the greatest transformations of the human experience in the twenty-first century. For example, smartphones and some vehicles automatically record the geospatial data tracking the location of the device, which often reveals the device user’s location as well. This data has emerged as a primary source for tracking users on the electronic grid, as evidenced in the Danville murder case in Chapter 1. In these cases, geospatial data is the input that allows practitioners to conduct geospatial analysis that reveals the patterns in which humans carry out

2. A data table may not yet contain geospatial data that is ready to view in a GIS if the data has not been processed to assign the necessary geographic coordinates, projection, or geometry. Once the table is uploaded into the GIS and the necessary geoprocessing tools are applied, the practitioner should then be able to view the data in the map viewer, and access the table or database to further examine the data.



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Figure 3.9  Vector base map with a vector layer of points overlaid [10].

Figure 3.10  Attribute data provides the observer with more detailed information and context [11].

their lives, known in the field as pattern of life.3 Such mobile data is available to private companies and government entities to collect and process. An associated development has been the emergence of the IoT, introduced in Chapter 2. It consists of devices connected to each other and a user, many of which record geospatial data enabled by GPS. Examples of such IoT devices are cell phones, smart watches, vehicles, televisions, and even home electric and alarm systems. These connected devices with enabled locations form an electronic grid, a concept introduced in Chapter 2, and represent massive platforms for producing geospatial data. Collecting and conducting geospatial analysis on cell phones, vehicles, and other IoT data for census, health, marketing, law enforcement, and national security purposes presents a massive opportunity for practitioners. The phenomenon of the IoT also presents challenges to consumers who 3. Pattern of life refers to patterns of regular human activity over a given area, usually interpreted through proxy data from mobile phones, satellite imagery analysis of movable entities such as vehicles, and video.

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are concerned with safeguarding their personally identifiable information and presents challenges to private companies and governments that must develop methods for transforming all of this geospatial data into useful information for their employees. In the modern era, as the convenience and excitement of internet browsing has drawn people in, it also reveals their location and other attributes to the world. As people browse online, their devices emit locational information on the electronic grid, which gives private companies, governments, and adversaries great potential for human surveillance. Now it is easy to locate people on the electronic grid as they use cell phones and the internet, leaving locational data trailing behind. Geospatial practitioners can transform this data into information and report on the pattern of life of human behavior. Only getting off the grid (turning off electronics) provides the metaphorical cleansing and reprieve from the electronic world of data by which humanity is now regularly consumed. Gateways into the electronic grid that can potentially use geospatial data are found in smartphones and demonstrated in Figure 3.11. The first is a permanent setting on a cell phone that allows the user to select to what extent their phone collects and stores locational data. By turning off location services, one opts mostly out of the electronic grid and can no longer use the orienting features that the device has to offer (although the phone may still record its location in certain applications). Users should think slowly and clearly about the calculated risks and rewards of having location services turned on. The second

Figure 3.11  The smartphone contains gateways in the electronic grid: location services and pop-up dialogue boxes.



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offers case-by-case location services within the electronic grid, including certain mapping services. These simple dialogue boxes are emblematic of the precision, importance, and universality of location in the modern era. When the user selects “allow,” it connects the user’s location to a wealth of information yielded by a location-based, electronically interconnected world. From hailing a rideshare to mapping a travel route, everyday usage of geospatial data and information has become ubiquitous. 3.2.5  The Geospatial Data Setup

The geospatial data setup is the first tangible step in the locational data-toinformation refinement process and involves elements of transformation and visualization. The first element of the setup involves using the geospatial toolset (sensors, systems, and software) to collect locational data of all types and transform them into geospatial data. The second element involves setting up the data in a visual environment that promotes geospatial observations and follow-on analysis. The similar terms “set up” and “setup” will be used here to describe the steps that the practitioner uses to prepare data and initiate sequences. First, the practitioner must transform relative locational data into geospatial data by linking geocoordinates to it. To create geospatial data from imagery, one must acquire visual data and join it with geocoordinates (called georeferencing). Geocoordinates may also separately be joined to locations with additional attribute data in a table. If it consists of street addresses or other relative locations, it must include geocoordinates to be considered geospatial data. If such data already consists of geocoordinates, then it is already geospatial data. Practitioners may further collect existing geospatial datasets via open data websites, private sector websites, or public sector storage systems that specialize in satellite imagery.4 Next, the practitioner must set up the data in a visual environment for follow-on geospatial observations and analysis. The ELT or GIS presents the perfect environment for visualizations in which practitioners will determine something as significant and continue the refinement process by applying geospatial analysis. The setup for imagery requires transforming the data from the sensor into a visual image, processing the image in a way that geo-enables it, and sending it to a storage system that can house it until a practitioner calls it up on a website or ELT for geospatial observations and analysis. For example, collection experts and geospatial analysts may work as a team to collect the required image, process it by georeferencing it, and then conduct geospatial analysis on 4. An example of an open data website that contains geospatial data is “Open Baltimore,” which archives many municipal datasets for the city of Baltimore (https://data.baltimorecity.gov/). Private sector examples offering access to satellite imagery are Maxar’s SecureWatch and Planet’s Explorer websites.

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it. The practitioner at the end of the chain accesses the ELT, downloads every relevant image available from an image library, and loads them in chronological order to prepare for exploitation. This imagery setup empowers the analyst with a thorough, all-inclusive visualization of the data and prevents them from missing certain dates, look angles, or perspectives. The setup for spatial analysis may include collecting data from a sensor, survey, or source system, cleaning the data for improved accuracy and ease of upload, and uploading it into a GIS to geocode, geolocate, and/or visualize the data as a layer. This setup prepares the data for geospatial observation and analysis. Some geospatial data, especially geospatial information, is already set up for the consumer. It is supplemented with attribute data and transformed into information when it presents itself for observation. Its delivery mechanism may be the internet, print media, or television, and the media may include images, videos, graphics, or maps ready for visualization and consumption. However, easily accessible information that does not show the underlying locational and attribute data should remind us of a magician’s trick: If you did not do the setup, someone else did. So, who is setting you up? A pause for critical thinking and information sourcing suggests the following questions: • What is the original source of the data? • Who or what did the background work to collect and process the data and transform it into information? • How did they choose to present it, and why? • Who was their intended target? • What advantages or disadvantages are afforded to the entity that wins the race of data transformation into information? • Was the result of one’s encounter with this information the result of free exploration and discovery, or were you directed to it? To fully examine the geospatial data setup, one must additionally understand the sensors, systems, hardware, software, and people who make up the geospatial toolset.

3.3  Geospatial Sensors Geospatial sensors are tools that collect geospatial data.5 Such collection brings in new data that makes up the basis of one’s research and geospatial analysis. 5. Using the word “sensor” to describe a machine relates to the five human senses: seeing, hearing, smelling, touching, and tasting. The human eye sits atop the human sensory hierarchy



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Geospatial sensors can be remote or direct, which describes their distance from the intended collection target and the extent to which they are controlled by humans. Humans are also flexible collectors of locational data that may be further transformed into geospatial data. Figure 3.12 shows remote, direct, and human collectors of geospatial and other locational data. Geospatial sensors create objective data for humanity to examine outside of the human subjective mind. This allows the data to be analyzed by humans over the world and to reach different conclusions generated from the same data basis. While machines play an important role in sensing geospatial data and sending that data to the systems that will further process it, humans play an equally vital role in collecting and processing this data. However, machines such as remote sensors have a distinct advantage in that they can go places where humans cannot. 3.3.1  Machines: Remote Sensors

Remote sensors are tools that collect at some distance from direct human control. The distance of remote sensors from their human operators provides distinct advantages and disadvantages. An advantage is that remote sensors can extend the reach of humanity and sense well beyond human capability. A disadvantage is that the further the distance of the remote sensor from the human, the less control the human has to operate and maintain it successfully. One of the primary geospatial remote sensors is a camera that provides practitioners

Figure 3.12  Remote, direct, and human collectors of locational data. and collects more data than any of the other senses. The visual realm allows for superior orientation, understanding, and decision-making, so scientists and inventors took up the quest to mimic this capability with machines.

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with visualizations of the Earth’s surface and the entities therein. The data generated from the remote sensors includes single-frame pictures (images), motion pictures (full-motion video), and a host of other, more technical capabilities such as radar and infrared images. Remote sensor cameras can be mounted on positions on Earth or on aerial platforms such as drones, planes, and satellites. Aerial platform remote sensors act as an “eye in the sky” for practitioners on Earth tasked with geospatial analysis of their collected geospatial data. Advances in the field of remote sensing have introduced exciting new capabilities in space and Earth-based sensors. Previously, only governments could afford to launch and maintain space-based remote sensors. Now private companies are offering satellite imagery, including high-resolution optical, radar, and hyperspectral, to address a range of issues from warfare to climate change.6 Earth-based sensors such as home security cameras and law enforcement pole cameras are also remote sensors that take in images and videos of the events that take place on Earth and the entities involved. Because they are at a distance from their human counterparts, they must either store or download the collected geospatial data to a system that can make it available to their handlers. Police departments are now subsidizing homeowners to purchase security cameras that are creating an interlocking web of persistent surveillance in neighborhoods [12]. These app-based cameras allow homeowners to become geospatial analysts as they receive alerts when their motion-sensor cameras are activated and tip them off. 3.3.2  Machines: Direct Sensors

Direct sensors are the opposite of remote sensors: they can be held, worn, or more closely connected to their human operators. These sensors often act as proxies for humans and can collect geospatial data on both external entities and their human hosts. Examples of this include handheld and mounted cameras and cell phones. Cell phone and handheld cameras are ubiquitous in the Information Age and act as the most popular geospatial data collector on Earth. The operator directly controls the device, which can collect and record locationbased visual events all over the Earth’s surface. In many cases, this data is then federated to the general public for geospatial analysis on websites and applications. In other cases, the data is uploaded into a system that stores and processes it for further geospatial analysis from within the organization. Mounted cameras such as dash-mounted and body-worn cameras are also in direct control of their human operators. These devices are responsible for a wealth of geospatial data from travel and transportation to law enforcement and national security. 6. Planet Labs PBC offers hyperspectral imagery at this website: www.planet.com/products/ hyperspectral. Maxar offers high-resolution panchromatic and radar imagery at this website: www.maxar.com/products/radar-imagery.



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For example, police departments around the world are investing large amounts of money on body-worn cameras. The geospatial data, images, and videos from these sensors will also require a large investment in trained practitioners who can conduct geospatial analysis on the data and transform it into useful information for attorneys, juries, and the public. Direct sensors have distinct advantages and disadvantages. Advantages include more human control over recordation and maintenance. Disadvantages include the requirement for human presence and the difficulties and dangers that presents. Another distinct and pernicious disadvantage is that these devices often also collect geospatial data on the user’s location and usage details. This data, as evidenced in the Danville murder case, can be sold to private companies for marketing purposes, shared with law enforcement for investigation, or even used by a foreign adversary for more nefarious purposes. Cell phones, vehicles, watches, bicycles, scooters, and other mobile devices record their user’s movements and make them available for geospatial analysis. The recordation of human behavior through their devices has become one of the most debated aspects of the Information Age. It represents a deep and durable ethical challenge for humans and their relationships with their devices. 3.3.3  Human Collection of Location Data

Humans collect locational data that can be transformed into geospatial data. Humans can flexibly use combinations of senses to tip and cue them to visual activity and important locations. They can at times see more clearly and in more directions than sensors and can use tools such as magnifiers (telescopes, binoculars, magnifying glasses, microscopes) as aids to further assist in the collection of visual data. Humans can either store sensory data in their brain to ready it for recall or dictate the data into documents and databases for subsequent analysis. Examples of human data collectors include scientists collecting visual field data, public safety officers recalling visual elements and locations of an investigation, and survey respondents providing their locations and attribute data. All of those details can be entered into a tabular dataset and then uploaded into a GIS for geocoding and geospatial analysis. Humans can also collect locational data by word of mouth. From giving directions to a stranger to debriefing a witness to a crime, locations are passed by word of mouth each day over the world. In a reflection of the importance of this type of data collection, the field of human intelligence (HUMINT) centers around conversations between people as a source of data and information. An example of a conversation that can lead to the development of geospatial data is a law enforcement official meeting with a confidential informant to collect locational information related to a crime, or a military HUMINT practitioner meeting with a source to collect locational information about a planned attack

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from an adversary. Conversations that generate locations can be collected and recorded and entered into the systems that store the data for further use by practitioners.

3.4  Geospatial Systems The term “system” usually refers to a grouping of computers, often servers and clients, that work together to accomplish a common task. A geospatial system is any computer system that is geo-enabled, which means configured to optimize the ingestion, processing, and storage of geospatial data. For example, a case management system ingests data from field collectors and then stores it in tables that can be accessed by analysts for geospatial analysis. � 3.4.1  Geospatial Systems: A Recipe for Success

In order to keep pace with the Information Age, organizations should use best practices that incorporate the location mindset and toolset. This includes moving the transformation of relative locations into absolute locations as close to the user conducting the data entry as possible. The customer, employee, or mission partner entering data into a form or system must share location validation responsibilities with a system. For example, as they type in addresses, a “suggest function” should give them options to select validated addresses. Once the address is verified by the user and the system, the system can geocode the address to transform it into an absolute location. If the location is not a validated address, but is another relative location such as a park or a field, they should have the ability to place a point on a map, transforming it into an absolute location. Either way, the validated, absolute location is then sent to the database for storage and eventual retrieval for geospatial analysis. The following steps provide a recipe for success for organizations seeking to geo-enable their systems: 1. Make location a mandatory field in interfaces that collect location data. 2. If addresses are collected, perform address validation on entry to ensure accuracy. 3. Geocode the addresses on entry to attach geocoordinates to them. 4. If no address exists, allow users to enter a point location on a map that yields geocoordinates. 5. Store those geocoordinates in the source system and make them available for geospatial analysis.



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Once the source system is geo-enabled and can store geocoordinates, the final step in optimizing the systems is to ensure that the analytic software can connect to the geospatial data and other attribute data. This puts geospatial data in the hands of the analysts and allows it to begin its journey from data into information through geospatial analysis.

3.5  Geospatial Hardware Practitioners rely on hardware such as desktop and laptop computers to connect to the systems that provide the geospatial data and host the software for conducting geospatial analysis. These computing tools are standard for Information Age practitioners and contain the processing, storage, and screen resolution necessary to transform geospatial data into information. Desktop computers are often the clients connected to servers that provide them with the geospatial data needed for analysis. They also provide the user with the software that allows them to conduct geospatial analysis. Desktop computers generally have the largest speed and storage capabilities to handle large imagery and spatial analysis software requirements. Laptops have progressed to handle many of the larger requirements, and their mobile capability makes them handy for a variety of use cases. By the 2020s, a sample GIS software manufacturer’s system requirements recommended a laptop computer with at least 32 GB of free-space storage, 32 GB of memory/random access memory, 4 GB of dedicated graphics memory, and 1080p of screen resolution [13]. Tablets represent a great breakthrough in mobile computing and can handle many of the web-based GIS and geospatially enabled applications. Finally, cell phones are the smallest and most mobile device in which one can consume, interact with, and even create geospatial products. Advances in cell phones have extended the capability of geospatial data and information into the hands of millions of people every day. All of these hardware solutions act as hosts of the software applications that they provide to the practitioner.

3.6  Geospatial Software Geospatial software is the component of the toolset that uploads, visualizes, and further processes geospatial data for analysis. Geospatial software puts the tools of exploitation and production into the hands of the organization’s practitioners. Geospatial software is available on all computing platforms and includes desktop, online, and mobile versions for varying user requirements in diverse environments. Two geospatial software tools introduced previously are the GIS

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and the ELT, both of which can be used by laypeople and practitioners alike to conduct geospatial observations and analysis. Geospatial software tools have become so commonplace that almost every cell phone owner has used them for navigation. Millions have also used them for everyday events such as dating, ride-sharing, and real estate searches. Geospatial practitioners have likely used larger mapping and imagery exploitation software such as a GIS or ELT. While GIS was formerly relegated to desktop versions, advances in web mapping have expanded GIS use to many new practitioners. Web mapping is becoming increasingly popular for creating, sharing, and interacting with content in groups and among teams online and within organizations’ secured networks. Web mapping is available in enterprise versions behind an organization’s firewall and in online versions on the World Wide Web.

3.7  The Importance of People in the Geospatial Toolset People are the most valuable part of the geospatial toolset, as they are the practitioners that advance the location data-to-information refinement process. Often, people are the collectors who create the geospatial data. People are also the inventors of new geospatial sensors, the managers of geospatial systems, and the analysts that transform geospatial data into the vital reports that inform leaders in the organization. Additionally, people are creatively dynamic, and can pivot from task to task, adjusting according to changing situations. As the Information Age advances and discussions of artificial intelligence and machine learning progress, it is the people who still provide the most dynamic use of geospatial data, information, and intelligence, and produce the most meaningful geospatial analysis. People enhance objectivity during analysis and peer review and provide training and mentorship that helps practitioners along their journey. Only another person can test a practitioner’s subjective spatial thinking and interpretations by providing a second set of eyes and a second opinion. Adding more people to this process continues the path towards objectivity through structured peer review. This vital function allows the geospatial analysis of an individual practitioner to gain exposure to new ideas and expand analytic variables and interpretations. Finally, people provide the custom-tailored training and personal mentorship that helps to develop a practitioner’s career. Because so much of the tradecraft of geospatial analysis comprises tacit knowledge that is passed down from analyst to analyst, only people can provide the relevant training and mentorship for mastering geospatial analysis workflows. Individual people use intuition, creativity, reasoning, and perspective to solve the world’s most perplexing geospatial analytic puzzles, as will be explored in subsequent chapters.



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3.8  Conclusion The toolset required for geospatial analysis connects the location mindset learned in the previous chapter with the geospatial skill sets explored in the following chapters. Exciting new developments in the geospatial toolset are making geospatial analysis easier, more available, and in more demand than ever before. These include improvements in the sensors, systems, hardware, software, and people. The IoT has created a host of new sensors and devices that can record accurate GPS locations and broadcast them on the internet. Commercial satellites are launching new capabilities each year that drastically increase practitioners’ ability to visualize the Earth’s surface and all of the entities and events therein. Governments and private companies are geo-enabling their systems to ingest locational and attribute data and create geospatial datasets that are ready for geospatial analysis. (In the case of the U.S. government, the GDA is mandating such activities.) Hardware is going mobile, with smaller and faster laptops, tablets, and cell phones that house the speed, storage, and screen resolution that can handle some geospatial applications. Web mapping software has revolutionized user experiences with GIS, making it more accessible and easier to learn and use. This has created a decentralized cadre of users across the world who can conduct geospatial analysis and share their entire projects with others. In order for all of these new users to optimize their experience with geospatial analysis, they will need to build on their location mindset and geospatial toolset with a geospatial skill set, explored in subsequent chapters.

References [1] Library of Congress, “Galileo and the Telescope,” www.loc.gov/collections/finding-ourplace-in-the-cosmos-with-carl-sagan/articles-and-essays/modeling-the-cosmos/galileoand-the-telescope. Accessed December 11, 2022. [2] Online Etymology Dictionary, “Data,” www.etymonline.com/word/data. Accessed December 11, 2022. [3] Jade, “The History of the Camera,” History Things, November 8, 2021, https://historythings.com/the-history-of-the-camera/. [4] UN Geospatial Network, “BLUEPRINT Geospatial for a Better World: Transforming the Lives of People, Places, and the Planet,” United Nations Committee of Experts on Global Geospatial Information Management, 2020, https://ggim.un.org/meetings/GGIMcommittee/10th-Session/documents/2020_UN-Geospatial-Network-Blueprint.pdf. [5] U.S. House of Representatives, “Geospatial Data Act of 2018,” 43 U.S. Code CH 36 Geospatial Data, https://uscode.house.gov/view.xhtml?hl=false&edition=2019&path=%2Fprelim%40title43%2Fchapter46&req=granuleid%3AUSC-2019-title43-chapter4 6&num=0&saved=L3ByZWxpbUB0aXRsZTQzL2NoYXB0ZXI0Ng%3D%3D%7CZ

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[6] Wilkinson, M. D., et al., “The FAIR Guiding Principles for Scientific Data Management and Stewardship,” Scientific Data, Vol. 3, 2016. [7] ESRI, “What Is Raster Data?” ArcGIS for Desktop, https://desktop.arcgis.com/en/arcmap/10.3/manage-data/raster-and-images/what-is-raster-data.htm#:~:text=Rasters%20 are%20digital%20aerial%20photographs,land%2Duse%20or%20soils%20data. [8] ESRI, ArcGIS Software with Imagery basemap, https://pro.arcgis.com/en/pro-app/latest/ help/mapping/map-authoring/author-a-basemap.htm. [9] ESRI Technical Support GIS Dictionary, “Vector Data,” https://support.esri.com/en/otherresources/gis-dictionary/term/7cbd3f7c-e17f-4bb0-a51a-318ccf5b68f1#:~:text=and%20 Esri%20technology.-,vector,as%20ordered%20lists%20of%20vertices. [10] ESRI, ArcGIS Software with Streets basemap, https://pro.arcgis.com/en/pro-app/latest/ help/mapping/map-authoring/author-a-basemap.htm. [11] ESRI, ArcGIS Software Attribute Table. [12] DC.gov, Office of Victim Services and Justice Grants, “Private Security Camera System Incentive Program,” https://ovsjg.dc.gov/service/private-security-camera-system-incentive -program#:~:text=The%20program%20provides%20a%20rebate,s)%20including%20 any%20applicable%20tax. [13] ESRI, “ArcGIS Pro 3.0 System Specifications,” https://pro.arcgis.com/en/pro-app/latest/ get-started/arcgis-pro-system-requirements.htm.

4 The Geospatial Skill Set: Observation Principles 4.1  Introduction to Geospatial Observations The geospatial skill set is a collection of principles and practices for transforming geospatial data into useful information. As the final element of the geospatial mindset, toolset, and skill set, the mastery of it requires the most time, attention, practice, and mentorship. Within the geospatial skill set, three nested competencies frame location-based research: observation, analysis, and communication (OAC). Figure 4.1 shows location as central to the OAC framework, which organizes the remaining chapters of the book. Note that this framework includes often overlooked and sparingly documented aspects of geospatial analysis: preanalysis observations and post-analysis communications. To start, observations initiate and form the basis of the analysis and communications that follow. Geospatial observations have special principles and practices. From engaging in the location mindset and the cerebral grid to creating the external conditions that optimize observations, this chapter introduces the principles of geospatial observations, including their definition, purpose, and the role that visualization plays in their development.

4.2  Defining Geospatial Observations Observations are sensory perceptions (usually visual) that humans register as significant and then record in some way, either in memory or in external docu51

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Figure 4.1  Location is central to geospatial OAC.

mentation. As a verb, a geospatial observation is the act of visually sensing a location, including the entities at that location, and then recording significance in some way. As a noun, a geospatial observation is the recorded combination of visual and locational data that a practitioner deems significant. Registering significance is central to the observation process; this establishes a level of importance that motivates further cognitive attention. Recording then becomes necessary to allow practitioners to both revisit their observation and share observations with peers for feedback. Observations can be registered through any of the senses, but geospatial observations are a subset conducted by the eye with help from the brain, including via place and grid cells in the hippocampus. Geospatial observations can be both conducted and created. Conducting a geospatial observation entails seeing geospatial data presented in a visual environment, whether in a GIS, ELT, or other software interface, or in nature. It further entails slowly and methodically using the eye, supported by the brain and cerebral grid, to scan raw data until registering something significant, which separates relevant from irrelevant data. Creating a geospatial observation entails recording this significance as a geospatially focused visualization, either within the mind or as an external notation. External notation can include writing it down on paper, keeping ordered notes in a spreadsheet, or saving slides of the image with annotations. The process of conducting and creating geospatial observations transforms visual data into more useful information for subsequent analysis.

4.3  Geospatial Observations: Purpose and General Practice The purpose of conducting and collecting geospatial observations is to identify as significant and record the Earth-referenced entities and events that will



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become the foundations of analysis and the eventual assessment. An entity is something with an independent existence, and geospatial practitioners often use this term to describe their object of focus. Entities can be people, places, or things. Examples of entities include animate and inanimate objects, events, places, and even political bodies or commercial enterprises. Producing highquality observations of entities and events is key to effective geospatial analysis and communication; as a sound philosophical conclusion relies on accurate premises, a geospatial assessment depends on the quality of the observations that form its basis. As a general practice, when conducting a geospatial observation, the practitioner focuses attention on entities to identify them and determine their location and significance. Some practitioners may be able to identify entities using spatial reasoning methods such as object and attribute differentiation, mental rotation and construction, and object recall, but these subjective methods of identification still need verification through geospatial analysis and objective peer review. If not able to immediately identify an entity, the practitioner must examine it with further scrutiny and analysis (covered in more detail in subsequent chapters).1 Either way, conducting and creating geospatial observations are necessary skills that initiate the OAC framework and are initial parts of the broader geospatial data-to-information refinement process.

4.4  Geospatial Observation Principles Geospatial observation principles are the general propositions that are the foundation of practices (detailed in Chapter 5). They incorporate elements of the location mindset along with new elements of cognitive and environmental factors. These principles are generally sequenced following a practitioner’s geospatial observation workflow and will help practitioners optimize the experience of conducting and creating a geospatial observation: 1. Geospatial observations are either discovered incidentally, or directed by the collection and processing of data according to a practitioner’s understanding of their target. 2. Visual environments enable superior geospatial observations. Prioritize bringing data into a georeferenced visual environment.

1. One can observe and identify entities in nature, in literal representations on imagery or video, or in nonliteral representations on maps. One can observe and identify entities by focusing on external attributes with the naked eye, and with the aid of instruments such as cameras, telescopes, and binoculars. One can also observe and detect internal characteristics of the entity to aid identification by using tools such as microscopes, X-ray machines, and computers.

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3. Observation is an initial waypoint in the data-to-information transformation. Once geospatial data is collected, processed, and brought into a visual environment, geospatial observations may begin. 4. Optimize conditions when possible for geospatial observations. Create the best sensory conditions for conducting observations by adjusting lighting, focusing attention, and slowing observations. 5. Err on the side of uncertainty during observations. Uncertainty should be the default mental condition, and objective statements that acknowledge the perennial existence of gaps in information should be similar to the null hypothesis. This approach differs slightly from expressions of confidence, a term that describes a mental condition derived from the accumulation of mostly objective building blocks towards knowledge. Uncertainty and confidence work together as opposite sides of a coin: uncertainty imagines or describes elements of the subject that remain unknown, while confidence describes the basis of evidence to support an assessment. 6. Unite the grids. Unify the internal location mindset, the cerebral grid, and the external geographic grid as geospatial observations are conducted. 7. Practitioners conducting geospatial observations should focus on both space and time. 8. Reference to resolve. To resolve a location, reference the geographic grid. To reference an entity’s identity, use documents or people. Referencing moves the observation from subjectivity towards objectivity. 9. Use a consistent approach to develop observations. For example, the Four Cornerstones are a method described in detail in Chapter 5 that helps practitioners to resolve entities. 10. Collect, document, and organize observations to prepare for analysis. The more observations that one collects and the better they are documented and organized, the easier it will be to continue to refine them during geospatial analysis. 11. Observations are iterative. They can occur at any point of OAC and are part of a constant process of thinking, learning, and communicating. To examine how these principles help practitioners to transform data into useful information, the following sections expand on some of the most important aspects of select principles: the importance of collection for directing observations, visualization, focused attention, georeferencing, and improving objectivity.



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4.4.1  Directed Observations: Collection Driven by Target Understanding

Geospatial observations are either discovered incidentally or directed by the collection of geospatial data according to a practitioner’s understanding of the target. This section focuses on directed observations, and how target knowledge affects geospatial data collection methods. Collection of geospatial data must be structured according to the practitioner’s best understanding of their research target. Understanding the target in categories such as animate or inanimate or moving or fixed is key to employing the correct sensors, parameters, and other data collection methods that will produce the desired geospatial data. When selecting a target for collection and eventual observation, one should take into account the extent to which the target is moving, fixed, animate, or inanimate. In general, one can identify fixed inanimate entities such as a building by collecting and observing the target on imagery or video. Most inanimate, fixed objects have stable and durable geospatial indicators such as color, shape, and location. Fixed objects such as buildings may require both imagery and spatial observations to derive accurate assessments. For example, practitioners could conduct imagery observations to identify the details of a factory. Then a practitioner could conduct spatial observations to better understand the factory’s corresponding political boundaries and geographic features. The more an inanimate object such as a vehicle moves, the more a practitioner can benefit from using video analysis to analyze its literal status or spatial analysis to analyze its nonliteral representation (points on a map). When moving, practitioners can also collect locational data by using GPS. Many inanimate objects, such as cell phones and vehicles, are connected to the IoT and therefore the electronic grid. They are proxies for humans, and their locations and attribute data are revealed during the observation process. Animate objects can be stationary or moving, and the same rules apply. 4.4.2  The Importance of Visualization

This book defines visualization as carefully applied attention to visual information, which is the most important type of sensory information related to geospatial analysis. While humans can experience location in all five of these senses, the eye sits atop the sensory hierarchy. The amount of data processed and the extent to which our brains find it compelling varies by sense. Studies of the human sensory hierarchy have established sight as the most important human sense, followed by hearing, touch, taste, and then smell [1]. The human eye and corresponding brain components process more data than any other sense, and humans tend to prioritize visual data in decision-making. In the brain, visual processing neurons make up about 50% of the cerebral cortex, compared to 8% for touch and just 3% for hearing [2]. These facts establish the brain-eye connection as the big data sensor of the human body. Further, visualizations

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convey large amounts of information quickly and coherently and tell a story that words alone cannot. For these reasons, visualizations are key to geospatial observations. 4.4.3  Optimizing Conditions: Focused Attention Improves Refinement

Transforming data into useful information requires deliberately focused attention at certain intervals. This is why professional practitioners and citizen scientists alike should ensure that their observations are guided away from fast and emotional perceptions and towards attentive, careful, slow observation-based visualizations. The following three aspects will improve a practitioner’s ability to deliberately harness attention at the right times: focus, single-tasking, and slow thinking. Practicing focus brings clarity to observations. Focus occurs at two levels: in general, making an entity the center of attention, and, specifically, making it the central feature of clarity in a visual observation. When one promotes an entity from peripheral vision to focus, one can extract much more detail. Then single-tasking applies visual and cognitive resources towards one topic, or observation, and blocks outside distractions. It is the opposite of multitasking, which may allow for completing a breadth of tasks at some level of mediocrity, but with greater potential for mistakes, and with little depth. In contrast, investing in single-tasking may yield increased depth of focus and minimize mistakes (at the cost of covering a narrower group of tasks). Finally, a key practice for applying attention to improve understanding is slow thinking, coined by psychologist Daniel Kahneman in Thinking, Fast and Slow. Khaneman described “System 1 thinking” as fast, instinctive, and emotional and “System 2 thinking” as a slow, deliberative, and logical way of thinking that improves understanding [3]. Taken together, focus, single-tasking, and slow thinking are practical methods for directing attention that facilitates a data-to-information transformation. To continue this transformation, next prioritize georeferencing visual data and visualizing geospatial data. 4.4.4  The Importance of Pairing Locations and Visualizations

Pairing visualization and location creates geospatial observations, which become the basis for subsequent geospatial analysis. Pairing visualizations with locations greatly enhances the practitioner’s ability to conduct meaningful observations. From the psychological perspective of spatial thinking, careful visual attention starts with object and attribute differentiation, as introduced in Chapter 2. Visualization may then further account for more abstract locations of an object in space and the relative locations of the attributes on the object.



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Next, from a geographical perspective, pairing visualizations and locations means applying Earth-referenced locations to observations. This catalyzes a cascade of relations and context that will drive the full scope of geospatial observations and analysis. In this sense, location acts as a bridge between psychology and geography and integrates the subjectively sensed colors, shapes, and relative locations of entities with their absolute, objective locations on Earth, including entities in those locations, their relations to other entities, and context as interpreted and shared with others. Figure 4.2 shows an example of the importance of pairing visualization of an entity with its location. The principle of pairing location and visualization requires georeferencing visual data and information, which means linking it to geocoordinates through automated processes or manual search methods, or visualizing georeferenced tabular and/or vector data in a GIS. Either route optimizes the geospatial observation by prioritizing the pairing of locations and visualizations for presentation to the practitioner. One version of georeferencing is georectifying, or the overlaying of the geographic grid onto images so practitioners can access geographic coordinates and correlate them to entities as part of their geospatial observations. The image, now equipped with latitudes and longitudes, can then be orthorectified, which takes into account elevation and other factors that make the geographic coordinates more precise. Linking geocoordinates to visual and tabular data offers practitioners an organized, durable, and measurable medium for conducting geospatial observations of locations on maps and imagery and is an anchor for layering multiple forms of geospatial data. Successful geospatial observations identify entities, link them to Earth-referenced

Figure 4.2  The importance of pairing visualization with location [4].

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locations, and record these links for ongoing integration and refinement during geospatial analysis. The principle of pairing locations and visualizations also moves observations from the subjective towards the more objective. For example, indexing visual observations with geocoordinates facilitates external peer review by making it easier to store and review observations over time. In another example, creating robust tabular geospatial data involves joining locational data with attribute data that will enhance the locations with extra information. Once this tabular geospatial data is uploaded into a GIS, practitioners can take advantage of these locations and their attribute data in a visual environment, greatly increasing the ability to transform the data into more useful information. These geocoordinate-enhanced visualizations are then more easily available for peer review to improve objectivity. This will solidify the geospatial observation as a building block for more complex geospatial analysis. 4.4.5  Observational Uncertainty as a Default Position

The human brain can only interpret select frames of the large volumes and high speed of the visual data that it receives, and so it seeks to fill resulting gaps, opening windows of uncertainty and potentially subjective bias [5]. While visual data can make strong immediate impressions, carefully considered observations are more difficult, and so practitioners should err on the side of uncertainty as a default approach to visual data. Beginning an observation from a mindset of uncertainty allows one to slowly build indicators and evidence towards a convincing identification or assessment that can be more objectively understood. As stated in the earlier principle, uncertainty should be a default mental condition that acknowledges the existence of gaps in information. Certainty, especially when reached subjectively, can blind one to other perspectives and possibilities and even make one hostile to opposing points of view. Because of this, practitioners must be cautious not to allow unrefined visualizations to have outsized influence, as they remain preliminary or subjective. Instead, the practitioner should openly acknowledge that visual perceptions in an individual’s mind are rife with uncertainty and that further refinement is required to displace subjectivity and improve clarity. Uncertainty permeates the pursuit of knowledge; it is humanity’s perpetual penumbra, the gray area between what is known and unknown. Acknowledging uncertainty is an essential first step towards reducing it. Assume that uncertainty plays a role in most observations and that one may never obtain certainty. Uncertainty may persist throughout all three steps of the OAC workflow and will only improve in iterative steps as one continues to push the boundaries of one’s knowledge. Assume that incidental ignorance may be in part to blame and that deliberate deception may also play a role in uncertainty



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during one’s observations. In order to reduce uncertainty, practitioners should be honest with themselves about areas of uncertainty and transparent enough to communicate this to others at all times. The following are some of the causes and remedies for observational uncertainty: ignorance, deception, and communication and refinement of uncertainty. 4.4.5.1  Ignorance

Ignorance is not knowing. Acknowledge ignorance by mapping the limits of one’s knowledge, and then use inquisitiveness, openness, and research to expand those boundaries. All practitioners have areas of ignorance that need improvement, and knowing one’s own capabilities and pushing into uncomfortable places of ignorance to expand knowledge can be challenging. Collecting additional data, developing observations, employing references, and conducting additional research will help the practitioner to overcome ignorance and mitigate uncertainty. 4.4.5.2  Deception

Uncertainty could be prompted by deception. Be aware that nature and humans use deception to confuse and hide their identity and whereabouts. State actors use deception such as camouflage and decoys to obscure their capabilities and intentions. Nonstate actors change proxies (phones, vehicles, houses) in order to obscure pattern-of-life indicators. Deception could be present at every level of observation, and the practitioner should add the consideration of deception to observational processes. 4.4.5.3  Communication of Uncertainty

Open communication helps to mitigate uncertainty. Practitioners should clearly communicate where uncertainty exists in their own interpretations. Practitioners should communicate to themselves any area of uncertainty that needs further examination in order to define the boundaries of what is known and unknown. In order to further expand that boundary, practitioners should also communicate with colleagues to provide independent interpretations. Colleagues can help to clarify the observational areas of uncertainty, identify new areas of uncertainty, and provide insights that may reduce both. 4.4.5.4  Uncertainty Refinement

The practitioner should embrace uncertainty during observations and use it as an iterative tool that helps to improve confidence in assessments. Uncertainty refinement begins as one clearly defines what is unknown in their research endeavor, which frames research questions, data collection, and subsequent observations. Next, observations and analysis help the practitioner to address the unknown with an assessment. However, each assessment will likely open new

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windows of uncertainty, framing new questions that will guide one towards the next iteration of research and discovery. From the starting point of uncertainty and through its refinement, one can build confidence, a mental condition derived from the accumulation of mostly objective building blocks towards assessments that clarify what is known about a topic. 4.4.6  Reference to Resolve

Reference to resolve entails using reference data and information to help to identify an entity and is one of the core principles of a geospatial observation. Similar to how additional GPS signals improve locational accuracy, so additional references to entities, including additional human peer review, can improve accuracy of assessments. A reference can be a document such as a key, journal article, or book; it can be another image or dataset to which one can compare, or it can be a person who can provide assistance or peer review. As stated earlier, visualizing georeferenced data and information provides a shareable index for referencing during peer review. Georeferencing links visual data to the geographic grid and allows it to be reliably referenced over time by others. Visualization of geospatial data allows multiple subjects to make their own assessment of the same data and information. The act of visually sharing systematically referenced observations turns internal ideas into external objects that can be observed by others; this facilitates peer review among multiple subjects and leads towards more objective assessments over time.2 It is precisely the act of sharing and peer reviewing that mitigates and corrects many of the common pitfalls that practitioners experience when conducting subjective visualizations. Figure 4.3 shows the geospatial, computer, print, and human references that practitioners can use to resolve entities and research questions. Using these resources as references provides a measure of objectivity that helps the practitioner to avoid some of the major pitfalls of visualization.

4.5  The Pitfalls of Visualization In the Information Age, visual data is abundant while attention is scarce, suggesting an economics of attention where attention is scarce relative to sources of 2. Law enforcement investigations that feature eyewitness accounts provide excellent case studies in which to examine the quality of subjective visual data. When eyewitnesses perceive visual data, they are encoded into the brain’s memory, stored, and retrieved on-demand. Subjects’ self-confidence in the memory of visual perceptions is emotional, and often inversely related to its accuracy [5]. In other words, a subject’s ephemeral visual perceptions are subjective, uncertain, and, by themselves, unverifiable. These perceptions are subject to visual and memory variables and not available for quality control. One can never perfectly replay a visual perception that one witnessed free of the filters and shortcomings that accompany memory, storage, and retrieval.



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Figure 4.3  Practitioners can examine geospatial, computer, print, and human references to resolve entities and research questions.

data and information [6]. Carefully focused attention is necessary to transform data into useful information, yet this can be challenging in visual environments designed to capture attention through fast visual engagement. For example, because visualization is so powerful, social media companies develop business models based on vying for human visual attention, and they shape most aspects of their platforms to capture users’ visual attention quickly through the promotion of content encouraging emotional reaction instead of carefully focused attention.3,4 Further, the visual world can present the observer with data overload, tunnel vision, visual paralysis, and a host of other pitfalls that shade perceptions and create faulty visualizations. Tunnel vision occurs when one perceives a narrowly focused field of view resulting in loss of perspective. Data overload is having too much information; one must limit the amount of ingested visual information and rest, recover, and revisit the visual world in manageable parcels. Visual paralysis occurs when visual stimuli overtake other senses and one cannot look away. Together, these pitfalls lead practitioners away from the sound principles for optimizing geospatial observations. Training an observer out of tunnel vision involves a forced broadening of one’s aperture in order to inject perspective into the frame. Tunnel vision, when 3. In fields such as internet advertising, psychology, and media studies, tracking eye movement is often used as a correlate for studying attention in general (Adam Greenberg, “The Role of Visual Attention in Advertising,” William Brady, et al., “Attention Capture Helps Explain Why Moral and Emotional Content Go Viral,” Kate Keib, et al., “Picture This: The Influence of Emotionally Valenced Images, On Attention, Selection, and Sharing of Social Media News”). 4. For example, terms such as “going viral” describe fast, emotional visual engagement behavior at scale, usually within a social media context.

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conducting geospatial observations, refers to having a narrow visual perspective that fails to see the peripheral context. It occurs because humans tend to fixate on objects and fail to see broader perspectives and context. The observer should first absorb the broad contextual elements and then narrow to focus more closely on an entity. While conducting observations, the practitioner should remain aware of the dangers of tunnel vision and systematically broaden their perspective by moving between narrow focus and broad perspective during the observation process. Next we offer examples of pitfalls associated with both visual and geospatial data. The first offers a specific example of visual information that is presented as an explanation of an important, tragic world event: civilian flight MH17 being destroyed over Ukraine in 2014. The second outlines the importance of preparing geospatial data for proper visualization to avoid the pitfall of creating inaccurate visualizations of geospatial data. 4.5.1  Pitfalls of Geospatial Data: Imagery

Image-based visual data and information is rife with potential pitfalls. Figure 4.4 presents a graphic designed to manipulate individual psychology that can be resolved through deliberate geospatial observations and reasoning. One should approach the image with the location mindset, optimized conditions, and uncertainty. The image, taken from a high altitude, appears to show clouds, the Earth below, and two objects that require careful observation. There are two black boxes in the image; the left box outlines an area, and the right box provides a zoomed-in inset of one of the objects. The objects differ in shape.

Figure 4.4  Example of a graphic designed to manipulate individual psychology that can be resolved through deliberate geospatial observations and reasoning [7].



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Note the broad outlines of shapes, colors, and any other visual perceptions that appear. As the practitioner increases attention and focus, they can begin to create careful observations of the specific objects and their attributes. Now ask: • What are the man-made objects in the image? • How do the two objects differ in shape and how does this affect one’s interpretation? • In the inset, what are the tones and shapes in front of the object that one observes? The practitioner should now pause and analyze the possibilities. Where would one’s thoughts likely drift? As the practitioner looks more closely at the inset, one can allow one’s eyes to shift focus and drift away to the terrain underneath. One can incorporate spatial reasoning by practicing object recall: revisiting the main picture from which the inset appears to be enlarged and then oscillating between the two, remembering the details of each. What new thoughts might emerge? This image, interpreted in different ways, could lead to drastically different assessments: 1. Literal interpretation: This image presents evidence of what may have been the final moments of a commercial airliner before a fighter plane shot it down. 2. Deception and disinformation: This image may be fake, published deceptively by a foreign government or organization to influence public opinion in a certain direction. 3. Uncertainty: This image presents visual data that is difficult to interpret and may take more images and perspective to corroborate assessments (1) or (2). If tasked with interpreting this image, how would a layperson fare? What principles could one use to approach a successful geospatial observation of the image? Using uncertainty as the default condition, focusing attention, slowly absorbing the details, fusing location with the visualization, and using references are the starting points. The image in Figure 4.4 is an example of deception and disinformation.5 Disinformation is information that has the function of misleading someone as 5. In this case, focusing on the location and the corresponding terrain differences between the inset and the rest of the background image should reveal the true nature of the fighter plane

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an intention or goal. This image is a graphic that was composed and released by Russian state media with the purpose of suggesting that Malaysian Flight MH17 was shot down by a fighter jet. Here, the graphic is composed in a manner to manipulate visual perceptions and confound sense-making abilities. The location mindset and the principles of geospatial observations should be sufficient to prevent the practitioner from the pitfalls of imagery observations, but observing geospatial data on maps comes with other considerations. 4.5.2  Pitfalls of Geospatial Data on Maps

Geospatial data also contains pitfalls that practitioners must overcome to successfully transform the data into accurate, visualized information. In order to prepare such data for visualization, there are often a number of steps that practitioners must accomplish. Geospatial data can originate in unstructured formats that require structuring or bringing into a table, where a practitioner can order and relate the data in fields and records. Geospatial data can also appear in structured tables, but may still need extensive cleaning and formatting. Cleaning is a process that arranges that data so that each record is properly and completely filled out and fits into the proper corresponding fields. Data that is not cleaned can present a major pitfall once that data is improperly visualized. Formatting involves a practitioner filling out the column header (field names) so that they are clear, succinct, free of prohibitive characters, and compatible for upload into a specific software tool or GIS. Once uploaded, more visualization pitfalls occur when address data is unsuccessfully geocoded. Sometimes the match rate of addresses may be low, and certain points may need to be rematched. Another pitfall arises when points are geocoded to a centroid and appear somewhere other than the point where the event occurred. Examples include unvalidated addresses that default to the city center, the ZIP code center, or the state center. Figure 4.5 shows a distribution of points that appears to accurately represent the dataset. To avoid a potential pitfall, the practitioner changes the visualization to a heat map to see any obscured points. The heat map visualization revealed 24 points resulting from unvalidated addresses, stacked on the city centroid. If geographic coordinates are geolocated, points may not be projected correctly, leading to incorrect visualizations that will misinform audiences. The points may need symbology and other adjustments in order to provide the optimum visual experience for the practitioner and the customer. To overcome the potential pitfalls associated with visualizing spatial and geospatial data, the best approach is to be slow and thorough, use GIS help documentation, online blogs, and videos, and work with a mentor. visual. See [7] for a full analysis.



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Figure 4.5  The pitfalls of unsuccessful geocoding visualized on a map [8].

4.6  Conclusion In 1610, Galileo Galilei remarked [9]: “…the nature or matter of the Milky Way itself, which, with the aid of the spyglass, may be observed so well that all the disputes that for so many generations have vexed philosophers are destroyed by visible certainty, and we are liberated from wordy arguments.” This chapter addressed various assertions and assumptions made in this statement by outlining principles for geospatial observations that include the power of the braineye connection and visualizations. It also cautioned the practitioner against the pitfalls of visualizations. Galileo’s quote embodies a common human valuation of visual data as being most important. However, given the pitfalls associated with visualization and certainty, the notion of visual certainty should raise concern in the minds of citizen scientists and trained professionals alike. Instead, this chapter introduced uncertainty as a philosophical starting point from which to approach visual data. Uncertainty will continue to be a theme in this book across all the remaining elements of OAC. Indeed, as deep fakes, deception, disinformation, and the big data deluge inundate the market of attention, practitioners must have a principled approach. This should include the location mindset, the geospatial toolset, and the skill set to transform data into accurate and objective information. Galileo’s quote also underscores a second principle: while visualizations alone can seemingly speak 1,000 words, infusing them with locations in a geospatial observation adds untold value. This principle should permeate one’s research as geospatial observations are conducted and collected. Yet beyond

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principles, practitioners also need a specific set of best practices to guide the refinement of geospatial observations into useful information. Chapter 5 introduces practices that will guide a practitioner’s skill set in the refinement of geospatial observations into the basis of geospatial analysis.

References [1] University of York, “Is There a Universal Hierarchy of Human Senses?” November 5, 2018. www.york.ac.uk/news-and-events/news/2018/research/is-there-a-universal-hierarchy-ofhuman-senses/#:~:text=Research%20at%20the%20University%20of,universally%20 true%20across%20all%20cultures.&text=Researchers%20found%20that%20rather%20 than,cultural%20factors%20were%20most%20important. [2] Grady, D., “The Vision Thing: Mainly in the Brain,” Discover, June 1, 1993, https://www. discovermagazine.com/mind/the-vision-thing-mainly-in-the-brain. [3] Kahneman, D., Thinking, Fast and Slow, New York: Farrar, Straus and Giroux, 2011. [4] ESRI, ArcGIS Software with Imagery basemap, https://pro.arcgis.com/en/pro-app/latest/ help/mapping/map-authoring/author-a-basemap.htm. [5] Albright, T. D., “Why Eyewitnesses Fail,” Tedx San Diego 2016, 2016, www.tedxsandiego. com/transcripts/2016-talks/thomas-albright/#:~:text=Finally%2C%20we%20have%20 confidence%2C%20or,discount%20their%20version%20of%20events. [6] Lanham, R., The Economics of Attention, Chicago, IL: The University of Chicago Press, 2006. [7] Kivimaki, V. -P., “Russian State Television Shares Fake Images of MH17 Being Attacked,” Bellingcat, November 14, 2014, https://www.bellingcat.com/news/2014/11/14/ russian-state-television-shares-fake-images-of-mh17-being-attacked/. [8] ESRI, ArcGIS Software with Light Gray Canvas basemap, https://pro.arcgis.com/en/proapp/latest/help/mapping/map-authoring/author-a-basemap.htm. [9] Tufte, E., “Nature Is Nowhere Rectangular: Galileo’s Starry Messenger Meets Matisse’s Le Guignon,” Skeptical Inquirer, Vol. 30, No. 6, November/December 2006, p. 38, https:// cdn.centerforinquiry.org/wp-content/uploads/sites/29/2006/11/22164553/p38.pdf.

5 The Geospatial Skill Set: Observation Practices 5.1  Introduction to Geospatial Observation Practices This chapter codifies specific practices for conducting and creating consistent geospatial observations from geospatial data. These practices will further the geospatial data-to-information refinement process and help to produce quality observations that will become the building blocks for analysis. The practices are presented as structured geospatial observation techniques (SGOT) that enhance tradecraft skill. The skills are approachable and easily replicable during the course of geospatial observations by laypeople and practitioners alike. They require little to no prerequisite knowledge or skills, just practice to learn and apply the techniques. Practitioners can then add these skills to the analysis and communication elements of the geospatial skill set.

5.2  SGOT SGOTs are geospatial industry tradecraft practices that enhance the practitioner’s ability to develop higher-quality and more objective geospatial observations.1 SGOTs integrate the foundational principles from Chapter 4 with more specific practices for developing the significance, relevance, and meaning

1. Chapter 7 explains geospatial analysis in terms of professional trades in more detail. 67

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of geospatial observations as part of an emerging assessment. The following SGOTs will be examined in this chapter: 1. 2. 3. 4. 5. 6. 7. 8.

The Four Cornerstones; Slow observations; Observational perspective; Focal point control; Observational reasoning; Observational notations and communications; Observation of process flows; Observable keys.

Once data is collected and visualized, the practitioner can use observation principles to connect entities to those locations and discover their identities, relationships, and context. Then the journey towards enhanced understanding of the geospatial world begins with the methodical practice of SGOTs, starting with the Four Cornerstones. 5.2.1  The Four Cornerstones for Observations

The Four Cornerstones is a structured method for systematically examining an entity in order to identify it. It consists of four categories: location, color, shape, and context. The Four Cornerstones should be primarily used during geospatial observations of entities on imagery, video, and in nature. It can also be used to a lesser extent for observations of spatial data in a GIS, but only after the data has been uploaded and is available for visualization. Practitioners should review each cornerstone step by step for guidance through a complete observation process of an entity and its context. Although initial analysis may occur alongside observations, this chapter will focus on observation processes. The following chapters will examine using the Four Cornerstones for analysis and then communications. Figure 5.1 illustrates the Four Cornerstones and provides practitioners with a road map for conducting geospatial observations. 5.2.1.1  Location

The location category of the Four Cornerstones consists of the Earth-based locations that provide a starting point for research questions. Pairing locations with visualizations, the practitioner must first orient themselves to the broad area locations such as the region, country, or state. This requires observing a map with features such as state boundaries, natural features such as bodies of



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Figure 5.1  The Four Cornerstones is a method introduced in the skill of observations for systematically examining an entity in order to identify and understand it.

water, man-made features such as road and rail networks, and point features such as cities. Once the practitioners are broadly oriented, they can progress to the more specific point targets and entities on maps and imagery that will form the basis of the research project. Entities on the earth’s surface consist of people, vehicles, equipment, buildings, and other structures that practitioners can observe to derive more specific identities, relationships, and context. To study these in a structured way, the location category includes points, lines, and areas (like vector data). Figure 5.2 shows an image of an airfield that demonstrates how a practitioner might observe an entity paired with point, line, and area locations.

Figure 5.2  The Four Cornerstones: location. This image of an airfield demonstrates how a practitioner might observe an entity paired with point, line, and area locations [1].

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Points

Points are precise measurements of relative or absolute locations. The most precise point locations are measured on the geographic grid and are presented as geocoordinates. Less precise relative points include street addresses and cultural names for locations. In datasets, point data can appear as street addresses (e.g., 1234 Maple Street, City, State, ZIP Code), which can then be geocoded (i.e., linked with geocoordinates). Point data may already include geocoordinates, which are measured and appear in different formats including decimal degrees (44.878611, 18.813056) and degrees, minutes, seconds (44°52′43″N 018°48′47″E). Another geographic grid system used by militaries in the North Atlantic Treaty Organization is the Military Grid Reference System (MGRS). This system divides the Earth’s surface into a grid and presents specific points as a string of numbers and letters (34TCQ2727171792). Figure 5.3 shows a table of common locations found in a dataset. When conducting geospatial observations, establishing point locations is a vital step that provides precision, provides structured entity identification, and enables more organized datasharing between organizations. Once those points’ locations are established, a practitioner can examine the lines that connect them and their potential relationships. Lines

Lines are connections between points and/or areas. Lines are also geospatially accurate representations of paths for objects and political boundaries, such as transportation lanes and county, state, and country borders. Examples of lines related to transportation paths include map or GIS visualizations of roads, rail-

Figure 5.3  Table of common location types.



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roads, airplane flight paths, and sea lanes of travel. Examples of lines related to resource distribution infrastructure include map or GIS visualizations containing electricity transmission lines, oil or gas pipelines, or underground water lines within a city. Lines connect points and areas, and so they may be used to analytically relate locations; this is sometimes referred to as line of communication analysis, which will be introduced in a later chapter. Lines can also connect, separate, or distinguish areas, which are the next geospatial consideration. Areas

An area is a polygon that creates a boundary line around point locations. Area locations include the boundaries of land parcels, natural features such as lakes, or political features such as counties. The area category is made up of broader locations that can either be independent of points or represent a point’s nearby or distant surroundings. An entity at a point location has an immediate surrounding area that may provide context to the practitioner’s observations. The practitioner should shift focus between point locations and surrounding areas as part of comprehensive observations of entities. An entity’s surroundings can be observed in the literal and nonliteral realms. When conducting literal entity observations (such as direct observations in nature, or observations on imagery or video), the nearby surroundings are the first concentric ring around the point location that provides the most evidence and related characteristics. There is no set measurement for this ring, instead it is a relative measurement based on the context of the observation. For example, when conducting a post-blast observation for a police or fire department from an urban fireworks explosion, or during battle damage assessments (also called bomb damage assessments) for a defense customer, the nearby surroundings may extend 100 ft away from the point target because all of the geography within 100 ft share similar attributes and patterns of shrapnel saturation and fire damage. Figure 5.4 presents a satellite image from Iraq used by imagery analysts to conduct geospatial observations of point locations and the nearby surroundings that would eventually lead to bomb damage assessments.2 The distant surrounding provides the second concentric ring around the entity or point that extends to a further distance. In the post-blast observation example, the distant surroundings may extend from 100 ft to 500 ft away from the target, where the damage pattern is minimal and entities such as flora, vehicles, and buildings appear undamaged but one can still observe some evidence of shrapnel and debris from the blast. Other examples include a secured facility where the perimeter bounds the nearby surroundings and the public space outside of the perimeter encompasses the distant surroundings. 2. The technique used to conduct observations on point targets and the surrounding context will be introduced later in this section as the target method.

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Figure 5.4  A satellite image from Iraq used by imagery analysts to conduct geospatial observations using the target method that would eventually lead to bomb damage assessments. (After: [2].)

One can also observe surroundings using spatial data on a GIS. A practitioner could observe nonliteral data such as a vector base map enriched with overlaid vector layers of crime events. The observer could then observe the nearby surroundings of a specific armed robbery to see what cell phones and other devices were within 10m of the crime scene before, during, and after the crime occurred. The practitioner could then observe the distant surroundings to see what other similar crime types happened in the area 11m to 500m from the point location in the past, which felons convicted of similar crimes reside in the vicinity, and what gangs are known to operate in that area. As the practitioner moves between observation of points and areas, visual patterns emerge on both imagery and GIS. Patterns are a repeated or recognizable design that can be observed at points, along lines, and in areas. Patterns can appear on an entity, including the entity with its surroundings, or external to the entity. The entity may create a pattern observable within close proximity such as military tanks in a platoon formation on imagery, or the entity may be part of a broader pattern of agriculture such as a single corn stalk in a field of planted corn rows. An example of patterns observable on a GIS is the spatial distribution of symbols on a map, such as that of a person’s digital footprint moving in space and time. Previous chapters introduced the concept of a person’s pattern of life from a geospatial perspective, where the visual representation on a map of a person’s cell phone, vehicle, or other proxies moving throughout each day over a certain period often establishes a recognizable pattern. Understanding



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patterns in each context and finding animate, inanimate, fixed, and moving patterns on the Earth will provide the observer with insights into the entity and its surroundings. 5.2.1.2  Color

The color category consists of two elements: color and tone. Color is the property possessed by an object producing different sensations on the eye as a result of the way the object reflects or emits light. Color is present in nature, on photographs and videos, and on some types of aerial imagery. Some of the more prominent colors are warm colors such as reds, oranges, and yellows. The more ambiguous colors to delineate for certain cultures around the world are cooler colors such as blues and greens. The most difficult colors to delineate for people with color vision deficiency (CVD) are red-green combinations [3]. Practitioners can observe colors and the patterns generated in each to develop observations about entities. When viewing objects or depictions of objects within data (on imagery or video), the practitioner will observe the actual color or computer manipulations of color of the object of focus. Color examples include the natural green and brown tones of foliage and landscapes and the deep blues and greens of water. Although many images are in color, some are also in black and white, which require observation of tonal differences. Tone is a subcategory of color used to describe grayscale seen in black and white images, videos, and low-light conditions. It refers to the quality of brightness or darkness on an image or in a scene. Practitioners observing panchromatic imagery in grayscale must perfect the minute differences found in the 256 shades of gray [4, 5]. Observations in grayscale require more time and attention, as there are fewer colors to delineate entities. As a practitioner progresses in the practice of grayscale observations, one can more easily conduct the basic skills of object and attribute differentiation. Then one can continue to progress to differentiate man-made features from natural features and more advanced entity identification. Practitioners conducting observations should note that the darkest tones are usually those of shadows and areas where light is blocked, and the lightest are often the brightest reflections of light from smooth surfaces. Tone is also important for observations of synthetic aperture radar (SAR) imagery, as pixel brightness in a SAR amplitude image is a measure of the strength of ground-reflected microwave energy received by the sensor, which, in turn, reveals information about objects on the ground [6]. When conducting observations of a hardcopy map or a GIS, practitioners should look carefully at color to draw distinctions between features. Base map colors often include earth tones such as blue for areas of water and taupe or tan for areas of land. Green areas often denote forested areas or parks. Man-made linear features such as highways often appear in brighter colors such as yellows and oranges, and political boundaries such as state and country borders often

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appear in dark gray or black. If a practitioner uploads layers of data, one can often control the colors of the base map and point, line, and area features. Practitioners should choose colors that create contrast and that take into account CVD. Practitioners should also remember that symbols with the same colors should share similar features. Figure 5.5 shows images and a map that demonstrate how a practitioner might observe color and tone. 5.2.1.3  Shape

Shape is the external form of an entity and is also one of the most readily apparent features of an object on imagery or features on a map. The shape category consists of the following elements that inform its interpretation: size, shape, shadow, and texture. Size is the extent and dimensional measurements of an entity and is one of the most readily apparent features of an object. Entity size is determined both relatively via object-to-object comparison and absolutely via measurement, including mensuration via satellite or aerial imagery. Shape is the form of an object that includes its lines and curves and is also one of the most readily apparent features to an observer. Size and shape make up two factors that often allow practitioners to identify entities using object recall, as the two features leave a lasting impression in the experienced mind. Practitioners use size and shape to conduct object and attribute differentiation during observations. Shadow is the dark shape caused by an entity when it is located between light rays and some surface on electro-optical imagery and between a SAR sensor’s directed energy and some surface (usually the ground) [6]. Shadow often reveals much about the size and shape of an entity and may reveal aspects of shape that are not otherwise visually apparent. Texture is the consistency of a surface that may offer clues to its material composition. Together, attention to

Figure 5.5  In the Four Cornerstones color category, these images and the map demonstrate how a practitioner might observe color and tone [1, 7, 8].



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size, shape, shadow, and texture help the practitioner to develop careful observations of an entity. When conducting observations of objects represented by vector data on a GIS, practitioners can observe the shapes of individual symbols and the shape or pattern of the overall dataset’s distribution. Practitioners should note that similar symbol shapes can denote feature similarities, especially when custom symbols are used. However, common shapes such as circles may represent different features denoted by color. Custom symbol shapes can be visualizations of the entity that they represent, such as an airplane-shaped symbol on a flighttracker map to represent an airplane. Maps can also vary the size of a single symbol in order to show the number of occurrences, with smaller symbols showing fewer occurrences and larger symbols showing more occurrences. Finally, largesized symbol clusters at smaller scales can represent multiple occurrences and can break up into individual smaller-sized symbols at larger scales. Figure 5.6 demonstrates how a practitioner might observe various elements of the shape category on maps and images. 5.2.1.4  Context

Context describes the observable circumstances that form the setting for an object, entity, or phenomenon. Context answers the question why. It provides the background for how the entity, event, or phenomenon can be more fully observed and understood. During observations, visual context describes surrounding observables that helps the observer to gain perspective. Visual context is any visual information in proximity to the entity that helps to characterize the entity. An example of visual context is an observation of burning oil wells at a distance and a city surrounded by military equipment, all of which would

Figure 5.6  The Four Cornerstones shape category. Images and map demonstrate how a practitioner might observe various sizes, shapes, shadows, and textures [1, 9].

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frame entity observations during wartime. Practitioners can observe visual context on imagery and video and in nature and on maps in a GIS. Practitioners should also collect nonvisual information such as articles, journals, databases, and other media to provide context for their observations. Figure 5.7 shows how a practitioner might observe context on an image and map. One technique for conducting visual observations of context is to use the target method, outlined next. The Target Method

The target method is a technique for structuring observations of context. Practitioners can apply the target method when conducting observations that are peripheral to the entity or object of focus that promotes perspective and reduces tunnel vision. The technique is best used on imagery or video or in the real world, but also has use cases when using a GIS. To start, the practitioner should mentally construct a broad area of observation as a target. Imagine establishing a point in the center of the target and then moving outwards in concentric rings to organize one’s observations. The goal of the observer is to maximize the observation of the point and its surrounding areas in the target area. The target method can be used when conducting observations in the real world during crime scene investigations, on imagery in observations of point targets and broad areas searches, and in observations on a GIS of a data layer containing environmental damage caused by an oil spill. The Target Method Example

The practitioner should begin by establishing the point in the center of the target that represents the location of the entity that is the focus of observations.

Figure 5.7  Image and map in the Four Cornerstones’ context category demonstrating how a practitioner might observe nearby and distant surroundings as context [1].



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This requires measuring the point location of the entity to establish its geocoordinates, which then becomes central to understanding all further measurements of distance, direction, and time and the relation of all other entities. The practitioner should then establish the nearby surroundings as the next concentric ring around the point and observe this area methodically for evidence that relates it to the entity and point in the center of the target. Invoking Tobler’s Law, the nearby surroundings will be the next highest priority for observation to discover relationships between points and the entities in those locations. After delineating nearby surroundings, the practitioner should then establish the distant surroundings as that next concentric circle around the target and observe this area methodically to provide the next layer of context. The distance from a precise location and its nearby surroundings to its distant surroundings will be more expansive and will require a careful methodology in conducting and recording observations. In an imagery exploitation environment or an ELT, the practitioner can use various methods to record previously observed areas. In the real world, one can use markers, flags, or placards to mark the areas where people have already searched and flag the most significant point locations. Finally, the practitioner should establish a broad visual context as the outermost concentric ring. This ring may not be in visual proximity of the target, but it provides broader context for the area that may inform research questions. This observational groundwork will pay huge dividends later when one brings their observations into the analysis phase. Figure 5.8 shows the target method and the point target, the nearby surrounding, and the distant surrounding.

Figure 5.8  A practitioner should use the target method alongside the Four Cornerstones (location, color, shape, context) to resolve the entity [1].

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An Example of the Four Cornerstones

Training one’s use of the Four Cornerstones is a necessary requirement for improvement. A practitioner is tasked with identifying the object in Figure 5.9 as part of an ongoing investigation. The practitioner must focus first on the target entity and attempt to resolve it. The practitioner leads with location (point, line, area) for broad and narrow orientations, then focuses on the object, and begins examining its color (including grayscale tone) and then shape (size, shape, shadow, texture). Finally, the practitioner examines the broader visual context in order to resolve the entity and any other research questions that may arise. Figure 5.9 shows an entity on imagery ready for exploitation by use of the Four Cornerstones. 5.2.2  Slow Observations

Taking time to develop careful observations is vital for subsequent analysis and communication. Slow observations describe the amount of time that a practitioner spends observing entities in nature, on maps, and on imagery. This entails spending more time and more focus on an observation in order to extract additional details, including looking at every available mapped point, every image or video, and every possible look angle and time of day for collected imagery data.3 It also entails single-tasking attention to focus on depth of examination. Although practitioners’ time is usually limited, they nonetheless must focus

Figure 5.9  A practitioner should use the Four Cornerstones (location, color, shape, context) to resolve the entity [10].� 3. Other important temporal factors include the time and date of each record in a dataset (including the collection date and time), the amount of time elapsed since the image or video was taken, the amount of time that the target entity is visible on a map, in a picture, or on video, and the amount of time that the target was likely present at that location.



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attention on developing careful observations with the understanding that uncertainty lies in that which can and cannot be seen. Slow observations involve the following components: time of observation, attention, detail orientation, sensory load balancing, and observational agnosticism. 5.2.2.1  Time of Observation

To develop effective observations, practitioners must learn to slow the brain-eye connection. Begin by transforming the slow-thinking principles from previous chapters into slow observations that press time into the service of understanding. With time on target, slow observations allow attributes and relationships to present themselves. Time allows for adjustments away from bias or visual pitfalls, improvement of locational and attribute understanding, and opportunities for soliciting second opinions. Time also allows observations to become more deliberate and methodical. The practitioner should not allow the brain to lapse into fast thinking and rushed observations, as this will harm one’s ability to gain understanding and resolve a question or entity. 5.2.2.2  Attention

Apply the attention principles from previous chapters and ensure that the observation is single-tasked and focused. Eliminate conversations, unnecessary electronics, and any other multitasking temptations that will distract from the observation at hand. After eliminating external distractions, focus internal attention by using controlled, deep breaths and a deliberate commitment to visual attention. Use attention to maintain focus on the entities that are relevant to the research questions without being distracted by other visualizations within the field of view. Use attention to set one’s eyes on the target and begin to conduct object differentiation by separating the relevant and significant from the irrelevant and insignificant. 5.2.2.3  Detail Orientation

Detail orientation entails absorbing more of the smaller elements and attributes of an entity and an observation. Use attribute differentiation to delineate every detail of an entity and allow them to help to resolve the bigger picture. Sometimes details that are overlooked because of time and attention constraints are the very elements that unlock an entity’s identity or purpose. On imagery, locate the details in the entity’s location, size, color, shape, shadow, and context. On maps, locate the details by examining the attribute table, the pop-up, or the way that certain symbols attract attention. 5.2.2.4  Sensory Load Balancing

Practice sensory load balancing that prioritizes visual observation. Many practitioners experience a sweet spot of observation that entails just the right amount

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of cognitive load dedicated to entity observations, with space for new perceptions should they arise. Although one may use other senses to aid in observation (such as hearing), ensure that the eye has the most dedicated cognitive load (other senses such as hearing may provide clues that can help or harm the observation, depending on context). For example, some practitioners wear noisecanceling headphones to prioritize visual observations. 5.2.2.5  Observational Agnosticism

Remain open-minded and allow careful observations to compel the path towards an assessment. Practitioners should balance openness with intuition and experience and not allow first impressions to cloud further observation or analysis. Many observations require refinement, and are incomplete until enough time and scrutiny can be applied such that they can be moved from the individual subject-based perceptions to a peer-reviewed, objective experience. On a map, practice observational agnosticism by overlaying all available layers in order to provide a broader perspective before allowing any one dataset to dominate. On imagery, practice this by first examining all available images of a location and/or entity before solidifying a hypothesis.� 5.2.3  Observational Perspective

During the course of a thorough observation process, a practitioner should observe an entity from a variety of perspectives. The more complete the observational perspective, the better the understanding. The practitioners can improve their observational perspectives by examining dimension, distance, angle, position, scale, and time. 5.2.3.1  Dimension

Observe the entity (or use spatial thinking to imagine it) from an overhead or plan (vertical) view, an oblique view, an elevation (horizontal) view, and any other available dimensional plane. Observe in three dimensions (or stereo in imagery analysis) to gain further visual perspective. This can be accomplished with video or with images from multiple dates and/or look angles. This can also be accomplished using spatial modeling techniques and certain other computer software that allows one to virtually re-create an entity and rotate it so the viewer can see it from various directions. 5.2.3.2  Distance: Zoom and Scale

Distance from the object of focus can be selected by zooming (or moving) in and out. Zooming describes the visual distance from a fixed-sized object. The distance from which an observer visualizes an object can greatly affect what the observer sees. Practitioners can zoom out to get more perspective and context



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and zoom in to visualize the object more clearly and gain more specific details of the entity’s color, shape, location, and nearby surroundings. Scale refers to a measured ratio between the real size of objects and their representative forms, such as their representation on a map. Map scale is fixed on a hardcopy map or product. Scale changes on a digital map as a practitioner explores each scale for differing details. For example, a smaller-scale map that shows a large regional area might have a scale of 1:1,000,000. A large-scale map that shows street-level details might have a ratio of 1:10,000. Maps usually require a scale bar, which provides the observer with the scale of a given visualization. Figure 5.10 demonstrates the difference between zoom and scale. 5.2.3.3  From External to Internal

While most of the visualization process is dedicated to identifying entities based on their exterior features, practitioners should also attempt to visualize the entity’s interior space. This may be difficult to do in person when viewing an image or video, but one can make certain assumptions by using spatial reasoning while employing the concept of uncertainty. Often, the exteriors of man-made equipment are shaped to house specific interior items that practitioners can infer and visualize through mental construction. An example is the hood of vehicles that are scooped to house certain large engines. Practitioners can also further study entity features under a microscope, internal imaging machine, or computer.

Figure 5.10  (Left) Zoom describes the visual distance from a fixed-sized object [1]. (Right) Scale refers to a measured ratio between the real size of objects and their representative forms, such as their representation on a map [1].

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The internal parts of entities such as tree rings, building and shipping container interiors, the inner workings of motorized equipment, and metadata often tell an important story. If practitioners have the opportunity to dissect or examine the interior of the entities that they study, it could greatly help their understanding of what to expect on the exterior during observations. 5.2.3.4  Time

Time is an ever-present category of observation and analysis that affects everything on Earth. Time plays a particularly important role in many elements of geospatial observations. Every observation of an entity must be negated, which means discovering when the entity arrived at its current position. This establishes a timeline that helps to structure observations and analysis of the entity. Then determine whether or not the entity is mobile. If so, where did it come from, who may have placed it there, how long has it been there, and where might it be going? Observe how entities may have changed over time and how they affected their environment. Practitioners can also observe time more directly in video and on time-enabled features on a GIS or other software tool. In motion pictures, time brings entities to life and allows the observer more perspective. The more a practitioner seeks to incorporate and understand time during observations, the more data can be successfully transformed into useful information. 5.2.4  Focal Point Control

Controlling one’s focal point is key to successful observations. Similar to a marksman who practices breathing and trigger control while the sight picture drifts in and out of focus, so practitioners must control their focal points during the observation process. Pointing one’s eye at a target, maintaining focus, shifting focal points, and resting require attention and control. Focal point control involves the following elements: hard focus, soft focus, shifting focus, rest, and revisit. 5.2.4.1  Hard Focus

The term “hard focus” describes forcing one’s eye to remain on target for an extended period. Hard focus takes training, often pushing eyes to the point of fatigue. Training hard focus involves creating the optimum lighting conditions and then staring at objects for long periods of time. The initial portion of staring at a random dot stereogram, the dots with colors for eye exams or commercial pleasure, is great training for hard focus and mimics what one may encounter when viewing imagery in stereo (or 3-D) [11]. Hard focus is required when the attributes and/or identification of an entity are not readily apparent, and only time on target will resolve the research question.



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5.2.4.2  Soft Focus

When focused narrowly on an entity, employ the concept of soft focus by letting the entity emerge while surrounding details blur or melt away. The second portion of staring at a random dot stereogram involves soft focus as the object presents and the periphery blurs. Viewing an entity in portrait mode on a camera creates a similar effect. Using a soft focus lens introduces a spherical aberration that gives the appearance of blurring the background of an image while retaining focus in the foreground or focal point. Figure 5.11 is an image illustrating the concept of soft focus. 5.2.4.3  Shifting Focus

Shifting focus describes a process whereby an observer moves from one focal point to another as observations require. Allow the eye to focus narrowly on an entity while blurring the surroundings (soft focus), and then allow the eye to scan or move to other targets by blurring the focal point and focusing on details of the surroundings. Move the eye around the peripheries and focus on as many objects as possible to gain a greater understanding of the surroundings. Scan the nearby surroundings and distant proximity to gain context. 5.2.4.4  Rest

Rest involves taking breaks as the eyes fatigue. The practitioners should close their eyes to rehydrate them. One can also shift focus points, blur the vision,

Figure 5.11  This image demonstrates the concept of soft focus.

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and scan the area. Observers can also make attempts to change their immediate lighting to allow the eyes to rest. This usually involves reducing sunlight, dimming the lights, and lowering screen brightness. Sometimes it involves abandoning the target altogether and engaging in a less visual activity for a period of time. Practitioners can take this time off target to contemplate the research question, compile recent observations, or even to sleep. Then subsequent revisits of the observation can help one to see it in a new light. 5.2.4.5  Revisit

To revisit the observation is to return to an observation after resting or spending time away. Time spent away from the target researching, analyzing, and resting is a valuable part of the observation process. Upon revisit, a practitioner should approach the observation with a mindset of openness and try to reserve definitive judgment, especially if the observation is subjective.� 5.2.5  Observational Reasoning

Observational reasoning refers to carefully filling in the visual gaps. Previous chapters examined spatial reasoning and the ways that practitioners can fill visual gaps by using mental construction and mental manipulation of objects. Previous chapters also examined gaps in visual perceptions and how the mind works to fill them in quickly, sometimes with flawed data. However, gaps in visual data can be filled at a more deliberate pace with observational reasoning. Methods of observational reasoning can be used in spatial and imagery observations and include visual baselining to support object recall, visual interpolation, and visual extrapolation. 5.2.5.1  Visual Baseline

To visually baseline an object or entity is to conduct a large quantity of highquality observations of the same or similar entities or phenomena. This creates a baseline of the entity in the practitioners’ minds, which improves their ability to conduct spatial reasoning practices such as mental rotation, mental construction, and object recall. After establishing a visual baseline, practitioners can more easily rotate an entity’s orientation in their mind’s eye, mentally construct a representative picture of the entity in different contexts (such as part of a process flow), conduct object recall when comparing new observations to the baseline, and more easily recognize deviations and change from established visual patterns.4 To create a visual baseline, practitioners should observe an entity from dozens of angles in hundreds of cases over long periods of time. 4. For example, if practitioners observe a Russian T-72 tank over 1,000 times from varying perspectives on aerial imagery, they are more likely to be able to recognize that piece of equipment the next time it appears on imagery.



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5.2.5.2  Visual Interpolation

When a portion of information is missing, obscured, or partially obscured within a visual dataset, use visual interpolation to mentally construct the missing portion’s attributes. Figure 5.12 provides an example of a spatial observation using visual interpolation. Some other examples are: • While conducting imagery analysis, an intelligence analyst uses visual interpolation to reason about the presence of a vessel berthed at a quay. Figure 5.13 provides an example of an imagery observation using visual interpolation.5 • While conducting visual analysis, a surveillance team uses visual interpolation to reason that a fence line is contiguous despite an object obscuring a portion in the middle. • While conducting spatial analysis, a criminal analyst uses visual interpolation to reason about a dataset that tracks a vehicle’s movement and renders visual information at irregular intervals. The observer must reason about where else within the dataset the vehicle was present. 5.2.5.3  Visual Extrapolation

When a portion of information is missing, obscured, or partially obscured because it is outside of the visual dataset, use visual extrapolation to mentally

Figure 5.12  Example of a spatial observation using visual interpolation [1].

5. Satellite image source: Maxar, May 3, 2020, Catalog ID: 1020010091DBE100.

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Figure 5.13  Example of an instance where a practitioner can use visual interpolation to infer the presence of another vessel obscured by clouds [12].

construct the missing information’s attributes. Figure 5.14 provides an example of a spatial observation using visual extrapolation. Some other examples are: • Watchmen on a ship observing an iceberg visually extrapolate the undersea portion of the iceberg to avoid it. • Municipal workers conducting observations visually extrapolate the underground roots of a tree using knowledge of tree variety and the size, shape, and orientation of the above-ground trunk.

Figure 5.14  Example of a spatial observation using visual extrapolation [1].



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• While conducting imagery analysis, a practitioner visually extrapolates the continuation of a road and/or military equipment that extends beyond the edge of an image or data that extends beyond the observable datasets. Figure 5.15 shows an imagery use case for visual extrapolation to assess road continuations or the total strength of a military unit. Further, one can also use aspects of spatial reasoning in tandem with observational reasoning. For example, practitioners can pair short-term observations of an entity’s change over time with mental construction to reason about the long-term effects of time on an entity, such as observing the effects of weather over time on natural features such as shorelines and vegetation. Additionally, a practitioner conducting observations of an object on imagery could use mental rotation to spin an entity’s orientation to make it more favorable for conducting further observations of the entity. The practitioner may then use physical rotation as an extension of mental rotation by orienting a map or image so that it is in the most favorable position for exploitation. Examples of physical rotation of geospatial data include the following: • Imagery or video: When exploiting imagery or video, usually the most beneficial orientation is up, so that the object is depicted in an upright position on the Earth’s surface. Use larger (and taller) objects and shadows (if available) to determine which direction is up.

Figure 5.15  An imagery use case for visual extrapolation to assess road continuations or the total strength of a military unit [13].

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• Map for strategic understanding: When using a map for strategic understanding, spin the map so that it is oriented to North. This will afford the observer the best understanding of the area of study. • Map for navigation: When using the map during navigation, spin the orientation of the map to face the direction of travel. This will orient the navigator so that moving forward is more literally interpreted, and right and left turns come more naturally. Exercise

In Figure 5.16, does one need to make a right or left turn on R Street in order to reach the destination? Was it easier to determine which direction one must turn by mental or physical rotation? 5.2.6  Observational Notations and Communications

Notations of observations are an example of unfinished geospatial communications. This chapter introduces four categories of observation communications that can be practiced in sequential steps: internal, documentation, external, and listening. 5.2.6.1  Internal

Communicate internally to oneself. Practitioners should have an inquisitive mindset that continually questions what one is observing. Initially, do not communicate with others, as it diminishes the cognitive bandwidth integral to one’s visual observation. An internal communication might include saying what you

Figure 5.16  Making a right or left turn on R Street in order to reach the destination [1].



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see, even if it is in one’s own head. An example of an internal communication might be saying to oneself, “I see a large, dark-toned, rectangular object on the corner of 1st Avenue and A Street, and it is nighttime.” 5.2.6.2  Notation and Documentation

Communicate by documenting observations in a hardcopy or softcopy medium that will preserve them. Careful documentation of observations supports follow-on analysis (see Geospatial Observation Principle 11 in Chapter 4). Practitioners should be equally detail-oriented in documentation as they were during slow observations. Creating ordered notes in a spreadsheet and slides with annotations are two of the best ways to document geospatial observations. When creating a spreadsheet of observations, begin with column headers consisting of four elements that will also be featured as principles of geospatial communications in Chapter 8: location, entity, time, and source. Fill in records for each observation and document the location with absolute or relative data, the entity with a description, the time with a date and/or time, and the source with a description, as seen in Figure 5.17.6 Then add any other fields that may be necessary for follow-on analysis. When creating slides, copy the image from the computer screen and add it to PowerPoint. Slide notation can also include the location, time, entity, and sourcing data in text boxes and callouts. Keeping a spreadsheet and slides of observations helps to organize observations for subsequent revisits and for follow-on analysis. Revisiting observations and the eventual ability to communicate them effectively depends on their accurate and organized documentation.

Figure 5.17  Observational documentation is the process of filling in records for each observation and documenting the location with absolute or relative data, the entity with a description, the time with a date and/or time, and the source with a description. 6. Note that, when adding geocoordinates to a spreadsheet, it is best to record latitude and longitude in separate columns.

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5.2.6.3  External

Communicate with colleagues to solicit their perspective; this helps to transform subjective interpretations into more objective observations. When requesting an external perspective, practitioners should not reveal their initial interpretations. Later, when it is time to reveal and communicate observations, it is important that practitioners first gather their thoughts and reference the database or notes to communicate them clearly the first time. Then they should say what they see, yet remain inquisitive of colleagues and mentors, as they often have insights that will help to resolve questions. An example of external observational communications would be to first invite a colleague over for a second set of eyes, then to allow the colleague to observe the item in question and render his or her judgment, and then respond with one’s own initial interpretations. 5.2.6.4  Listening

Complete the communication loop by listening to peer and independent observational feedback. Listening to others can guide observational improvement and also move findings from subjective interpretation to more objective observations. Listening must be as strong a skill as speaking, as one cannot grow if one only listens to one’s own voice. Allow time during the listening session to take others’ perspectives into account, reflect on one’s own potential bias, and maintain a mindset of uncertainty that does not allow for an unhealthy attachment to one’s initial findings. Document the opinions and feedback from others in a database or notes alongside one’s findings. Listening can also be practiced in a metaphorical sense. Some practitioners believe their object of focus has a story to tell, but it requires a form of observational listening in order to truly draw out all of the attribute details. The entity likely has features or indicators that will reveal themselves to those who conduct slow observations and use the Four Cornerstones. For example, consider conducting an observation like a medical examiner or coroner. A coroner’s job is to use observation and other detection tools to absorb attribute details that will answer questions and potentially solve crimes. In doing so, coroners must listen to the deceased in order that they tell their story. 5.2.7  Observation of Process Flows

Most things in the world happen as part of a process. Processes generally refer to repeatable sequences of events that reveal themselves for observation in virtually every area on Earth. A process flow refers to specific, functional sequences of events or actions that create an intended outcome. Examples include functional knowledge of specific industrial, military, and geologic processes, as well as more general knowledge of geography, human behavior, agricultural growth



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patterns, and more. Process flows provide an abstract framework that can help to fill gaps in observations; even when one only observes one step of a process, the steps before and after can be reasonably inferred. In this way, knowledge of process flows contribute to observational reasoning, including visual interpolation and extrapolation. Following are several examples of processes that may improve observational reasoning. The Uranium Fuel Cycle illustrated in Figure 5.18 shows one type of process flow. When one observes a bird with a twig in its beak in the springtime, it is reasonable to assume this is a part of the bird’s process of foraging for nest parts. When observing a delivery person at a neighbor’s front door, it is reasonable to assume that they are part of a logistical process that began with an order and will end with fulfillment. 5.2.8  Observable Keys

Observable keys are documented versions of the mentally stored, visual baseline observations of entities. Keys consist of pictures, illustrations, and charts of the visible features, indicators, and signatures that allow practitioners to identify, recall, and share knowledge of entities. Many career fields have keys of the visual data within their field. An example of observable keys is Audubon’s “Guide to North American Birds” [15]. Referencing various field guides will allow birdwatchers to identify a bird based on small, observed attribute differentiations [15]. Similarly, a military equipment analyst can use keys such as Janes’ equipment identification [16]. Within keys, practitioners will find different types of observables that will help them to identify entities. Figure 5.19 illustrates a

Figure 5.18  An example of process flow: the uranium fuel cycle [14].

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Figure 5.19  A document that could be used as a key for using observable features to identify naval vessels [17].

document that could be used as a key for using observable features to identify naval vessels. Practitioners should discover, develop, categorize, and store the following types of observables: negators, indicators, and signatures. These terms will become increasingly important as one conducts and gathers imagery and naturebased observations and begins to transform them into more useful information via imagery analysis. Additionally, these terms will help to define knowledge limitations and communicate uncertainty in the assessment. 5.2.8.1  Negators

A negator is an observable that rules out or disproves an entity’s identity, circumstance, capability, or starting point in time at a location. The size of a vehicle may negate its capability of traveling on a certain road. A picture or video recording of a person at one time and place negates that person’s presence elsewhere at that time, providing an alibi. Functional identification of a piece of equipment would negate it from conducting any other function, such as a concrete mixer that could not possibly excavate at a construction site. Figure 5.20 shows a vehicle from an overhead perspective or plan view that appears to be operationally capable, but, upon observing it from an elevation view, it is on blocks and without wheels, thus negating its operational capability.



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Figure 5.20  Three types of observables: a negator, indicator, and signature [19–21].

5.2.8.2  Indicators of the Observed

In the world of observation, an indicator is a specific observable or set of observables that imply a broader function or identity of an entity or phenomenon, but falls short of identifying it with certainty. Indicators can be observable features of objects that help to identify it or predict its action. When using an indicator or indicators towards the identification of an entity, it is important to consider uncertainty, the overall strength of that indicator, and what other indicators may result in greater certainty of identification. Examples of an observable indicator are the hyperboloid cooling towers, as seen in Figure 5.20, which may be used primarily for nuclear power plants, but sometimes also for other types of power plants. Another example is wings on a bird. Wings are an indicator of flight, but in rare circumstances they are simply vestiges of the animal’s past. Although indicators are not definitive, they can point research and assessments in the right direction, especially when accompanied by accurate and caveated communications.7 5.2.8.3  Indicators of the Unobserved

Some indicators lead not to identifications of objects but to predictions and assessments of general activity in an area. Such indicators can become a powerful analytic tool and may be the foundation of broader analytic assessments. For example, if the practitioners are scrutinizing a manufacturing complex and they observe smoke or steam emanating from exhaust stacks at the plant, they may 7. As most of the world’s shapes indicate something, practitioners should baseline myriad shapes and build a cognitive and documented library of entity features to expand their object recall ability and provide reference material for future analysis.

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interpret this observation as an indicator that the manufacturing complex may be operational. If practitioners observe nonliteral cell phone vector points on a road, they may hypothesize that the device was in a car that an actor was driving along that road. If practitioners observe military equipment deployed along the border of a foreign nation, they may interpret this as an indicator for an imminent invasion. All of these examples highlight how visual indicators, usually coupled with reason, can direct practitioners towards assessments of broader activities that are often reasoned but not directly observed. 5.2.8.4  Signatures

In the professional trade of geospatial analysis, a signature is a unique observable or grouping of observables that identifies an entity with certainty. Similar to biological signatures such as human DNA and fingerprints, a signature in an image or video reveals an entity’s identity with certainty. For example, in some cases practitioners can observe the wings, engines, fuselage, and tail (WEFT) of an aircraft to discover signature shapes and patterns that reveal its identity with certainty [18]. Using this technique, the observer will find that the Boeing 747 has very large wings with two engines under each, a humped two-level fuselage, and an upside-down T-tail. These indicators combined in one aircraft make up its signature, and practitioners can identify it as a Boeing 747 with certainty. Most aircraft and other vehicles have signatures, and it is important to baseline entities’ signatures so one can quickly identify them in the future. The signature is the gold standard in entity identification, and if objectively confirmed, the practitioner can shift focus to another entity and decide whether that geospatial observation is worthy of notation and further refinement during geospatial analysis. Figure 5.20 shows all three types of observables: a negator, indicator, and signature.

5.3  External Versus Internal Observations SGOT incorporates visual and locational data for geospatial practices, yet these techniques may be applied in other disciplines. Further, while most of this chapter’s content is dedicated to observations of external attributes of an entity, it is important to also consider how observations of internal data could improve understanding. For example, scientists often conduct observations by using a microscope to examine the smallest attributes of an entity, and medical workers use a variety of scanning machines to conduct internal observations of patient’s internal organs in order to diagnose disorders. From the spatial observation perspective, visualizations of layers in a GIS are the external outputs of datasets with internal, often unobserved, attribute tables (discussed in a previous chapter). Observing attribute tables helps the practitioner to understand the internal aspects of external GIS visualizations,



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much the same way that an auto mechanic understands how an engine works beneath the vehicle hood. GIS practitioners should spend time observing attribute tables to understand what is there and what is not. Lead with location by identifying the location fields within the attribute table. Then examine the attribute data fields to see what context they provide, such as time of data collection, location names (i.e., cultural context), possible functional characterization of the location, and source of the data. Finally, examine the records to see if the data is consistent, formatted, and complete. Figure 5.21 shows an example of an attribute table that corresponds to a visualization in a map viewer. When studying entities in a GIS, especially ESRI’s ArcGIS products, practitioners can also access the details of an entity’s attributes in a pop-up. A pop-up is the attribute table behind the symbol on a web map. Pop-ups can be optimized for observation through a configuration that visually prioritizes the most important attribute data. The pop-up can also contain other data and information that can aid observation, such as photographs and links to contextual data and information. Figure 5.22 shows attribute data in a web map’s pop-up. Another type of internal background data is metadata, which is usually source-related information listed within a dataset. For example, digital images contain metadata regarding the location, date, time, size, and other properties of the image. Geospatial metadata types include Content Standard for Digital Geospatial Metadata (CSDGM) and the International Organization for Standardization (ISO) [23]. Observing a dataset’s internal metadata improves understanding of the data itself.

5.4  Tradecraft Examples for Observation Overhead imagery (both satellite and aerial) presents two complementary geospatial tradecraft techniques for change observation: broad area search (BAS)

Figure 5.21  Attribute table that corresponds to points on a map viewer [22].

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Figure 5.22  Attribute data in a web map pop-up [1].

and change-over-time8 [24]. Both techniques are initiated with geospatial observations and then may be transitioned into geospatial analysis. The goal of BAS is to find and identify new locations and entities across a large area. Once a target is identified, the practitioner will conduct change observation by monitoring the newly identified location to observe how it changes over time. When paired, BAS and change-over-time provide a powerful combination for locating and monitoring targets related to national security, threats to public health and safety, environmental concerns, and a host of other issues that can be addressed through imagery-based observations. The following sections offer two tradecraft-related observation examples using overhead imagery. 5.4.1  Imagery-Based BAS

Practitioners can transform data from imagery into geospatial observations during a BAS. An imagery-based BAS is a location-based visual search of reconnaissance imagery over a large area. During such a BAS, an analyst is presented with a certain volume of imagery data to visually search for certain entities, objects, or phenomena. Because a BAS can range from observations to analysis, the observation portion of BAS includes scanning areas and identifying entities. The following best practices are outlined here for practitioners: 1. Search setup: tips, tools, and guides; 2. Starting the search: area, frame, and cadence; 8. For example, NGA’s 2019 Anything as a Service (XaaS) project linked identification of North Korean military facilities with subsequent monitoring for change at these facilities over time [24].



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3. Catching the eye: from data to perception; 4. Attention and scrutiny: from perception to observation; 5. Notation and then back to BAS. 5.4.1.1  Search Setup: Tips and Tools

When starting a visual BAS, it is important to develop a system for accessing and reviewing imagery that can be coordinated with other analysts. In the opensource commercial setting, the most plentiful source of imagery data is satellite imagery, the access of which depends on the different digital platforms of individual companies. While the platforms of most commercial satellite imagery companies are proprietary and only available for a certain cost, there are free, open-source alternatives. Prior to starting a search, the practitioner should develop observable reference keys (documents) of the objects, entities, and/or phenomena for which they are searching to assist with object recall during the search. These references provide the key for identifying significant entities during the BAS and should contain image examples of entities, objects, or phenomena from multiple observational perspectives. These references become observable indicators for use during the BAS. For example, a practitioner is tasked with locating People’s Liberation Army Navy Coastal Defense Force (PLAN CDF) facilities in the People’s Republic of China. Prior to starting a BAS, the practitioner develops a set of reference graphics for use in identifying and characterizing PLAN CDF facilities, including facility features (patterned areas) and key equipment (objects). Although creating reference graphics for a BAS involves some analysis, covered in the next chapters, once certain features and equipment are identified, these keys facilitate the observation process during the BAS. Figure 5.23 demonstrates observable keys of certain patterned areas for identifying PLAN CDF facilities. Figure 5.24 demonstrates observable keys of certain equipment for identifying PLAN CDF facilities. Once the reference document of observable keys is complete, the practitioner may start the BAS. 5.4.1.2  Starting the Search: Area, Eye Altitude, and Framing

To start the BAS, the next steps entail defining the area, organizing view frames, and settling into a viewing cadence. Defining the general search area begins with a broad set of project goals. Then specific search ranges become the purview of analyst experts and are set by regional, national, and functional considerations. Upon establishing general search objectives, practitioners create a target list of entities. A region is designated and national borders are considered for establishing search area boundaries. Then functional characteristics related to the search objectives may narrow the search boundaries further. For example, given this section’s PLAN search example, the practitioner is able to narrow the

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Figure 5.23  Observable keys of certain patterned areas for identifying PLAN CDF facilities [25].

Figure 5.24  Observable keys of certain equipment for identifying PLAN CDF facilities [26].

search area to a region (East Asia) and nation-state (the People’s Republic of China). Based on the functional characteristics of the PLAN (i.e., it is a military branch with facilities near or on coastlines), the practitioner is able to further



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narrow the search area to within some distance, or buffer, within China’s coastal areas. Figure 5.25 shows an example of a buffered search area, outlined in red. Once the general search area boundary is set, the practitioner must establish a proper view frame for searching. This first entails setting large-scale boundaries for the search area, such as publicly available digitized map grids, custom buffer files, or simply manually drawn lines within the practitioner’s search tool or GIS. The next step is to set an “eye altitude,” or zoom level, for the search that balances feature recognition capability of the specific target with area coverage. The specific zoom level should balance the practitioner’s visual preferences with the size of the object of inquiry. For a BAS of a facility, approximately 3,000m of eye-altitude zoom level over a search area is a first approximation for striking this balance. However, for a BAS of cargo trucks, a lower zoom level would be required for recognition of these smaller objects. Then, based on the eye-altitude, it is useful to develop a system for tracking search progress, such as custom view frame boundaries to guide scrolling, or “snail trail” features in an ELT. Once these parameters are established, the search begins. 5.4.1.3  Catching the Eye: Attention, Significance, and Observation

As the search begins, the practitioner’s attention will be both pushed and pulled across scenes as visual data is perceived. To start, the practitioners apply softly focused attention to push their eyes across the scene. Periodically, certain patterns of color, shape, and size will then capture, or pull, attention, resulting in a pause and a shift towards hard focused attention on a specific area. Areas of interest are then compared with the practitioner’s internal object recall and external observable keys. Upon additional scrutiny and focus, the practitioner uses observational reasoning, including mental rotation, mental construction,

Figure 5.25  An example of a buffered search area along the coast of China, outlined in red [27].

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and the Four Cornerstones to match the initial visual perceptions of patterns to visual observable keys. If observations match aspects of established observable keys, including indicators and signatures related to both facilities and equipment, the practitioner decides whether or not this area is significant enough to note it for subsequent interpretation and analysis. 5.4.1.4  Observational Notations for BAS

Because BAS projects usually require visual searches of a large area within a given timeframe, the practitioner will collect observational notations of locations of initial observations for subsequent review, similar to bookmarks. This requires making a notation of entity candidates, either to catalog the candidate as an established observation or for further review at a later time. A common method for recording notations on a GIS is to create a layer of vector points and polygons outlining the area of the entity, especially in the case of a facility candidate identification. Vector layers should contain notations that include certain attribute information such as location, identification of the entity, the time and source of information used for interpretation, and at least a few words of additional context. This attribute information creates a durable record that allows peer review and is a first step towards analysis and ultimately communication. Additionally, the practitioners should create a graphic that documents and communicates location, identification of the entity, the time and source of information, and other context. These notations facilitate subsequent revisitation of observations to further refine their list of candidate entities. As the list of candidates is verified, a variety of patterns will emerge that will provide direction for subsequent geospatial analysis. 5.4.2  Geospatial Change Observation

Geospatial change observation is the use of visualization to detect changes on the Earth’s surface. The object of change observation can be the Earth’s surface itself or animate and inanimate objects and entities on the Earth’s surface. Practitioners can conduct geospatial change observation by using either literal data from imagery or videos or nonliteral data from maps. Observation of change at identified locations deepens one’s understanding of the entity. Satellite and aerial imagery present numerous opportunities for practitioners to observe changes on the Earth’s surface, such as an adversary preparing for a missile launch, a factory undergoing a production cycle, a person or vehicle arriving or departing from a location, or changes in vegetation that warrant further research. Mapping software also presents numerous opportunities for practitioners to observe changes on a single, time-enabled layer using a time slider or by overlaying numerous notation layers with differing dates and turning them on and off.



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Observations of change over time depend on data and availability and include binary changes apparent via imagery analysis, or motion-related change via video analysis. For example, a practitioner observes an empty tract of land on the first image and a building in that same location on the second image. Or imagine a motion picture (a series of images separated by only fractions of seconds) that provides the observer with a more coherent visualization of a parking lot over 3 days. Then imagine that same visualization after removing all but one image per day. The former would require only observation to understand, and the latter would require further scrutiny to analyze and characterize that location and its change over time. The following are two of the change observation practices conducted in the imagery analysis field: change detection and change-over-time. While the terms may sound similar, practitioners refer to change detection or coherent change detection (CCD) as the minute measurements that reveal change, and change-over-time as the larger, more obvious visual changes of entities. 5.4.2.1  CCD

CCD uses analysis of SAR imagery to measure minute changes at a location that cannot be seen by the naked eye. To conduct CCD, there must first be a SAR image baseline of the area of interest. The SAR sensor then conducts a second collection of SAR data of the same area using the same collection parameters. A geoprocessing tool then measures the two images for magnitude differences by using automated technical analysis, and the results are processed into a finished CCD image. A finished amplitude CCD image creates a visualization for the practitioner to observe highlights of the areas of change with differing colors: for example, a red area indicating that objects departed, and a blue area indicating that objects arrived. Simple visual analysis of the resulting image can resolve whether a vehicle arrived or departed from a location. Figure 5.26 shows how CCD can reveal measured changes from left to right. The left image is the

Figure 5.26  CCD revealing measured changes from left to right [28].

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SAR first pass, the middle image is the SAR second pass, and the right image is the CCD image showing magnitude changes in red and blue. 5.4.2.2  Change-over-Time on Imagery

Change-over-time on imagery means conducting observations for basic change at a location using a diverse suite of images and videos. The practitioner may be searching for such simple changes as large vehicle movement (arrive/depart), construction projects (built/not built), military deployments, and environmental changes. Common examples of change-over-time observations include the arrival and departure of equipment at a location; construction projects of new facilities, tunnels, and lines of communication (roads, rails); and deforestation. In order to conduct change-over-time observations, the practitioner sequentially assembles a historical imagery baseline of a location and then conducts subsequent observations of that location using indicators and signatures against this baseline. For example, a practitioner monitoring military base construction would use observation keys and observation of process flow to identify structures, construction equipment, or military equipment that were constructed or delivered compared to the historical baseline of the base. Figure 5.27 shows construction discovered by an analyst conducting observation of change-overtime at a PLAN CDF facility. Once construction is completed, the practitioner can continue to observe change-over-time to show how specific locations relate to each other and to broader social and political activity. Practitioners can also observe change-over-time using satellite images from government sources such as the United States Geological Survey (USGS)

Figure 5.27  Construction discovered by an analyst conducting observation of change-overtime at a PLAN CDF base’s probable underground facility [29].



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and the National Aeronautics and Space Administration (NASA). Figure 5.28 provides an imagery example of vegetation data from Phoenix, Arizona, taken from the NASA Landsat 5 and Landsat 8 satellites. From left to right, the images reveal a decrease in vegetation during that time period. The red color denotes vegetation and an observation of the red dots from left to right reveals vegetation decreasing from 1991 to 2015 as residential and commercial land use increased in the same areas. 5.4.2.3  Change-over-Time on a GIS

Practitioners can also observe change-over-time on a GIS. Point data with temporal attributes can be uploaded into a GIS and then viewed separately or played like a video to show a time lapse. Tools such as time sliders allow practitioners to observe the movement of an entity throughout the day, on a daily basis, monthly, and seasonally. Figure 5.29 shows a time-enabled version that reveals seasonal changes in Baltimore shootings. The heat map timeframe displayed shows shootings in winter. The practitioner can enable the time slider and observe shooting patterns through the four seasons. The heat map will shift areas as the seasons change, giving the practitioner observational details that can be communicated to decision-makers for action. Figure 5.30 shows Baltimore shootings and the changes by season.

5.5  Conclusion As deep fakes, deception, disinformation, and the big data deluge inundate human attention, geospatial analysis practitioners must use certain principles and practices to properly refine data into information. This chapter provides SGOTs for the practitioners to optimize their geospatial observations. Specific SGOTs such as the Four Cornerstones provide a method for systematically extracting more meaning from entities and their related surroundings. Taken together, these practices and techniques allow the practitioner to develop empirical, more objective observations that may then become the basis for subsequent analysis. Although this chapter presents a set of structured techniques for practitioners, developing geospatial observations also involves the art of intuition.

Figure 5.28  An imagery example of vegetation data from Phoenix, Arizona, taken from the NASA Landsat 5 and Landsat 8 satellites [30].

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Figure 5.29  A time-enabled version of the heat map from a previous chapter that reveals seasonal changes in Baltimore shootings [22].

Figure 5.30  Baltimore shootings and the changes by season [22].�

Intuition is a feature that straddles observation and analysis and can play a major role in the observation process. Intuition refers to the art of sensing change, abnormality, or notable things that are not readily apparent to those with less experience. It combines such skills as object recall, soft focus, and change observation, all working in the background of a practitioner’s mind behind the conscious, hard focus of completing tasks. It is part of the art of geospatial analysis that allows some practitioners to see harmony and patterns as part of a broader context, where others may see chaos, disconnect, or nothing at all. Building on this book’s data-to-information refinement themes, this chapter closed by applying SGOTs to two vital geospatial workflows: BAS and change observation. Practitioners can use these workflows in tandem to observe new things and monitor them over time. As significant locations and entities present themself, quality and quantity of geospatial observations will increase, and this breadth of data requires further analytic refinement. To continue that workflow, the practitioners can carry collected observations, interpretations, and identifications for further processing into the next element of the OAC framework: analysis.



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References [1] ESRI, ArcGIS Software with Imagery basemap. [2] U.S. Department of Defense, “Bomb Damage Assessment of Al Sahra Airfield, Iraq,” assessment photos used by Vice Adm. Scott A. Fry, U.S. Navy, director, J-3, Joint Staff and Rear Adm. Thomas R. Wilson, U.S. Navy, director, J-2, Joint Staff, in a Pentagon press briefing on December 18, 1998, https://www.defense.gov/Multimedia/Photos/ igphoto/2002017528/. [3] Shaffer, J., “5 Tips on Designing Colorblind-Friendly Visualizations: Examine the Issue of Using Red and Green Together in Data Visualization,” Tableau, April 20, 2016, https:// www.tableau.com/blog/examining-data-viz-rules-dont-use-red-green-together. [4] McAuliffe, K., “Panchromatic Imaging: Application in Remote Sensing.” ArcGIS StoryMap, https://storymaps.arcgis.com/stories/28a2091d2819476c8c8fac573798e912. [5] RADIOLOGYPICS.COM, “256 Shades of Gray – Explanation of Grayscale,” March 9, 2013, https://radiologypics.com/2013/03/09/256-shades-of-gray/. [6] Ager, T., The Essentials of SAR, Lewes, DE: TomAger LLC, 2022. [7] Kivimaki, V. -P., “Russian State Television Shares Fake Images of MH17 Being Attacked,” Bellingcat, November 14, 2014, https://www.bellingcat.com/news/2014/11/14/ russian-state-television-shares-fake-images-of-mh17-being-attacked/. [8] ESRI. ArcGIS Software Streets basemap, https://pro.arcgis.com/en/pro-app/latest/help/ mapping/map-authoring/author-a-basemap.htm. [9] Pinterest, “Golf Ball Stock Image. Image of Isolated, Ball, Macro – 11940055,” https:// www.pinterest.com/pin/663788432573817068/. [10] Maxar, Satellite image from January 10, 2019, Catalog ID: 1050010013D86800. [11] Magic Eye, “History of the Random Dot Stereogram,” https://www.magiceye.com/faq/. [12] Maxar, Satellite image from May 3, 2020, Catalog ID: 1020010091DBE100. [13] Swan, B. W., and Pi. McLeary, “Satellite Images Show New Russian Military Buildup Near Ukraine,” Politico, November 1, 2021. [14] U.S. Energy Information Administration. “Nuclear Explained: The Nuclear Fuel Cycle,” July 12, 2022, National Energy Education Development Project, Curriculum Guides, “Nuclear.” [15] Audubon, “Guide to North American Birds,” https://www.audubon.org/bird-guide. [16] Janes, “Equipment Intelligence,” https://www.janes.com/capabilities/defence-equipmentintelligence//. [17] RallyPoint Team, “Example of the United States Navy Operational Forces,” RallyPoint, April 27, 2015, https://www.rallypoint.com/answers/how-many-sailors-belong-to-asection-vs-a-division-in-the-u-s-navy. [18] Department of the Army, Visual Aircraft Recognition, Washington, D.C.: Department of the Army, May 2017, https://irp.fas.org/doddir/army/tc3-01-80.pdf.

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[19] Cadbull, “Family Small Car Front Side and Top View Elevation CAD Block Design dwg File,” 2019, https://cadbull.com/detail/135131/Family-small-car-front-side-and-top-view -elevation-cad-block-design-dwg-file. [20] Kisscc0, “Nuclear Power Plant Image,” https://www.kisscc0.com/clipart/nuclear-powerplant-nuclear-reactor-power-station-j0z5wv/. [21] Kisscc0, “Boeing 747 Silhouette,” https://www.kisscc0.com/clipart/airplane-jet-aircraftcomputer-icons-boeing-747-si-li5uan/. [22] ESRI, ArcGIS Software Light Gray Canvas basemap, https://pro.arcgis.com/en/pro-app/ latest/help/mapping/map-authoring/author-a-basemap.htm. [23] Federal Geographic Data Committee, “Content Standard for Digital Geospatial Metadata (CSDGM),” June 1998, https://www.fgdc.gov/standards/projects/metadata/basemetadata/v2_0698.pdf. [24] GISUSER, “AllSource Analysis Wins NGA Contract to Identify and Monitor North Korean Military Facilities,” September 12, 2019, https://gisuser.com/2019/09/allsource-analysiswins-nga-contract-to-identify-and-monitor-north-korean-military-facilities/?fbclid=IwA R0DLppOYGmy0Evd1Asz9D06kT33FN0BEuK22gXSzNF1hUbXw07sYW7JfTg. [25] Maxar, Satellite image from December 18, 2020, Catalog ID: 1040010065B78B00. [26] Tyg728. “File:YJ-62 Anti-Ship Missiles 20170716.jpg,” Wikimedia Commons, July 16, 2017, https://commons.wikimedia.org/wiki/File:YJ-62_Anti-ship_missiles_20170716.jpg. [27] QGIS, https://www.qgis.org/en/site/. [28] IMSAR, “Coherent Change Detection,” 2021, https://www.imsar.com/portfolio/ coherent-change-detection/. [29] Maxar, Satellite images from September 22, 2015, Catalog ID: 10400100125FE900, and January 10, 2019, Catalog ID: 1050010013D86800.. [30] USGS, “Tracking Change over Time: Urban Area Change—Phoenix, AZ,” Teacher Guide, https://pubs.usgs.gov/gip/133/pdf/Phoenix-Teacher_web.pdf.

6 The Geospatial Skill Set: Analysis Principles 6.1  Introduction to Geospatial Analysis Principles Analysis is the engine of the geospatial skill set that includes OAC. Analysis includes the process of refining geospatial observations towards more useful information using visual, technical, and cognitive skills. In this way, analysis represents the next waypoint in the data-to-information refinement process. Analysis is the further scrutiny or processing of information. Geospatial analysis requires visual, cognitive, and, for the first time, technical examination of Earth-referenced entities and locations. Technical examination differentiates geospatial analysis from geospatial observation and provides practitioners with a powerful information edge. It begins when practitioners locate entities of significance via technical measurement of latitude and longitude and then employ in-depth visual, cognitive, and technical examinations of entities in those locations to identify them. Identifications lead to classifications within hierarchical and organizational systems, which then further relate entities in space and time. These identifications and relations then undergo further visual, technical, and cognitive analysis to assess their meaning within a broader context. This chapter defines geospatial analysis, provides an overview of its purpose, introduces some foundational principles, and outlines two complementary fields of study within the field. This provides a foundation for the structured geospatial analysis practices introduced in Chapter 7.

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6.2  Defining Geospatial Analysis Geospatial analysis (the verb) is the practice of combining visual examination with technical tools to interpret Earth-referenced data, observations, and information in the context of space and time. Geospatial analysis (the noun) is the overarching field of study that includes the further scrutiny and examination of geospatial data and information. Such scrutiny generally takes the following form. First, the practitioner manages and integrates resolved observations into a broader research endeavor and collects new observations to address gaps in knowledge. Next, the practitioner combines visual examination with technical tools (such as an ELT or GIS) to further identify and relate these observations, develop insights within the context of the broader research endeavor, and refine the overall quality of the assessment. Finally, the practitioner uses peer review to improve objectivity and achieve the most accurate and concise version of a basis and assessment. The field of geospatial analysis includes the subordinate methodologies of imagery analysis and spatial analysis, introduced at the end of this chapter and detailed in Chapter 7. Each has its own analytic practices or skills, referred to in the industry as tradecraft, that practitioners perform in order to carry out workflows that produce assessments and answers to research questions.

6.3  The Purpose of Geospatial Analysis The purpose of geospatial analysis is to create an assessment, which is an evaluation of something based on evidence (i.e., what is) that may include predictions or forecasts of things to come (i.e., what will be). An assessment is the documented outcome of analysis that represents the analyst’s contribution to knowledge. Assessments are evaluations of entities, events, and phenomena, derived from facts, reasoned estimations, and interpretations. They are the end product of the data-to-information transformation that informs audiences. To create an assessment, geospatial analysis first solves for where by establishing a location and then transforming the data in that place into useful information. Then answers to questions that begin with who, what, why, when, and how contribute to the assessment. Geospatial analysis is propelled by questions such as: • Where is the most likely location to find something or someone? • What are the entities at a location, and what are the spatial and temporal relationships between these and other entities? • What or who was at a location at a specific time? • When did something arrive at a location?



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• Why is this location conducive to a certain activity? • How does an entity’s location reveal a broader context? Creating a geospatial analysis assessment requires attention to certain principles introduced next.

6.4  Foundational Principles of Geospatial Analysis Geospatial analysis is a unique field of study that has its own characteristics. It differs from other types of analysis by prioritizing the powerful combination of locations and visualizations to answer research questions. Its principles remain constant no matter the data type and form a basis both for broader deductive reasoning and for more narrow analytic tradecraft. The following principles will help practitioners to build a solid foundation in geospatial analysis so they can conceptualize the scope of possibilities and capabilities and apply them to relevant practices. 1. Prioritize the location mindset. Everything on Earth has a location and can be identified and understood through this. To start, identify every entity’s location on the grid. 2. Everything is related in space. Tobler’s first law of geography states [1]: “Everything is related to everything else, but near things are more related than distant things.” Location is the anchor from which to understand the spatial relationships between entities. One could also improvise Tobler’s law to end with “in both space and time.” 3. Everything is related in time. Time is a standard by which to measure, compare, identify, and relate entities and locations. The longer an entity dwells at a location, the more related it likely is to that location. This is a geospatial-temporal corollary to Tobler’s law. Further, everything is part of a temporal process that occurs in a particular sequence. Understand the process flow of where things are, where they came from, and where they are going. 4. Everything can be visualized, measured, compared to a reference standard, and identified. Visualization and technical measurements of locations and entities facilitate interpreting data and transforming it into useful information. Visualization of geo-enabled imagery and geo-enriched maps stimulates analysis by helping to relate entities, see distributions, and layer meaning.

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5. Location often provides an identity for people, social units, and political units. For example, states are political units that are identified in terms of their location (i.e., territorial boundaries). Locations also provide cultural markers that help to define groups of people across state boundaries. Further, point locations with specific attribute information can sometimes be proxies for human identities. 6. Everything can be classified in a relational system. A classification system such as the evolutionary tree of life or a military table of organization and equipment is another way that entities can be related, in addition to space and time. 7. Everything happens in a broader context. Locate additional meaning in historical, social, and political contexts. 8. Uncertainty is an essential aspect of analysis. Uncertainty motivates the search for deeper knowledge, provides boundaries for assessments, and helps to define confidence levels. The practitioner should accept uncertainty and use it to frame research agendas. These principles are a starting point for using geospatial analysis to develop strong assessments. They reflect fundamental themes within geospatial analysis: identification, relation, context, and uncertainty. Next we provide additional reflections on these themes. 6.4.1  Identification

The identification of entities at a location is the first in a series of discoveries during imagery analysis, and it is the most important piece of a location’s attribute data during spatial analysis, as it is often what identifies and links a feature class. To identify something is to assess its functional or recognizable name and/ or purpose. Identifying an entity is foundational to all forms of visual and spatial analysis and begins without reference to its broader context. While the act of identifying an entity can be quite difficult, there is an element of simplicity in first assessing the entity alone and aside from the system in which it resides. The identification process entails comparing object characteristics of location, color, shape, and context to reference keys, with the goal of properly naming the object according to established conventions. The practitioner should begin by identifying the location, which entails visualization and/or measurement.1 Once location identification is complete, the practitioner then gath1. Location is central to identification; indeed, location often acts as identity. For example, nation-states are organizations of people identified by their locations as defined by territorial boundaries. Cultures are identified by locations that reveal geography-specific traits, such as South Sudan. The locations of cell phones and vehicle locations are often proxies for human identities.



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ers and organizes attributes of the location, including detailed observations of entities at the location. Using object differentiation, the shape of a single entity is identified. Then further attribute differentiation begins by observing and describing an entity’s color, shape, and shadow, including measuring its length, width, and/or area. Then all of this data is compared using object recall from the brain and external references (such as keys) to establish each entity’s identification, including its function and name. Names for entities can be common, formal, or scientific. The classification of names for entities are made up of levels that include more generalized terms, such as the entity’s family or broader grouping, to more specific terms, such as the name of the entity itself [2, 3].2 As an entity comes into focus and the practitioner moves from describing the attributes of the entity to naming it, one should default to the most general order of classification that can be definitively assigned and allow analysis to guide them down the classification tree towards the specific name as the evidence allows. The following are examples of identifying entities while conducting imagery and spatial analysis: • Spatial example: John Snow’s 1854 cholera map identified and visualized locations of cholera-related deaths in London [4]. • Imagery example: Arthur Lundahl’s National Photographic Interpretation Center (NPIC) team identified Russian medium and intermediaterange missiles in Cuba in 1962 [5]. Identifying or naming an entity begins the process of relating it to other entities, which leads to the next major theme focusing on relations. 6.4.2  Relation

Once a practitioner identifies an entity, understanding its relation, or connection, to other things in space and time follows. An entity’s relations will yield a second-order assessment that builds on the foundational identification and provides more understanding of the broader connections. Relating things in the world allows one to understand an entity within systems; understanding the various ways to classify an item is beneficial to understanding an entity from various perspectives. For example, John Snow’s cholera map identified cholerarelated deaths, related them to each other as clustered locations in London, and then related those clustered locations to neighborhood water pumps [4]. 2. Psychologists have described naming according to three levels: basic (most common), subordinate (most specific), and superordinate (most general or abstract). Barbara Tversky discussed object naming for toddlers according to this three-level approach [2, pp. 36–39]; see also [1].

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In another example, Arthur Lundahl’s imagery analysis team identified Russian medium and intermediate-range missiles in Cuba in 1962 and related them to launch sites, storage sites, troop garrisons, naval resources, and aircraft [5]. Further, classification systems, such as a biological taxonomy or a military’s table of organization and equipment (TOE), serve as maps that transform an individual entity to an interconnected system of related entities. Classifying could apply to newly discovered entities that require entry (naming) into a classification system or could apply to updating previously identified items that already exist within a classification system. In either case, classifying according to a system and then scrutinizing the relationships within that system shifts from analyzing the entity independently to understanding how it is related and interconnected to other things. This, in turn, facilitates the development of numerous observable and unobserved analytic inferences that could contribute to broader assessments.3 Determining relationships builds from identification to include visual and technical measurements and comparisons of entities’ similarities, differences, actions, reactions, and causes and effects. Once relationships are assessed, the next geospatial analytic step is to explore the context in which the identified entity and its related elements exist. 6.4.3  Context

Context provides the background for identification and relationships. Once the practitioner has assessed an entity’s identity and its related elements, the next step is to use geospatial analysis to zoom out and assess the context surrounding the entity. Understanding context requires comparing assessments of identification and relations to collateral explanations of entities and events. The practitioner should be aware that collateral explanations, which may be referenced only to a broad area, or not Earth-referenced at all, may lose some of the empirical and transparent qualities of imagery and spatial data, and thus may introduce more uncertainty. However, connecting identified and related entities to a broader context will yield a third-order assessment that is much more explanatory and robust. For example, John Snow’s cholera map identified outbreaks of cholera in dense clusters in London and related those disease clusters to neighborhood water pumps. These observations were connected to broader explanations of 3. In this way, identification and relation are closely connected, as the name of an entity implies a web of facts and relationships that could be building blocks for broader assessments. For example, identifying an object as a T-55 tank entails a web of related facts: it was made in a factory, it moves at a certain speed, three well-trained military members operate it, and it requires fuel to move via an internal combustion engine, which, in turn, requires a large logistical supply line for support. The observation and identification of the T-55 tank therefore entail both the observed and unobserved webs of entailed logistical relationships.



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methods of disease spread, including prevailing theories of airborne disease transmission. Eventually, Snow’s spatial analysis yielded an assessment that the cholera outbreak was likely water-borne, which, in turn, led to changes in theories of disease spread. Figure 6.1 shows John Snow’s 1854 cholera map. In another example, Arthur Lundahl’s imagery analysis team identified Soviet medium and intermediate-range missiles in Cuba in 1962 and related them to launch sites, storage sites, troop garrisons, naval resources, and aircraft. They compared these specific observations to broader historical precedents, nuclear deterrence strategies, and Soviet leadership statements and concluded that the buildup of Soviet missiles at this location was the first in the Western Hemisphere and constituted a direct threat to the security of the United States. The briefing board in Figure 6.2 contains visually compelling elements of an analytic assessment produced by analysts under the direction of Arthur Lundahl during the Cuban Missile Crisis in 1962 [5]. Mr. Lundahl presented a series of briefing boards to President Kennedy, which helped the leadership of the United States understand the extent of the threat posed by Soviet expansion into Cuba. The briefing boards contained clandestinely collected images, maps, and geospatial analysis that was instrumental in affecting U.S. actions during the crisis. After identifying an entity, relating it to other entities, and understanding its broader context, the practitioner is then ready to frame a robust geospatial assessment. Two assessment frames widely recognized in scientific endeavors, and common in geospatial analysis, are the hypothesis and the thesis.4 Both are reason-based statements that explain something, while also leaving open the possibility for uncertainty that calls for corroboration, testing, and ongoing critical review. 6.4.4  Uncertainty

Uncertainty, an important principle in Chapter 4, animates identification, relation, and contextualization. Uncertainty offers opportunities for practitioners to build trust with an audience and to frame subsequent areas of inquiry. To take advantage of these opportunities, the practitioner must embrace uncertainty by openly acknowledging the limits of their assessment, that is, what they do not know and cannot yet identify, relate, or contextualize. One primary method 4. A hypothesis is a preliminary, reason-based assessment that frames an issue on the basis of one or more observations. It is systematically tested and falsified through self-review or peer review in order to improve it. A thesis is an advanced, reason-based assessment built on a more proven basis of observations, peer review, and test results. As it develops, it is also tested, augmented, and/or falsified during self-review and peer review. It is subject to change, but less so than a hypothesis, as it has developed over a longer period and withstood substantial testing. Thesis and assessment are overlapping concepts, but we use thesis to denote only broad summary statements of research outcomes and use assessment across a range of outcomes, including observational identifications, relations, and broader contextual statements.

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Figure 6.1  John Snow’s 1854 cholera map identified locations of disease-related deaths and identified it as cholera in dense clusters in London [6].

Figure 6.2  Briefing boards that contain elements of a geospatial analytic assessment produced by NPIC analysts under the direction of Arthur Lundahl during the Cuban Missile Crisis in 1962 [5].

for embracing uncertainty is to clearly communicate limits of knowledge with specific language, discussed in detail in Chapters 8 and 9. This approach builds trust in audiences by suggesting a measure of humility and by including the audience in part of the inquiry. Further, it allows the practitioner to frame subsequent areas of research for the audience, providing a map of next steps to address new questions raised by the practitioner’s assessment. Practitioners should resist temptations to reflexively explain away areas of uncertainty in their assessments. This is usually based on a false sense that the practitioners must know all the answers on a topic that they have researched. For example, the concept of denial and deception is sometimes employed in geospatial analysis to provide a veneer of explanation for uncertain aspects of an



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assessment. Denial refers to any measures taken to deny collection of information about an issue/actor (often through exploiting gaps in data collection windows (e.g., satellite imagery collection times)), and deception refers to measures taken to intentionally mislead through promotion of false information during data collection (e.g., setting out fake equipment during satellite imaging windows to mislead assessments of military capability) [7]. Given their complexity, establishing denial and deception within an assessment requires a foundation of extensive supporting evidence.5 Without this foundation, the practitioner must guard against the notion that denial and deception may easily be invoked to explain either a lack of expected observation (denial) or observations that may contradict analyst expectations (deception). In summary, while these and other concepts should be considered as possible explanations for unknown or contradictory information, the practitioner’s default position should begin with accepting general uncertainty in aspects of their assessment as they continually work to gather and assess additional data. Finally, uncertainty should not be confused with confidence. While uncertainty should be the default mental condition that acknowledges the perennial existence of gaps in information, confidence describes a mental condition derived from the accumulation of (mostly) objective building blocks towards knowledge. Confidence, and the use of confidence levels in analysis and communication, will be addressed in the following chapters. Having established the purpose and the principles of geospatial analysis, this chapter concludes with a broad outline of the geospatial analytic methodologies of imagery and spatial analysis. Sections 6.5 and 6.6 summarize geospatial analysis methodologies to provide the practitioner with context for Chapter 7’s overview of imagery and spatial analysis tradecraft and structured geospatial analysis techniques.

6.5  Geospatial Analytic Methodologies This section introduces geospatial analytic methodologies as a prelude to Chapter 7’s overview of geospatial analysis as a professional tradecraft. A methodology is a system of principles and procedures used within a discipline or field of study. Geospatial analysis is an overarching field of study that contains many subordinate methodologies, including imagery analysis and spatial analysis. It is important to be aware of the other related methodologies that may add value to their research endeavor. Further, each methodology within geospatial analysis has its own specific practices, or tradecraft. The selection of imagery analysis, spatial analysis, a combination of the two, or an additional related methodol5. Further, denial and deception practices vary by locality (especially state to state), and so location remains central to careful assessments of how these concepts may affect observations.

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ogy often depends on the research question, available types of geospatial data, and any collected geospatial observations that require further examination. In general, practitioners conduct imagery analysis when the primary data source is raster imagery or literal pictures of entities in the natural world and conduct spatial analysis when the primary data source is a tabular dataset transformed into vector layers then observed on a GIS. The following sections explain each in more depth. 6.5.1  Imagery Analysis

Imagery analysis is the scrutiny of visual, literal representations of objects in photographs and video with the goal of resolving an entity and deriving new insights. Imagery analysis is a form of visual analysis that entails seeing an entity, whether in nature or on an image, and attempting to identify it, relate it to its surroundings and location, and understand it in a broader context under conditions of uncertainty. Examples of imagery analysis include examining photographs taken by cameras, images taken by satellites, and even recordings and stills captured on video in order to identify entities and understand them. While imagery analysis requires no specific technical skill at the entry level, some people possess a better aptitude for processing visual information than others. In addition to individual aptitudes, the practice of imagery analysis further requires a keen eye, slow observations, spatial orientation, attention to detail, tradecraft and technology training, and years of experience. The advent and ubiquity of videos have expanded the domain of imagery analysis to include video analysis. Video analysis is the further scrutiny of visual, literal representations of objects in motion with the goal of deriving new insights or conclusions. In particular, video analysis facilitates pattern-oflife analysis that relates entities to each other and to certain locations. Video recording devices include cell phones, security cameras, drones, body cameras, and a host of other devices that have become commonplace in the Information Age. Analysis of video requires many of the same tradecraft elements as imagery analysis, with some additional, unique elements. Image and video analysis is susceptible to certain pitfalls that may lead the untrained eye to quick, reactive interpretations that could prove over time to be incorrect. The well-trained, patient practitioner of imagery analysis uses slow thinking, attention to detail, and reference keys to navigate these pitfalls. Because humans prioritize visual data and images are ubiquitous on social and mainstream media, our ability to understand images and videos seems innate. For these reasons, people may put little effort into careful image and video interpretation. However, errors such as photographic anomalies and manipulation are widespread in social and print media, and quick interpretations of those images and videos are commonplace. It is therefore the domain of the



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geospatial analyst to provide clear, structured interpretations of images and videos to the untrained public eye.6 6.5.2  Spatial Analysis

Spatial analysis is the process of examining the locations, attributes, and relationships of features on maps in order to address a question or gain useful knowledge [8]. Spatial analysis uses overlay, measurement, geoprocessing tools, and visual analysis to help practitioners to understand locations and events, relate entities, detect and quantify patterns, find the most suitable locations and paths, and predict events. An example of spatial analysis is examining a dataset transformed into a layer of nonliteral representations on a GIS and determining where they are, how they are related, and how densely they are clustered. Spatial analysis requires some technical skill at the outset that relies on software and conceptual training. Training in spatial analysis is widely available in academic settings and online. Spatial analysis, especially as it relates to mapping, is also susceptible to certain pitfalls. For example, maps that contain points, lines, and areas can seem highly accurate to the untrained observer. However, the Earth is a living, everchanging system that often evolves quicker than maps can reflect. Additionally, maps use projections that affect how size and distance are visually presented, and further, a map’s underlying data may not be as accurate as it appears. Spatial analysis errors are not as readily apparent in social and print media, but are also frequent due to data, processing, display, and visualization errors. It is the job of the spatial analyst to understand the underlying data, projection, and visualization such that the most accurate portrayal of the information is reflected, including all of the proper caveats.

6.6  Conclusion The principles of geospatial analysis are firmly entrenched in geography, psychology, and philosophy. The foundations include elements of space, time, visualization, measurement, identity, relationships, context, and uncertainty. These principles guide practices of geospatial analysis that are reshaping organizations and agencies under the priority of leading with location. Now a new generation of location and visual-minded practitioners is transforming yesterday’s principles such as “everything is somewhere” and “everything is related” into practiceoriented approaches such as “unite the grids to locate everything” and “location 6. For example, see Amy Sherman, “Viral Images of Border Patrol on Horses and Haitian Migrants Have Sparked Outrage. Here’s What We Know,” Poynter, September 28, 2021, https:// www.poynter.org/fact-checking/2021/border-patrol-haitian-immigrants-whip-horses-factcheck.

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is identity.” With these sound principles as guidelines, the practitioner can now engage with the practices of geospatial analysis found in Chapter 7.

References [1] Tobler, W. R., “A Computer Movie Simulating Urban Growth in the Detroit Region,” Economic Geography, Vol. 46, Supplement: Proceedings, International Geographical Union, Commission on Quantitative Methods, June 1970, pp. 234–240, www.jstor.org/ stable/143141?origin=JSTOR-pdf. [2] Tversky, B., Mind in Motion: How Actions Shape Thought, New York: Basic Books, 2019. [3] Brown, R., “How Shall a Thing Be Called?” Psychological Review, Vol. 65, No. 1, 1958. [4] Tulchinsky, T., “John Snow, Cholera, the Broad Street Pump; Waterborne Diseases Then and Now,” Case Studies in Publica Health, March 30, 2018, https://www.ncbi.nlm.nih. gov/pmc/articles/PMC7150208/. Accessed December 12, 2022. [5] National Geospatial-Intelligence Agency, “13 Days Over Cuba: The Role of the Intelligence Community in the Cuban Missile Crisis,” October 2022, https://www.nga.mil/ history/Cuban_Missile_Crisis.html. [6] National Geographic Society, “Mapping a London Epidemic,” National Geographic, https://www.nationalgeographic.org/activity/mapping-london-epidemic/. [7] Lowenthal, M., Intelligence: From Secrets to Policy, Washington, D.C.: CQ Press, 2009, p. 79. [8] ESRI GIS Dictionary, “Spatial Analysis,” https://support.esri.com/en-us/gis-dictionary/ spatial-analysis.

7 The Skill Set: Geospatial Analysis Practices 7.1  Introduction to Geospatial Analysis Practices Geospatial analysis practices are specific techniques for transforming geospatial observations and other data into geospatial assessments. Geospatial analysis continues two important transformations introduced in earlier chapters: data into information and subjective observations into more objective geospatial assessments. The first data-to-information transformation combines visual and technical examinations with absolute locations. The second transformation occurs when subjective observations and analytic judgments are exposed to peer review in order to improve quality and objectivity. The more people with varying levels of experience review the material, the more objective the final assessment. This chapter introduces geospatial analysis as a trade characterized by specialized practices and structured techniques. It begins with a summary of imagery and spatial analysis practices, referred to as tradecraft, which are two main subcategories within geospatial analysis as it is professionally practiced. Then this chapter presents structured geospatial analysis techniques, which constitute the most important practices that more generally span imagery and spatial analysis. Throughout, this chapter provides text and graphic examples of geospatial analysis that illustrate these practices.

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7.2  Geospatial Analysis as a Profession: Imagery and Spatial Analysis Tradecraft This section introduces geospatial analysis as a professional trade comprising specific skills and practices, referred to here as tradecraft. A trade refers to any skilled job requiring training and experience. Tradecraft refers to the specific skills and practices required to work in a given job or trade, often a mix of explicit training and tacit knowledge derived from on-the-job mentorship and experience. Geospatial analytic tradecraft is the technical practices within the field used to execute geospatial analysis research and product creation. Tradecraft can range from manual techniques to computational geoprocessing. It can also range from an art consisting of tacit knowledge taught by senior analysts over years of mentorship, to a science made up of explicit knowledge that can be learned in a classroom over the course of a semester. Some skills are more foundational and ubiquitous across geospatial career fields, and others are more specific to intelligence tradecraft and can be quite extensive, complex, and prone to change. Sections 7.2.2 through 7.2.4 provide an overview of imagery and spatial analysis tradecraft. Although spatial analysis tradecraft has a robust foundation in geography and a large body of literature supporting its various analytic practices, imagery analysis does not. Imagery analysis tradecraft has been passed down from senior to junior analyst in some professional environments, taught in some classrooms to limited audiences, insubstantially written in books, and insufficiently demonstrated on the internet. Because imagery analysis is a younger field with less academic foundation, analysts rely heavily on innate spatial and visual skills, attention to detail, and mentorship of geospatial tradecraft through tacit knowledge transfer. 7.2.1  Imagery Analysis Tradecraft

Imagery analysis tradecraft is a set of practices for systematically exploiting any image in order to derive details that can form the basis of a geospatial analysis assessment. Exploitation refers to any process for visualizing and manipulating an image to enable these practices. This chapter focuses on practices for assessing georeferenced imagery, that is, imagery linked to geocoordinates, especially satellite imagery. Imagery analysis entails addressing the following questions, based on the geospatial analysis principles from Chapter 6: • Location: Where is the entity and what is the significance of its location? • Time: What did the entity do in the past and what will it do in the future?



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• Identification: What is the entity and how would one classify it? What tools will allow one to better dissect and understand the entity and its relations and purpose? • Relations: To what other entities are this entity related? How are they related? • Context: What is the broader context in which this entity operates? • Uncertainty: What do we know, how well do we know it, and what remains unknown about observed entities? In general, imagery analysis tradecraft requires spatial orientation, depth perception, mental and physical rotation skills, interpolation and extrapolation skills, object and attribute differentiation, critical thinking and reference skills, an understanding of entities and processes, and a host of other skills and training. More specifically, imagery analysis tradecraft entails visual practices related to initial observations of entities within an image, technical practices related to using special tools to manipulate and measure images, and target specific practices related to the functional characteristics of entities found within images. The following is an overview of each of these practices.� 7.2.1.1  Visual Practices

Visual practices for imagery analysis tradecraft refer to the initial observation techniques that the practitioner employs to conduct observations on imagery, such as the Four Cornerstones, the target method, and other SGOTs introduced in Chapter 5. These structured practices are important because changes in light and look angles have a great effect on how humans visually observe entities. The Four Cornerstones provide a step-by-step approach for interpreting the attributes of an entity, including its location, color, (relative) size, and shape. This facilitates object and attribute differentiation and the other human interpretive methods. Then, to discover context, the practitioner uses the target method by placing points on a visual horizontal plane to organize searches in concentric circular areas around those points. The practitioner must further consider the three types of visual observables outlined in previous chapters: negators, which rule out, disprove, or establish a starting point in time; indicators, which strongly imply; and signatures, which identify with certainty. Together, this helps a practitioner to identify an entity, relate it to other entities and issues, and assess its change over time (change analysis is introduced later). During visual practices, the practitioner will need to further consider technical practices,

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including entity measurement, comparison with other images, integration of contextual reporting, and then additional collection via other sensors.1 7.2.1.2  Technical Practices

Technical practices for imagery analysis tradecraft require technical knowledge to properly exploit images, usually within an ELT or via a specialized online streaming service. Technical tradecraft is oriented to data and software setup and includes loading images chronologically so one can compare locations in a temporal sequence to establish negation and assess change. It also includes methods such as adjusting the brightness and focus, using the pan and zoom functions for moving around an image on the horizontal and vertical planes, orienting the image right side up for detailed visual analysis, and measuring locations, shapes, and distances with mensuration tools. Proper technical practices require certain analytic tools to identify an entity, its surroundings, and its context. Analytic Tools

Analytic tools are the technical items that aid human visual and cognitive capacities in examining entities and locations; they are the software models and applications and man-made instruments that help the practitioner to measure, interpret, and transform data into information. While analytic tools can be used in many of the structured geospatial analysis techniques (SGATs), this section orders them first because of their unique capability to assist analytic workflows in ways that the brain and eye cannot. With this in mind, examination of analytic tools and their benefit to geospatial analysis will be examined first, and the other practices that use them will follow. Analytic tools can be broken into two categories: spatial analytic tools and imagery analytic tools. Spatial Analysis Tools

Spatial analysis tools, also referred to as geoprocessing tools, are the technical tools available on a GIS (and some ELTs) that allow the practitioner to visualize, measure, and query nonliteral spatial data. These tools can range from simple to advanced and can be standard or custom. There are hundreds of geoprocessing tools standardly available on a GIS, and practitioners can build custom geoprocessing tools by building models that combine tools and processes. Us1. Visual practices also include stereo analysis of geospatial data, as visualization of data in three dimensions improves one’s ability to understand a target. Viewing entities in stereo allows imagery analysts to gain a 3-D perspective that may lead to new insights about the target. Stereo analysis provides practitioners with additional height, shape, and depth perception, allowing the analysts to use their mental rotation skills to see changes in terrain and target viewsheds more clearly. The tradecraft of stereo exploitation ranges from narrow collection requirements to software tools, imagery overlay parameters, and interpretation mentorship.



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ing spatial analytic tools directly affects how the practitioners are able to assess spatial data and, as such, their effective usage is fundamental to proper spatial analysis tradecraft. The most foundational tools on a GIS are those used for adding nonliteral and literal datasets as visible layers, layer navigation, coordinate reference system queries and adjustments, and saving datasets in different formats. Further, as described in Section 7.2.2, foundational spatial analysis (or geoprocessing) tools in a GIS include those that use dataset layers to create buffers, aggregate points in an area, create heat maps, and summarize areas in different and customizable ways. Imagery Analysis Tools

Imagery analysis tools are the technical tools available on an ELT (and some GISs) that allow the practitioner to navigate through an image, manipulate characteristics of an image, and measure entities within an image. Tools to navigate an image and manipulate image characteristics are often referred to as exploitation tools, and such tools include those related to zoom adjustment, movement of an image within a viewer, and raster value range adjustment such as contrast and brightness controls. Using exploitation tools directly affects what practitioners are able to observe within an image, and, as such, their effective usage is fundamental to proper imagery analysis tradecraft. Tools to measure within an ELT are often referred to as mensuration tools and include those related to measuring points, distance, height, and area on imagery. Point tools measure a geocoordinate in the imagery and can place a point on an image that displays a geographic coordinate (thereby bookmarking that location on the geographic grid). Distance tools can measure accurate distance lines across the Earth’s surface in various measurement types. Height tools can measure the height of walls, buildings, cars, and other features on the Earth’s surface. Area tools can measure the area of a polygon such as a property line, an impact crater, or a neighborhood boundary. Taken together, mensuration tools provide quantitative data that can greatly assist practitioners working on tactical target packages for law enforcement or military operations, can help to provide data about the effects of natural events such as floods and fires, and can provide a rough order of magnitude of a facility’s functional capacity. 7.2.1.3  Target-Specific Practices

Imagery analysis is usually conducted with a focus on individual point targets (such as a facility or piece of equipment), lines (such as roads), or areas (such as when conducting a search). Target refers to a specific location (if known), or a specific desired entity observation (if searching). Target-specific practices for imagery analysis tradecraft refer to knowledge that can only be amassed through

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imagery analysis experience on a target, including functional and regional expertise applied to specific locations. Target-specific imagery analysis tradecraft may be organized into three categories: point targets, lines of communication, and areas. These categories are derived from the three types of vector data found in spatial analysis: points, lines, and polygons. Additionally, point target analysis practices include additional reference to shadow analysis and change analysis. The following are examples of target-specific imagery analysis tradecraft in each of these categories. Point Target Analysis Practices

Point target analysis is a process in which practitioners visualize, measure, and interpret a single entity that resides at a fixed point. Point target analysis tradecraft begins by finding a significant entity on an image, either through discovery during a search (e.g., Chapter 5’s BAS example) or by referencing the geocoordinates of a known location of interest. It continues through the application of geospatial analysis principles (Chapter 6) and visual and technical tradecraft. Then the practitioner applies target-specific tradecraft based on the functional characteristics of the entity in question, usually divided into fixed targets such as facilities or targets capable of movement such as equipment. Fixed Point Target: Facility

Facilities are fixed, man-made areas with a specific functional purpose that have a perimeter, internal buildings or structures, and routes for internal navigation.2 Once a facility is constructed, it does not move and may be consistently observed over time as imagery is collected of its location. Imagery analysis tradecraft for a facility entails examining its perimeter boundary, including looking for a continuous wall and/or fence line. This contributes to an overall security assessment, which entails assessing the boundary type, guard positions, entry control points, and other possible access points. Then practitioners zoom in to functionally assess different parts of the facility and consider how these parts may define the facility’s overall identification and purpose. Finally, practitioners identify any key moveable pieces of equipment that may provide further clues about the facility’s function, process flow, and other patterns of life (a concept introduced in Chapter 3) within the facility. Specific facility types require additional imagery analysis tradecraft related to their function, which is reflected in the facility’s internal infrastructure. For example, a People’s Republic of China People’s Liberation Army Navy (PLAN) Coastal Defense Force (CDF) facility has a certain pattern of infrastructure related to its functional characteristics that most other types of facilities do not 2. While a facility location is referenced according to a latitude and longitude point, it also encompasses an area and incorporates lines (i.e., internal routes).



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have. Figure 7.1 shows the identification and functional assessment of a PLAN CDF facility in Yantai, People’s Republic of China, on imagery. This type of facility usually contains man-made underground areas, vehicle sheds for facility-specific equipment, a headquarters area, and certain specialized equipment maintenance building amd areas. These functional characteristics give practitioners specific locations and indicators to observe and analyze within the facility. For example, over time, practitioners will monitor the facility’s vehicle sheds to assess overall levels of activity and patterns of life and to identify any facilityspecific equipment that could improve the overall assessment of the facility. In another example, when examining a familiar chemical manufacturing plant on imagery, target-specific practices include practitioners conducting observations and analysis in a specific order related to the target’s function. First, the practitioners examine the chemical production buildings to see if the plant is active, then parking aprons and security checkpoints to see what new vehicles have arrived or departed, then the chemical tank farm to see if any tanks have been removed or added, and finally all of the other locations, buildings, and features to see if anything appears new, different, or changed in a way that deserves further scrutiny. Analyzing a plant in this order with these elements in mind is a demonstration of the importance of functional knowledge and how it can benefit practitioners in various fields. In this way, practitioners will develop specific imagery analysis practices based on the type of facility that they are observing.

Figure 7.1  The identification and functional assessment of a PLAN CDF facility in Yantai, People’s Republic of China, on imagery [1].

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Moving Point Target: Equipment

Moving point targets refer to man-made equipment that changes locations over time. Because equipment may move, observations of equipment may be intermittent in a given location. Certain types of equipment help to establish functional assessments of an area or facility and may further establish patterns revealing relationships between locations and/or process flow in an area. Imagery analysis tradecraft for equipment entails identification of equipment category and type through a combination of object recall and reference keys. If equipment is observed within a facility, practitioners should identify it and then assess its approximate number, where it is housed, if there is on-site maintenance of it, and how it moves through the facility. Specific equipment types require additional imagery analysis tradecraft related to their function. For example, the People’s Republic of China’s PLAN CDF equipment includes missile transporter erector launcher (TEL) vehicles with a certain dimension and configuration. Figure 7.2 shows YJ-62 TELs on imagery in a blue PLAN color scheme. YJ-62 TELs, a PLAN CDF missile system, are identified by their measurement (length and width) and the configuration of the vehicle’s forward cab, middle crew compartment, and rear three missile canisters. Imagery analysis tradecraft for identifying YJ-62 TELs requires accurate observation keys, imagery resolution high enough to assess the vehicle’s configuration, and careful mensuration. Further, identifying TELs within a facility indicates that the facility contains roads wide enough for TELs

Figure 7.2  YJ-62 TELs on imagery in a blue PLAN color scheme [2].



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to maneuver and suggests that the facility contains vehicle sheds large enough to house them and a motor pool area to maintain them. In another example related to chemical manufacturing, practitioners can use observation practices such as object differentiation to isolate a single chemical container and then attribute differentiation to determine that the container had certain indicators such as pinched ends. Then practitioners can use technical practices such as measurement to determine that the container is 5.5m long and reference keys to determine that the container may contain chlorine. Finally, practitioners can use geospatial reasoning (covered in depth later) to determine that the location and positioning of the container on the main production building apron are indicative of an empty, spent container. In these and other examples, the practitioner will identify and monitor these areas and pieces of equipment over time as they study their equipment target. In this way, practitioners will develop specific imagery analysis tradecraft based on the type of equipment they are observing. Point Target: Shadow Analysis

Shadow analysis is unique to imagery analysis and most often employed against point targets. As defined in Chapter 5, shadow is the dark shape caused by an entity when it is located between light rays and some surface on electro-optical imagery and between a SAR sensor’s directed energy and some surface (usually the ground). Shadows can reveal items that are hidden and even reveal outlines of items when the entity’s real outlines are obscured. Shadows can further contribute to temporal analysis by indicating the time of day and time of year. In this way, careful observation of shadows is another practice to identify and classify an entity. Visual and technical tradecraft can be applied to shadows. The tradecraft of visual analysis of shadows can be used to assess an entity’s location and shape and to conduct relative measurements of the shadow’s dimensions to estimate an entity’s size. Estimating shapes and relative size is possible when other similar objects are in close proximity and are also casting shadows that can provide a practitioner with patterns and relative measurements. For example, Figure 7.3 shows shadows of an overhead streetlight and a probable lightning arrestor.3 The shadow of the overhead streetlight shows the identifying shape of the overhead light arm. The adjacent lightning arrestor shadow is relatively longer and reveals both the entity’s location and its relative size. The tradecraft of technical analysis can also be used to mensurate shadows with measurement tools that drop points at key locations of the shadow. Such technical measurements can be used alongside visual relative measurements for additional data points. Through 3. The probable lightning arrestor is identified based on surrounding context.

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Figure 7.3  Shadows of an overhead streetlight and a probable lightning arrestor [3].

focusing on shadows, practitioners come closer to identifying the entities and understanding their purposes. Point Target: Change Analysis

While assessing change is ubiquitous in most analytic endeavors, assessing change via imagery analysis, sometimes referred to as change detection, refers to assessing complex change over periods of time through structured analysis of satellite imagery. The tradecraft of change detection includes collecting images from multiple sensors, viewing them either overlaid, side by side, or consecutively and conducting visual and technical analysis to detect where and how change occurred. Practitioners should conduct notation of changes in a structured manner, recording time, location, entity, and source of changes observations and then note gaps in imagery data related to observed changes. As scrutiny of the target increases, practitioners should collect additional imagery to narrow these gaps, including consideration of intraday imagery collection. For example, Figure 7.4 shows change over time at an Iranian port. Line of Communication: Relating

Lines of communication refers to lanes of travel and modes of transmission related to human activity, exchanges, or communication that connect and relate points and entities. These include roads, railroad lines, sea lanes, flight paths,



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Figure 7.4  Change over time at an Iranian port [4].

water lines, and electricity transmission lines.4 Understanding these lines of communication is of vital importance because transportation and logistics are integral to most other capabilities. Line-of-communication assessments refer to a method of imagery collection and analytic tradecraft that focuses on power lines, roads, rails, shipping lanes, water lines, and any other features that provide a pathway and/or mode of transmission for vehicles, vessels, aircraft, power, water, commerce, or any other human activities. These pathways are abstractly referred to as lines connecting fixed locations and facilitating communication between them. Line-of-communication analysis therefore examines the physical connections and relations between entities. Assessing lines of communication establishes an essential infrastructure and logistical baseline of information for practitioners. The tradecraft involves scanning in a linear fashion to determine the starting point, waypoints, and the endpoint of the specific line-of-communication feature. The tradecraft also involves interpreting the man-made infrastructure, vehicles or vessels, and any interruptions, changes, or transactions along the way. Imagery analysts use special collection and processing strategies in order to exploit these long, narrow swaths of land and sea. Analysts then use the Four Cornerstones to identify entities and interpret their function, capability, and relations to other entities.

4. For the importance of sea lines of communication, see Stravridis’ Sea Power: The History and Geopolitics of the World’s Oceans.

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Analysis of lines of communication can identify and relate point targets using imagery and spatial analysis. For example, Figure 7.5 shows how imagery analysis of newly graded roads at the People’s Republic of China’s Lop Nor Nuclear Weapons Test Area led to identifying a new probable underground facility linked to a test support area. Careful observation, analysis, and visualization of these newly graded roads or lines of communication relate the newly discovered probable underground facility to previously identified nuclear weapons test support facilities in the testing area. Additionally, the construction of roads indicates that analysts should monitor for vehicles and other equipment traveling to and from these areas along these routes; this type of monitoring informs pattern-of-life assessments that may further relate different fixed locations. Practitioners can also use a GIS to perform these processes using network analysis tools to examine the properties of natural and man-made networks (lines of communication) to understand relationships. Analysis of a different type of line of communication, such as electricity lines, may also yield different insights. For example, Figure 7.6 further shows how combining imagery analysis, geospatial reasoning (see Section 7.3.5), and line-of-communication tradecraft revealed electricity infrastructure upgrades that further link known nuclear weapons test support areas to the newly discovered facility in the East. Construction of electricity infrastructure additionally suggests that this area could become capable of operating overnight (e.g., with lighting) and year-round (e.g., with climate control). Taken together, line-of-communication analysis revealed that newly discovered facilities at Lop Nor are related to historical nuclear weapons test areas via roads that facilitate

Figure 7.5  How imagery analysis of newly graded roads at the People’s Republic of China’s Lop Nor Nuclear Weapons Test Area led to identifying a new probable underground facility linked to a test support area [5].



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Figure 7.6  Combining imagery analysis, geospatial reasoning, and line of communication tradecraft revealed electricity infrastructure upgrades that further link known nuclear weapons test support areas to the newly discovered facility in the east [6].

equipment transfers and electricity infrastructure that support operations requiring a power supply. Areas: Point Target Surroundings and BAS

Areas comprise a point target’s nearby and distant surroundings and require panning and searching on an image in order to properly analyze their complete extent. Area may be categorized as geographic extents surrounding identified point targets and geographies that must be searched for certain new entities. Areas surrounding point targets contain related entities that provide contextual understanding. For example, Figure 7.7 shows the Yantai PLAN CDF facility is located on a peninsula along the People’s Republic of China’s northern coast and is situated within mountainous terrain. This surrounding area provides context related to the function of the facility; as a Coastal Defense Facility tasked with a sensitive national security function, it is located close to the People’s Republic of China’s coastline within mountainous terrain that may offer some physical protection against some forms of surveillance and attack. Search analysis, synonymous with BAS introduced in Chapter 5, requires practitioners to build on the BAS observation practices previously outlined and add further scrutiny.5 The observation practices identified and bookmarked observations that required further scrutiny. Now practitioners must apply the analytic practices of measurement, deeper attribute differentiation, reference, context, and collateral research to those observations. Some examples of search 5. BAS is a practice of methodically scanning certain areas to find an entity or detect every entity within a defined geographic area.

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Figure 7.7  The Yantai PLAN CDF facility is located on a peninsula along the People’s Republic of China’s northern coast, and is situated within mountainous terrain [1].

analysis topics include environmental health, poaching, finding various types of facilities, and finding deployed military units. The results of this analysis could include the creation of a geospatial dataset of vector layer notations or individual graphic visualizations of candidate entities (and frequently both). Chapter 5’s focus on observation techniques provided a practical set of methods and an example of BAS focusing on the People’s Republic of China’s PLAN CDF. This example BAS identified candidate facilities based on an interpretation of objects and facility characteristics related to established observation keys, and practitioners created a vector layer of points and polygons (i.e., vector layer notations) that recorded each candidate facility. BAS analysis then submits recorded observations (vector layer notations and/or imagery graphics) to structured peer review using both spatial and imagery analysis methods for quality and consistency. Spatial analysis reviews entail ensuring that the attribute table is uniform, with all data properly inputted to the correct fields; that the overall geometry of the vector layer is coherent, with no broken or internally inconsistent polygons; and that the projection of features within the vector layer is uniform, and the layer’s projection is appropriate for the geoprocessing requirements for the dataset. Imagery analysis reviews entail researching individual identifications and interpretations through applying structured geospatial analytic techniques, introduced later in this chapter. This analysis should combine the results of geospatial observations (facility characteristics and specialized object identification) with time assessments and collateral research and then apply various structured techniques to further develop and test the understanding of each identified entity. The results of this analysis should then be consolidated into a document that clearly communicates the resulting comprehensive analysis.



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7.2.2  Spatial Analysis Tradecraft

Spatial analysis seeks to assess spatial relationships within large, nonliteral datasets. Nonliteral spatial data may be visualized on a GIS, including the data’s attributes such as location, date, time, entity type, event type, amount, miscellaneous text notes, and other related fields. Spatial analysis tradecraft requires the following practices: data preparation and uploading into a GIS, geocoding and geolocating, and the use of spatial analysis tools. The following is an overview of each of these practices. 7.2.2.1  Data Preparation and Uploading

Data preparation entails formatting geospatial data so that it is accurate and ready for upload to a GIS. This includes the following: • If the data is tabular, format the column headers (field names) so that they are compatible with the GIS software program. • Make sure that each field name is a clear and concise term describing the information in the column. • To whatever extent possible, limit the total characters in field names, as some GIS data formats have specified field character limits. • Eliminate special characters in the field names, except for underscores. • Review the locational data fields for accuracy and decide whether to separate or concatenate latitude and longitude.6 • Review the temporal data field/s for accuracy and decide whether to separate or concatenate dates and times.7 Once review of the attribute data is complete, the next step is to upload the data to a GIS. To upload geospatial data to a GIS, the practitioner must find and select the options in the GIS software tool that allows for the import or upload of data. This could entail executing a function that allows them to browse to the file and upload or could involve connecting a dataset’s saved folder to the GIS. Some web mapping software tools allow the user to either display the data or geocode the data at this step, while other desktop GIS software offers geocoding services as a separate step. Some GIS allow the user to simply drag and drop the dataset onto the map viewer.

6. If street addresses, validate them manually by copying and pasting it into a search engine, or by using an address validation software tool. If geographic coordinates, enter them into a mapping tool to validate their overall accuracy. 7. If one plans on enabling the time slider and viewing data in increments smaller than a single day (hours, minutes), one must concatenate those fields.

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7.2.2.2  Geocoding and Geolocation

Geocoding is a spatial analysis practice that transforms relative or cultural features such as street addresses into absolute, geographic grid-referenced features such as latitudes and longitudes (often referred to as XY data). Geocoding makes points more precise and more durable and creates a permanent record of points in one’s data holdings, saving speed and time in future use cases. Geocoding may be done manually, such as through a mapping application that allows practitioners to add points that are then automatically assigned a latitude and longitude (geocoordinate). For larger datasets in tables that contain street addresses, batch geocoding via specialized tools and applications may be preferable. Some tabular datasets already contain geocoordinates and can be directly uploaded into a GIS without the need to geocode. This process is called geolocating, which means plotting geocoordinates on the map. Once geolocated, the points can be symbolized and further analyzed. 7.2.2.3  Using Spatial Analysis Tools

Spatial analysis tools, also referred to as geoprocessing tools, are the analytic functions that one employs and executes on datasets to answer questions. There are hundreds of geoprocessing tools available in GIS, and each tool or grouping of tools may have its own implementation practices, including those facilitating summarizing and managing data, finding locations, analyzing patterns, and assessing proximity.8 Practitioners can also build new analytic tools in models that combine a number of analytic tools and processes. The following is an overview of some foundational spatial analysis tools on a GIS: create buffers, aggregate points in an area, create heat maps and hot spot analysis, and summarize areas. Practitioners can also build models and combine geoprocessing tools to create customized spatial analysis tools and workflows. Buffers

A buffer is a circle or polygon created around a feature that allows the practitioner to then observe, measure, or calculate entities or events. For example, one could build buffers around an impact zone to observe or measure the potential damage from an explosion, or around a school to count the number of crimes that happened within a specified proximity. Buffers are a simple analytic tool that provide a bridge to many other analytic tools and results. An example of the use of buffers is introduced in the Summarizing Areas: Building Customized Spatial Analysis Workflows section.

8. These are five categories of geoprocessing tools in ESRI’s ArcGIS Enterprise and ArcGIS Software in version 10.8, 2022, https://enterprise.arcgis.com/en/portal/latest/use/use-analysis-tools.htm.



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Aggregation

An aggregation is a summary of features within a boundary. In spatial analysis, to aggregate points is to run a geoprocessing tool that counts the number of points within an area. One could aggregate the number of invasive species within a park boundary or the number of voters within a congressional district. For example, practitioners are directed to answer a research question about the number of shootings in Baltimore within each police district to help to inform the chief of police’s decision to allocate resources. Practitioners acquire a point dataset with the dates and locations of each shooting in Baltimore.9 Then the practitioners acquire an area dataset with the police district polygons. The practitioners elect to use ArcGIS Software, upload both datasets, and then build the layers in the Content section in the following priority order [7]: 1. The Baltimore Homicides and Shootings points layer (on top); 2. The Baltimore Police District areas layer (in the middle); 3. An image or vector base map (on the bottom). Once uploaded and ordered properly, the practitioners open the attribute tables of each data layer to ensure that all of the data was uploaded properly and all of the fields and records are accurately reflected. Then, in the map viewer, the layers are prepared for optimum visualization by changing to a dark color for contrast. The practitioners adjust the symbology on the area layer by selecting a unique color for each district and then adjusting the transparency to 50% so one can clearly see the points on top of the background colors. The practitioners click one of the points to view and configure the pop-up so it only shows the priority fields. Now that the setup is complete, the practitioners are ready to run the aggregation to answer the research question. Figure 7.8 shows the ArcGIS Software content pane, map viewer, and table for a Baltimore map. The practitioners navigate to the geoprocessing toolbox or analysis button and locate the “aggregate points” tool. The tool allows the users to select the point layer to count and then the area layer that will serve as the polygon and then runs and delivers the result. Once the practitioners name the new layer, the tool runs and produces a result that outlines each area and provides a weighted circle in the center with a pop-up that provides the point count within that area. The numerical answer is also available in the table for the new aggregation layer, and that table can be ordered to show highest to lowest. Figure 7.9 shows an ArcGIS Software aggregation of Baltimore shootings into Baltimore police districts. The practitioners can then stylize the map by transforming the aggregation into a choropleth map. The choropleth map greatly improves data visualization 9. Baltimore datasets were acquired at the Open Baltimore website: https://data.baltimorecity. gov/.

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Figure 7.8  The ArcGIS Software content pane, map viewer, and table for a Baltimore map [8].

Figure 7.9  An ArcGIS Software aggregation of Baltimore shootings into Baltimore police districts [8].

by providing grades of color to represent the count or distribution of shootings within each police district. The choropleth gives the viewer immediate answers to the broadest questions regarding the density of clustering in an area. It should be noted that often choropleth maps visualize normalized data across areas; because areas are of different sizes and populations, visualizing ratios of counts per attribute (such as population, or per capita) provides a standardized visualization. However, some choropleth maps may show raw counts of certain high interest variables, such as homicides, where normalizing the data does not meet the reporting requirement. Figure 7.10 shows a map of a choropleth and



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Figure 7.10  An ArcGIS Software choropleth and a legend that explains how the gradation of color from light to dark visualizes the density of clustering [8].

a legend that explains how the gradation of color from light to dark visualizes the density of clustering. Heat Maps and Hot Spot Analysis

Density of clustering can also be shown in heat maps and by conducting hot spot analysis. Practitioners can transform vector data points into raster data in order to visualize data clusters or concentrations. By creating a heat map, practitioners can visualize the amount of points that overlap or are close to each other. The heat map has become a standard visualization tool for broad analysis of clustering and provides more detail of clustering than the choropleth. Previous chapters introduced heat maps to visualize overlapping point clusters and to see how those clusters changed over time. However, the process of creating a heat map visualization does not showcase the full analytic ability of clustering tools available in GIS. Some research endeavors require the practitioner to delve deeper into the data and measure clusters with mathematical tools to provide a higher-quality statistical analysis using tools that conduct hot spot analysis. The hot spot analysis tool uses the Getis-Ord Gi* statistic, explained in ESRI documentation [9]. Figure 7.11 shows the same dataset as a heat map on the left and a hot spot analysis on the right. The hot spot analysis tool reveals mathematical measurements of the data using spatial analytics and statistics that can provide answers with more specificity and confidence. The red squares are

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Figure 7.11  The same dataset as an ArcGIS Software heat map on the left and a hot spot analysis on the right [10].

hot spots with 99% confidence, with gradations of orange with 95% and 90% confidence, and white areas were not significant. Summarizing Areas: Building Customized Spatial Analysis Workflows

Some spatial analytic workflows and tools can summarize an area, including measurements that help practitioners to focus on smaller, specific target areas. For example, Geospatial Focus Areas (GFA) are a customized spatial workflow using ArcGIS Software that combines a target-specific decision tree with custom spatial analysis tools to provide analysts with a weighted and bounded geospatial target area to search for criminals, when other leads are scarce or nonexistent.10 The GFA workflow requires practitioners to use spatial analysis tradecraft involving geoprocessing tools. Practitioners can either work through these tools one by one, or string them together by building a model. Analysts can use them immediately during the crime spree or shortly thereafter to plot and display the crime scenes and run geoprocessing tools on the locations. The only information that is required is the locations of crimes conducted by the

10. Texas State University hosts a website that describes a similar initial process that they refer to as “geographic profiling.” While many of the overarching ideas of “geospatial focus areas” and “geographic profiling” overlap at the outset, the materials presented on the Texas State website differ in terms of software, geoprocessing tools, order of operations, tradecraft, and end state. Texas State University, School of Criminal Justice. Overview of Geographic Profiling, https:// www.txstate.edu/gii/geographic-profiling/overview.html.



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same person, weapon, or group, usually related through the same modus operandi. The following is an overview of the GFA workflow. The first step requires following some initial rules based on certain assumptions: that criminals follow patterns and either commit crimes nearby where they live (“indigenous”), or commute to areas where they commit crimes (“commuter”). Once the analysts decide whether the criminal is likely a commuter or indigenous, with each category entailing the application of different spatial analytic processes, they apply spatial analysis tradecraft by running a geoprocessing tool available in ArcGIS called Summarize Center and Dispersion [11]. This tool will run geostatistical measurements on the data and create a visualization on the map viewer of an ellipse of 1, 2, or 3 standard deviations, a central feature location (i.e., center of minimum distance of the points), a median center location, and a mean center location. The analysts then transform the central feature, mean, and median of the points into the primary, secondary, and tertiary focus areas by generating half-mile buffers around each. This tradecraft is executed by using another geoprocessing tool called Create Buffers [12]. Once the buffers are created, the analysts change the colors of the buffer areas to red for primary, orange for secondary, and yellow for tertiary. The varying colors create a visual priority cue to the analysts to begin searching databases for criminals that correlate to those areas with similar crimes on their records, vehicles in those areas that may match eyewitness accounts, and areas to direct canvassing and surveillance for further leads. Beyond criminal analysis, this workflow can be used more broadly for calculating the locations of endangered species, retail customers, and vital natural resources. Figure 7.12 shows the use of ArcGIS Software to create geospatial focus areas for identifying locations following a crime spree.

Figure 7.12  The use of ArcGIS Software geospatial focus areas for identifying locations following a crime spree [13].

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7.2.3  Merging Imagery and Spatial Analysis Tradecraft

Many opportunities exist for practitioners to conduct a more holistic version of geospatial analysis that merges the tradecraft of imagery and spatial analysis. Imagery analysis and spatial analysis may have slightly different interpretive starting points, but quickly align towards common goals. Imagery analysis of an entity’s attributes may be the starting point, wherein visual and technical interpretation results in a series of detailed observations. Then practitioners may decide to use a GIS to create vector notations of these observations to organize, visualize, and perform spatial analysis of them, or spatial analysis of a tabular dataset may be the starting point, wherein location is the primary element and all other properties, including observations of entities, are understood as attributes of locations. The practitioners may then decide to conduct imagery analysis to gather specific information about specific locations. For example, the line of communication analysis graphics shown previously in Figures 7.5 and 7.6 show GIS-based visualizations of custom vector data (roads and electricity lines) created via careful imagery analysis. Together, these graphics reveal merged imagery and spatial analysis tradecraft that relate several distinct point target locations to one another via lines of communication as part of a broader assessment. Another example of merged imagery and spatial analysis tradecraft is related to preparation for tactical operations. For example, law enforcement and national security operations often require surprise searches of residential houses as part of an investigation or operation. In such cases, the practitioner must first define the target residential house, then conduct a detailed target assessment via imagery analysis, and finally conduct a drive time and ingress/egress analysis via both imagery and spatial analysis. Conducting a target assessment may include extensive measurement of the height of the building, door, windows, walls, and fences via imagery analysis. It may also include the identification of roof access points, door swing directions, obstacles, and other features that will enable actions at the objective. Conducting ingress/egress analysis may include imagery analysis that shows a broad overview of the area, a suggested route, obstacles, and other relevant positions. It may also contain spatial analysis of drive time and viewsheds that reveal line of sight for friendly or enemy observers and snipers and tessellations, which are custom gridded reference guides (GRGs), that enable orientation and communications during actions at the target. This fusion of the full suite of geospatial analysis creates a complete target package for investigators and operators. This section provided an overview of geospatial analysis as a professional trade comprising specialized imagery and spatial analysis practices, or tradecraft. Section 7.3 provides a list of structured geospatial analysis techniques that span all types of geospatial data analysis and can be flexibly applied to a wide variety of geospatial research endeavors.



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7.3  SGATs SGATs are general practices that help practitioners to more effectively examine, measure, process, and interpret a broad variety of geospatial data and information. Many of these methods are part of the tacit knowledge within professional geospatial analyst communities that has been transmitted between analysts through practical application.11 Several also overlap with the SGOTs in Chapter 5. SGATs are best applied deliberately and are generally listed next in a progression. 1. 2. 3. 4. 5. 6. 7.

Find, link, and layer locations; Analyzing entities using the Four Cornerstones; Analyzing for relationships; Geospatial analytic reasoning; Analysis keys; Analysis for geospatial collection; Analytic communication.

Systematic geospatial analysis starts with finding, linking, and layering locations. 7.3.1  Find, Link, and Layer Locations

Finding, linking, and layering locations are the very first steps in beginning geospatial analysis, and help the practitioners to later conduct more robust identification, relation, and contextualization of entities and locations that will culminate in an assessment. The following is an overview of find locations, link locations, and layer locations. 7.3.1.1  Find Locations

Finding locations entails identifying a location of interest in one’s research and then measuring it on the geographic grid. Finding a location is invaluable for discovering a travel destination, locating a hostage, finding a suitable location for a well, identifying a terrorist training camp, and emplacing a sensor. Practitioners can find locations by a process of either discovery or direction. The discovery of a location may be the result of conducting BAS on imagery and then measuring a location, introduced in an earlier chapter. One might also 11. “Tacit knowledge” refers to knowledge within a group that is not written but rather passed through interpersonal communication (for more on how this concept is applied to other technical communities, see Donald Mackenzie and Graham Spinardi’s “Tacit Knowledge, Weapons Design, and the Uninvention of Nuclear Weapons”).

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be directed to a location on a map from a dataset, a report, an informant, or a supervisor. When prompted by such direction, the steps for finding locations on a map are covered in Section 7.2.2 and involve geocoding addresses or geolocating geographic coordinates. Whatever the case, finding a location puts it on the grid, which creates a bookmark that creates a permanent record. In turn, the bookmark cues practitioners to future research, monitoring, and discovery. The practice of finding locations can be initiated by the simple act of measuring a location on imagery or a map and assigning it a geographic coordinate, finding an entity on imagery that is the subject of a research inquiry and then measuring its location, or by finding a line or area on imagery or a map that is suitable for development. Once practitioners find a location of interest, it becomes a starting point for further inquiry including suitability studies and links to other data and information. 7.3.1.2  Link Locations

Locations may be further identified and related by linking multiple forms of data and information to them. Data and information can be linked to locations along multiple axes including space, time, and activity. Linking in space includes displaying a feature layer on a map with a common symbology to denote their spatial relationship or a road on imagery that links an important feature at point A to point B. Linking in time can be achieved on a map by selecting features at locations that share a common attribute of date and time and on imagery by observing a single image and the objects and events within. Linking locations by activity or attribute, a core practice of pattern-of-life analysis, includes analyzing places on a map or imagery where the same types of events are occurring. Examples of linking locations include similar military operations taking place at different locations, simultaneous bank robberies with the same modus operandi, or a site visit by a human. In these cases, the locations are not related to each other until they are suddenly linked by similar activities. Practitioners use a number of visual and technical methods to link different data and information together via locations. In particular, the practices of linking map-to-image, image-to-map, image-to-image, and description-toimage facilitate deeper identification and relation of locations. Map-to-Image and Image-to-Map

Linking maps and imagery to a location leads to a deeper understanding of that location. Sometimes this starts when practitioners find a location on a map and must observe that point on the imagery of the Earth’s surface to determine what is there and how to best understand it. In this case, the practitioners can first find a location by measuring the point on a GIS (map) to determine the geographic coordinates and then input those coordinates on geo-enabled imagery. Once that location has been matched on imagery, the practitioners can



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conduct observations from numerous look angles and dates and interpret all of the requirements of the research question. At other times, an image is presented that needs to be geolocated on a map for orientation and source verification purposes. Practitioners can compare that image to other images and then link the location to map data through accurate measurements of geographic coordinates. Figure 7.13 is a graphic that shows a map-to-image match that yields additional information about a PLAN CDF base to include a military unit identification number that can be used for additional research. Ground Image-to-Satellite Image

Frequently, practitioners may only have a non-geo-enabled picture of a location of interest, such as a standard ground photograph, image, or other visual information in a media source. Practitioners must then link this information to the location by matching it to other geo-enabled data, such as a satellite image. For example, Figure 7.13 showed how linking satellite imagery to map data of a PLAN CDF base showed a military unit number that, when searched online, revealed an article mentioning that military unit number with a ground image. The next step for the practitioners in this case is to try to match the ground image to satellite imagery of the base.

Figure 7.13  A map-to-image match that yields additional information about a PLAN CDF base, to include a military unit identification number that can be used for additional research [1].

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The tradecraft of ground image-to-satellite image matching includes visual analysis of the picture for specific geographic features that are unique to that location. Then the practitioners try to match these unique geographic features to geo-enabled satellite imagery. This may take multiple attempts, depending on the level of detail that the practitioners have to help to find the location. Figure 7.14 is a graphic that shows how additional research of map information led to a ground image that matches the satellite imagery of the PLAN CDF base, which further corroborates the base’s function and relation to the PLAN organization. Once the location is noted, the practitioners can add the ground image of that location to their mental keys and use this new information to expand their identification of the facility and their relation of that facility to other locations.12 Description-to-Image

Sometimes practitioners have as part of their collected data a written or verbal description of an event or location of interest. For example, in the fields of law enforcement, national security intelligence, and law, witnesses and sources describe things that need to be pinpointed on a map or an image for validation. Matching the description that a source gives with imagery of the Earth can validate whether or not what they are describing is accurate, which can lead to a number of very important follow-on events. The following outlines a summarized process for interviewing a person who is providing a description of an event and/or location of interest. The practitioners should start by identifying common ground, which is either a place to which both the source and the interviewer are oriented or the largest central feature of that area. From common ground, the geospatial debriefer should begin by asking the person to describe how one would get from that commonly understood location, through various waypoints, to the destination. The geospatial debriefer should ask the source to be as detailed as possible and to give details about the environment that can be seen on imagery for corroboration. Once the description is complete, the geospatial debriefer can also elect to ask the participant to sketch or draw the described details on paper. Finally, the practitioners should refer to satellite imagery to match it with the description and sketch and then ask follow-up questions as appropriate. It is 12. Another example of this process could be geolocating a hostage photograph or video. In this case, practitioners must conduct slow observations, the Four Cornerstones, and various SGOTs from Chapter 4 and scour every pixel of the image for clues. Practitioners must compare the interior and exterior features from the hostage picture or video with existing archives of images and videos. Items such as trees, telephone poles, street lights, vehicles, storefronts, and building details may be the only locational clues, so each must be carefully examined with an eye towards identification. Once an indicator or signature from the picture is matched to the same feature on geo-enabled imagery, the location is found and can be linked with other data and information, including maps of the region.



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Figure 7.14  A graphic showing how additional research of map information led to a ground image that matches satellite imagery of the PLAN CDF base, which further corroborates the base’s functional identification and relation to the PLAN organization [1, 2].

important not to lead the witness by showing the source the satellite imagery too soon, but once the practitioners have validated the source’s description and are comfortable that the source is well oriented in the reported environment, the practitioners can show the source the imagery and allow them to provide more fine-tuned details. In the following example, a source describes visiting a house near the corner of 7th and Main Street. Figure 7.15 shows an example of a descriptionto-image match. The following questions will allow practitioners to establish common ground, waypoints, and a destination: 1. What is the largest central feature in Figure 7.15 on the overview image on the left that could be used to establish common ground? 2. How should the geospatial debriefer successfully walk the source through waypoints to the corner of 7th and Main Street? 3. What details could the geospatial debriefer use to tease out the precise location of the destination house?

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Figure 7.15  An example of a description-to-image match in which a source describes visiting a house near the corner of 7th and Main Street [14].

As the practitioners find and link locations, they can also layer this data and information to visualize results and facilitate deeper identification and relationship analysis. 7.3.1.3  Layer by Location

Layering by location, or overlaying, refers to the specific technical methods that practitioners use to gather formerly disparate datasets and observations and visually overlay them with location as the common linking feature. To start, if research is missing location (such as collateral, introduced later), adding location will immediately improve its meaning and link it to other georeferenced information. Next, while conducting imagery or video analysis on an ELT or GIS, a practitioner can visualize a number of layered images that share a location and were taken in rapid succession, or a video. This overlay of images can be further analyzed for details that a single image cannot, such as those related to animate and inanimate object characteristics that are only revealed in motion. During spatial analysis, practitioners can layer multiple datasets and use location as a linking field. This overlays varying features, which allows practitioners to begin more complex visual and technical analysis of the geo-enriched locations, including establishing relations and context. Over time, the more one collects and layers data into a visual environment, the better one can discover, relate, and contextualize. 7.3.2  Analyzing Entities Using the Four Cornerstones

Chapter 4 presented, and Chapter 5 elaborated on, the Four Cornerstones as a method to use location, color, shape, and context as a best practice for identifying entities during observations. Here, the Four Cornerstones are introduced



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as a method to guide analysis of entities and their identifications, relationships, and the contextual components of time and collateral. Upon completion of the location, color, and shape categories of the Four Cornerstones, the practitioner should have answered the questions where, what, and possibly who. Upon completion of the context category, one might be able to answer broader questions such as when, how, and why. 7.3.2.1  Analysis of Locations and Entities: Solving for Where

Analysis of locations and the entities therein is the foundation of geospatial analysis. Once a location is found and established as important for an inquiry, it must be examined and interpreted to extract all of the geologic, geographic, animate, inanimate, and attribute details that help the practitioners towards an assessment. Practitioners can use both visual and technical tools to analyze these factors. The following is an overview of analyzing location according to entities, including the major entity subcategories of humans, vehicles, and buildings. Analysis of entities related to locations includes foundational elements such as terrain, weather, elevation, mobility, suitability, and frequency of use. First, analyze terrain and the effects that it will have on entities in that location, as terrain can severely limit or enable the number of people and vehicles that can populate a location. Next, analyze the climate and weather patterns to assess their effects on the entities at that location. In particular, weather can drastically affect the capabilities and limitations of the people, vehicles, and structures in that location. Next, examine the altitude and how it affects the entities in that location. Elevation affects the way that people and vehicles behave, in addition to temperature, air quality, weather patterns, access to resources, and a number of other factors. Next, consider the mobility of the people and vehicles in the location. The more developed and equipped with hardened roads, bridges, and lines of communication, the more the people and entities can move about and participate in more advanced and complex networks and activities. Next, analyze a location’s suitability by examining to what extent it is capable of housing an entity or hosting an event. Analysis of suitability entails studying the interaction between the entity in question and the local environment. This includes examining other locations where that entity or event has already been observed and comparing that to the parameters of the new location. This suitability study can be conducted on imagery or maps and can range from an environment hospitable for a protected species to a travel route suitable for a large vehicle. Finally, one must analyze the frequency with which an entity is in a location. Using the foundational principles from Chapter 6, an entity is more related to locations in which it spends more time. This includes humans and vehicles, as both tend towards a range of more or less frequented locations. Analyzing the proxies of a person on a GIS can reveal their most frequented locations.

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Analysis of humans related to location is foundational to spatial and imagery analysis tradecraft and requires a broad level of understanding basic human capabilities and limitations. Humans are systems that have requirements of food, water, shelter, and sleep, which act as constraints on human activity. They also usually seek other items such as clothing, goods, and relationships with other humans. Eating, drinking, sleeping, and meeting with others are all activities that one does in locations that practitioners can observe on imagery and maps. Because sleeping usually occupies long periods of time at night and is conducted in a single location, it provides an extended, dormant, and predictable time period when humans create a pattern of behavior that can be exploited and observed. The location where one spends the night is often referred to as a bed-down location, and this location is especially important for national security, military, and police operations. Analysis of humans and the entities in which they operate on imagery, and their proxies and representations on maps, is the best way to visualize and understand their pattern of life, most frequented locations, relationships, and the context in which it all takes place. The more esoteric, target-specific knowledge of human indicators and signatures must be observed and analyzed by each practitioner as the time on target increases. Analysis of vehicles and vessels related to location is foundational to spatial and imagery analysis tradecraft and requires foundational knowledge of vehicle requirements and the constraints that this imposes. Vehicles and vessels require fuel, have a distance range, and expel exhaust fumes that need air circulation. Further, they can only travel within the constraints of the surfaces or maritime environments for which they are rated suitable. Heavier land-based vehicles need strong and stable surfaces to operate effectively. Larger maritime vessels need deeper and wider waterways to accommodate their draft and turning radii. Certain vehicles may require colocation with other vehicles, such as a main battle tank unit reinforced with command, control, and communications vehicles. Some vehicles are indicators or signatures for what other locations are nearby, such as a military base or a police or fire station. Others are indicators or signatures for broader activities of interest, such as a leadership convoy or a missile launch. Practitioners should examine vehicles and their locations on imagery to develop a deeper visual library of object recall and develop keys for identification, relations, and context. Practitioners should also use a GIS to examine datasets of vehicle locations and the vector base maps that reveal road types and standards. The more time that one spends examining the more specific vehicles and their relations to specific locations that make up the study area, the more target-specific knowledge that one will accumulate. Then one can also relate and contextualize them to help to drive towards the assessment. Analysis of buildings related to location is foundational to spatial and imagery analysis tradecraft. The locations of buildings can determine likely nearby influences, zoning, and logistical considerations. Practitioners can assess



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function, capacity, capability, and purpose by examining a building and its surroundings. Buildings require upkeep to remain functional, and practitioners can determine their status based on exterior factors such as lighting, structural integrity, and human activity associated with it. They can often determine whether the building is zoned commercial or residential and what the socioeconomic factors are in the neighborhood that might determine who lives and operates in the area. Although most buildings are linked via roads to other locations, some are more isolated while others are centrally located in hubs that allow for maximum logistical connections. Some smaller buildings like sheds are located within proximity of a larger building. They are often small and open and may house tools, storage, supplies, or vehicles. Other buildings are larger and closed and can house a small family or large gathering. Practitioners can observe buildings on imagery to see literal details and on a GIS to see relative geographic data such as a business name, address, zoning details, and property footprint. Practitioners should analyze all of these conditions to better understand the location and the entity in that location. The more time that practitioners spend examining various buildings and structures, the more target-specific knowledge that they can accumulate. 7.3.2.2  Analysis of Color

Color is one of the first things that most humans see when observing an entity. It is one of the most objective and universally recognizable features of an entity, and analysis of color can help practitioners to interpret an entity’s identity or function. Further, color may be used as an observational feature to build an assessment, including identification of an entity or relating an entity to different locations. Color adds meaning to spatial and temporal analysis; it provides clues to the meaning of cultural features across the globe and to historical eras in the form of clothing fashion, military uniforms, equipment, and more. The color category consists of color and tone, and the visual and technical analysis of color helps practitioners to answer the questions who, what, or sometimes why. Color can indicate poison (red berries), danger (red blood, red sky), or signal for attraction (birds and flowers). It can be the key to classification of entities such as rainbows (ROYGBIV), flora (red roses), fauna (a bluebird), and man-made objects (camouflage main battle tanks). For black and white imagery, low-light situations, and the visually impaired, tonal analysis takes the place of color. Color can also indicate the passage of time, such as the deterioration of a vehicle in the form of rust or the damage to a building when it has experienced a fire. Additionally, factors such as light and camouflage can change the true appearance of color on an entity, requiring slower and more careful analytic tradecraft. Analyzing color requires slow observations, attention to uncertainty and processes, and some technical analysis. Scrutinize the entity and confirm the

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color or tone, define areas of uncertainty in assessing the color, and assess how processes may dictate changes in color. Then, when using an ELT or other software tool to analyze imagery or video, one may be able to modify the color through technical means. If the imagery is in black and white, visual analysis of tone is required. Practitioners can also use technical analysis to measure the strength of colors in the raster cells of certain types of images (such as multispectral) in order to assign it a value and better characterize its classification. In a GIS, color can be visually manipulated or technically analyzed to improve understanding. Color can be important in understanding an entity through symbology, such as a feature class with a shared symbology of purple circles that convey a relationship to the observer. Color also appears in the surrounding and underlying geographic information (blue waterways and yellow/green/ brown topographical landmass). Finally, color appears in a GIS on raster data to signify statistical measurements of pixels that, when aggregated, relay visual meaning that contributes to assessments. For example, heat maps use color to immediately convey density to an observer. 7.3.2.3  Analysis of Shape

Shape is another one of the first things a person observes when encountering a new entity, because the shape of an object often suggests its purpose or its potential to present a threat. The shape category includes size, shadow, and texture and can answer the questions who, what, why, and how. Analysis of the shape of an entity can be achieved with visual or technical measurement. For example, the shape of an entity on imagery facilitates object differentiation, then attribute differentiation, and then classification. The shape of a cluster or density of entities on a GIS may be indicative of multiple events that tip or cue a practitioner towards further research. The following is an overview of how the practitioner may accurately assess shape. The shape of an entity refers to its outer physical contours, which suggests the entity’s classification and/or function. Some shapes can be interpreted using visual analysis, such as the distinctiveness of certain animals or vehicles. When analyzing entities using visual analysis, begin with object differentiation. Once the practitioner has isolated a single object, begin attribute differentiation on that object by choosing the most prominent feature and then working down to the least. It is the most prominent feature or features that can become the indicators or signatures that will help practitioners to identify the entity (see Figure 7.14). Others may require technical analysis including measurements to delineate more specific features that make an entity unique. Shapes of literal objects can often be resolved using analytic keys, guides, an objective perspective, and other reference materials. Shapes of nonliteral data phenomena such as clusters, polygons, and densities are best observed on a GIS and frequently require measurement and other methods of spatial analysis to answer questions about the



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size, shape, and relationships of the distribution (such as a grove of trees). On imagery, one might observe the shape of a specific construction vehicle with a large, barrel-shaped object on it and know that its function is to turn and pour concrete. Animate objects are shaped by evolution for speed, efficiency, durability, utility, and a number of other reasons. Man-made objects are often built for the same reasons, and their shapes often give away their primary purpose. The size of an entity is an essential aspect of its shape. An entity’s size is usually an indication of its capacity, purpose, weight, speed, power, danger, and other factors.13 Practitioners should analyze the size of an entity by measuring them with visualization and technical tools. Visual measurement includes relative measurements performed by the practitioner by comparing the size of an entity to nearby entities of known or standard size. Technical measurement of size involves using mensuration tools to measure an entity’s height, length, width, and volume. For example, measuring the size of a chemical container on imagery can produce precise results that allow the practitioner to successfully identify the contents. On a GIS, one can visually measure distance using relative measures or the map scale bar. One can also use technical measurement in the toolbox to measure distances and areas. For example, measuring the distances between fire stations and fires on a GIS helps supervisors understand the average drive time for which they must plan. Further, measuring the area of a series of land parcels can give a local government an estimate of the land value and how to zone it. The practitioner may also assess shape through analyzing shadows. As introduced in Section 7.2.2.3, analysis of shadows is most often conducted in nature and on imagery and can reveal significant information to the practitioner. Shadows result from entities located between an energy source, usually light, and another object and/or the ground, resulting in a dark outline of the entity.14 Shadow analysis involves the visual and technical measurements to calculate the size and shape. Then further analysis is required to determine the shape, the time of day, the time of year, and other factors that cue practitioners to broader answers and explanations. The shape of the shadow can be compared to memory or keys to help identify the entity. It can also help practitioners to determine the height of a building or wall, obstacles at an objective, whether military items are being hidden, or whether an antenna is in use. Texture is analyzed visually by looking at the roughness or smoothness of an object in nature or on imagery, which may reveal aspects of the entity’s 13. For example, the size of a vehicle determines important factors such as the human, cargo, towing capacity, and terrain and weather capability. Larger vehicles can carry more load and tow more weight, but require more fuel and larger road surfaces. They also require a significant cost, which should factor into analysis of ownership. Smaller vehicles can fit in smaller places but have limits in the aforementioned factors. 14. During daylight, shadows are partially lit due to ongoing light reflection throughout the atmosphere; this sometimes allows for partial vision of objects within a shadow.

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composition. Many man-made objects have smooth surfaces that reflect light at a greater rate than more natural surfaces. For example, metal surfaces on rooftops and vehicles, along with metal objects such as railroad lines, will reflect light at certain angles that can reveal their composition and nature. Natural objects such as flora and fauna tend to have more textured surfaces and are less light/energy-reflective. This is true for radar imagery as well; radar energy is reflected differently based in part on object composition, and practitioners can be trained to interpret this phenomenology. For example, grass will produce reduced, diffuse radar reflections, while metal objects tend to produce strong, clear reflections. Camouflaging these characteristics by wearing ghillie suits or using paint patterns and radar reflective netting entail breaking up unnatural outlines and linear features of man-made objects to blend with natural features and textures. 7.3.2.4  Analysis of Context

Analysis of context connects visual and technical analysis of absolute geospatial data with other sources of peripheral and collateral information. This connects geospatial analysis to a broader perspective and helps to improve an assessment. Analysis of visual context can elevate analysis above the entity to answer broader questions such as why and how and begins by examining entities and events that are outside the target location.15 Analysis of temporal context is gained by analyzing past data of a location and assessing how previous circumstances may help to identify and relate an entity or phenomenon.16 Analysis of collateral is a new category that describes efforts of a practitioner to draw in broader sources of information such as media articles or reports that include the locations, dates and times, and other supporting information that may aid the research effort. As described below, analysis of visual context, temporal context, and collateral may be more subjective than analysis of color, shape, and location and therefore requires deeper analysis and more quality control. Visual Context

Analysis of visual context begins with visualizations of peripheral entities and areas surrounding the target location. Using Tobler’s Law as a premise, deductive reasoning will further guide the analyst to reason which distant and disparate things may be related to the target location. When doing so, remember that 15. For example, on imagery, it is the observation and analysis of the burning oil wells outside of Baghdad, Iraq, while war raged on the city’s streets. On a map, it is the observation of an overall environment of civil unrest as practitioners focus on a specific building that has called for fire service. 16. Temporal context can be analyzed on imagery when looking at the arrival and departure times of a vehicle in the past to see if those times are consistent with current times. It can be analyzed on a map by looking at the symbols of a representation of the same vehicle and the attribute data to calculate its times of arrival and departure.



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nearer entities provide more visual cues that may relate them to one’s focus of effort, but distant entities require more focus to discover attributes that may be related. Nearer entities may require the target method to help catalyze a cascade of analytic depth and context. Distant entities may require a BAS outside of a target area, linking distant nodes or entities to another location via line of communication analysis, matching ground images to add visual context to a location, and other previously described tradecraft methods may assist in analysis of visual context. Temporal Context

Analysis of temporal context is the examination of information related to time that can answer the question when and entails using time to structure observation and analysis of a location in different ways. The practitioners start using time to structure analysis by tracking data collection times (e.g., image acquisition date and time) and the time that an observation was made (e.g., communication or publication date) of a target entity. Then the practitioners should negate the entity, which refers to finding the origin point of an entity in time and space. The practitioner must find the point just prior to when that object first arrived at an area of interest. If the target entity is a facility, negation refers to that time just prior to when the facility was constructed; if it is a piece of movable equipment or a person, then negation refers to that point just prior to the entity’s arrival at a location of interest. Negating an entity is the first step in establishing a timeline for an entity, which then begins a causal explanation related to that entity. After negation, time remains a key factor when observing changes to an entity in multiple images or videos or when viewing a dataset in a GIS using a time slider to show points in motion over time. Then tracking observations of an entity over time may lead to developing an understanding of its patterns of life, which may, in turn, further relate the entity to other locations and entities. Analysis of temporal context includes using time to structure analysis of a location. A timeline is one of the most important, and intuitive, tools that practitioners may use to initiate temporal analysis. A timeline is an organization of entities and events in a temporal sequence and may support assessments ranging from negation to causal arguments.17 Establishing a timeline of events is a common method for organizing otherwise disparate pieces of information and is a common visualization tool in analysis-related disciplines. Practitioners can build notational timelines on paper or on slides, use various software tools, or simply arrange their images in chronological order. For example, imagery ana17. For example, a person’s age represents a point along a timeline of years in sequence. Similarly, a country’s major historical events are often presented in a time sequence (usually from earliest to most current), the arrangement of which may contribute to understanding how important events unfolded.

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lysts often arrange satellite images in a temporal sequence within their ELT to assess change over time. Further, timelines contribute to causal explanations of events, because causality itself is a concept that frames the relationship between events in terms of a sequence.18 Deeper temporal analysis may incorporate such concepts as path dependency for understanding persistent outcomes over time (i.e., lack of change) and critical junctures for understanding the possible effects of sudden disruptions to otherwise path-dependent event sequences.19 .

Collateral

Geospatial analysis can be augmented with collateral, which refers to any related data or information from an outside organization or research endeavor that provides insight and context for the focus of research. Imagery examples of collateral may include published reports that help to identify an entity, relate it to other entities or events, or contextualize its purpose or reason for being at that location. Spatial examples of collateral within a GIS environment may include links to other datasets, databases, blogs, or articles that provide background information. Collateral can also more generally be intelligence reports, websites, databases, media articles, scholarly papers, or any other form of data and information that gives the practitioners a broader perspective about a target location and/or entity. Collateral analysis entails linking outside data, information, or entities with the target location of interest. The practitioner can begin with applying aspects of the link and layer method outlined above. For example: • Link by location: If the data is not currently georeferenced but capable of being so, the practitioner should work to georeference it (geocode, geolocate) and then link that location to other data and information, including other locations. • Layer location: If the data is already georeferenced, the practitioner should simply layer it onto the original data by using the original location as a linking field and thereby relating its attributes to the original entity or focus area (either literally on a GIS, physically with hard evidence, or by using mental construction). • Find a linking field: If the data cannot be georeferenced, the practitioner should move to the next most capable field or attribute, such as time, 18. To say “A causes B” entails that A exists and then causes B to occur afterward. 19. Path dependence is an explanation for how the timing and sequence of events shape historical outcomes. For more on this and other temporal analysis concepts that are applied in the social sciences, see: James Mahoney, “Path Dependence in Historical Sociology”; Paul Pierson, Politics in Time; Giovanni Capoccia and Daniel R. Keleman, “The Study of Critical Junctures: Theory, Narrative, and Counterfactuals in Historical Institutionalism”; and Kathleen Thelen, “Historical Institutionalism in Comparative Politics.”



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that can be linked to the original focus area or entity, and layer using those attributes. Relative geographic data such as place names may also provide a linking field between collateral information and a target location. For example, because most collateral information is referenced (or indexed) according to generic place names (e.g., countries) and functional issues (e.g., terrorism), searching for collateral sometimes requires combining place names and functional terms. First, the practitioners should gather place names according to a hierarchy of echelon areas based on the region or country of interest.20 Then find names for these locations by comparing multiple sources, such as OpenStreetMap, Google Maps, and indigenous mapping services within the target countries.21,22 Second, the practitioner should develop a list of functional terms related to their research topic.23 Then combine searches of these place names and functional terms within a variety of information repositories, from physical libraries to online datasets; use a variety of approaches and combinations; and make sure to filter for descriptions of locations, remote sensing data, images (photographic and digital), and video, as relative visual data of locations may help to georeference collateral sources. Last, once the practitioners have found new information, link and layer it into the research topic as described above. 7.3.3  Analyzing for Relationships

Analysis of relationships between entities and locations can reveal answers to more complex questions such as what, why, and how. Although numerous prior sections have introduced principles and practices that help practitioners to understand the importance of how things are related, this section introduces relationships in more detail to facilitate deeper assessments. The following is an overview describing how entities and locations can be related in space, time, classification, appearance, and measurement and functionally. 7.3.3.1  Related in Space

Analysis of the relations between items in space is foundational to geospatial analysis. The first principle for establishing relationships is Tobler’s Law (entities close to one another in space may share other relationships), which can 20. For the United States, this would be state, county, city, street; for China, province, county/ city/town/village, district (within city), street; and so on. 21. OpenStreetMap is a website that allows users to enter and share details about features on Earth. Its website is www.openstreetmap.org. 22. Google Maps is a website that provides free mapping capabilities. Its website is www.google. com/maps. 23. For example, if the research topic is nuclear weapons testing, then develop a list of associated functional terms such as tunnel, test area, and radiation.

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be applied using visual or technical tools on images and maps. Visual analysis includes observing things that are next to each other on imagery, video, or a map and analyzing them to determine the extent of their relationship. Technical analysis includes using geoprocessing tools to better understand entities’ spatial relationships, often conceptualized in terms of points, lines, and areas (sometimes represented as polygons). Analysis of point relations in space on imagery begins by examining specific facilities and equipment. Because most facilities have a distinct boundary, especially sensitive, secured facilities, then any entities identified within that boundary are related in space.24 Analysis of point relations on a map begins in its simplest form when practitioners examine a layer that shares the same symbology. Practitioners can assume that each symbol representing entities at disparate locations share at least one common feature or field that relates them. Increasing in complexity, practitioners may visualize points with the same symbols clustered at a location and make assumptions about their spatial relationship based on Tobler’s Law. This may raise further questions that prompt the practitioners to employ technical analysis including measuring entities by creating a raster heat map as discussed in Section 7.2.2.3. Finally, a practitioner may increase complexity yet again by overlaying other data layers and querying among the layers to find relationships in their attributes. Analysis of relationships in space is also accomplished by use of lines. Lines have both a measurement and visualization function on ELTs (imagery) and GIS (spatial data). A line can measure the distance and direction between entities and can visually depict a relationship between two entities based on attribute details. In a GIS, practitioners can use analytic tools to generate lines that link, show connections, connect origin to destination, find nearest, and show incident paths and sequences. Practitioners can also use a line tool to create custom vector lines in an ELT to trace lines of communication such as roads and electricity transmission cables, which relate points and areas to one another. Such lines create another durable visual and technical measurement that denotes connections and plainly communicates relationships to future audiences. Practitioners can also use areas to analyze relationships in space. On imagery, practitioners can analyze contiguous and related areas such as forests that house camouflaged military equipment or fenced perimeters that house sensitive manufacturing, storage, or prisons. On a map, practitioners can use visual or technical analysis to group related items into a clustered area or use vector boundaries such as state, county, city, zoning, or voting areas to assume relationships between the people and infrastructure within. Analyzing the relationship between points and areas on a map includes examining when certain people were in certain places using vector data layers that represent a person’s proxy 24. Note that facilities with boundaries may be conceived as both point targets and areas that can be represented as polygons.



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devices and the area where an event occurred, such as the previous example of the Danville homicide. 7.3.3.2  Related in Time

Analyzing relations between items in time is another foundational principle of geospatial analysis that closely follows space. Practitioners can analyze temporal relations using visual or technical tools on images and maps. Visual analysis of relations in time includes observing entities on the same image denoting the same time or on multiple images to observe changes in appearance over time. Technical analysis of the relationship between entities in time begins by conceptualizing principles such as the geospatial temporal corollary (entities more closely related in time may share other relationships) and then continues with time-enabling data on a GIS for the setup, using analytic tools that specialize in space and time, attribute filters or geoprocessing tools on a map to better understand entities’ relationships, and even specialized software dedicated to time analysis. 7.3.3.3  Related by Classification

Entities may be related through functional, biological, or hierarchical classification systems (also referred to as taxonomies). Common classification systems include hierarchical tables of organization and equipment (TO&E), and taxonomic charts of living things. All of these classification charts should also be used as keys by practitioners in order to thoroughly analyze the identification of entities, their relations to others, and the context in which they are functioning or exist. Practitioners can apply entity classifications to their observations and analysis of entities on imagery and maps. On imagery, one can analyze the makeup and purpose of a large military unit deployed with operational and support vehicles, and match each to the TO&E chart. Figure 7.16 provides an example of a People’s Republic of China People’s Liberation Army Navy TO&E chart. For example, accurate identification of specific equipment such as a model of towed howitzer reveals its capability (i.e., where it can operate, how far it can shoot, and what type of round it can fire) and its place on the TO&E chart, which indicates how many pieces of equipment typically operate in a given military unit (assisting observational and analytic interpolation and extrapolation). On a map, a practitioner can analyze a dataset that contains locations and attribute data that displays the hierarchy of an organization using unique symbols. 7.3.3.4  Related by Appearance and Measurement

The more similar entities are in appearance (i.e., color, size, shape, shadow, and texture), the more closely they may be related. Entities that appear the same

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Figure 7.16  An example of a People’s Republic of China People’s Liberation Army Navy Table of Organization and Equipment [15].

are more likely to be closer in classification and therefore may possess similar capabilities and limitations. The more entities differ in appearance, the less they likely share similar attributes and are probably further from one another on the classification table or chart. Analysis of relation by appearance is achieved by using visual and technical analysis on imagery and maps. For example, using visual or technical analysis on imagery, practitioners can visually assess the same signature on various fighter jets, which reveal the same make and model. Using visual or technical analysis on a map, the analyst can observe the same symbol across a data layer and assume that each contains related attributes or use geoprocessing tools that find related features. Many entities are related based on their measurements of length, width, height, volume, and area. Analysis of the measurements can show that similar vehicles, equipment, and land areas can be used for similar functions, which can drive capability and suitability assessments. Visual and technical measurements on imagery of length and width of vehicle cargo beds can yield assessments of similar hauling capacities. Measurements on maps of areas can yield assessments related to land-use potential. Measurements of liquid or gas containers on im-



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agery can help practitioners to understand the relationship between each during a manufacturing process or the extent to which the tanks are interchangeable. 7.3.3.5  Related Functionally

Many processes require different pieces of equipment that work together to perform the same function. Therefore, while the color, shape, and size of each piece of equipment differs from others based on its specific functionality, together they work to achieve a common functional outcome. Practitioners can use visual and technical analysis on imagery and maps to discover relations in function. For example, the practitioner may conduct technical analysis on a map in a GIS using geoprocessing tools that find all of the disparate types of windmills across a large area that are related in their function to generate power for the electric grid. In another example, the practitioners may visualize dozens of construction vehicles on imagery in an ELT that vary greatly in appearance (such as dump trucks, front end loaders, and road graders), but are related in their broader function of construction in an area. 7.3.4  Geospatial Analytic Reasoning

Reasoning is the process of using existing information to critically think about a subject in order to improve knowledge and is foundational to geospatial analysis. In previous chapters, we introduced spatial reasoning (i.e., mental rotation and mental construction) and geospatial observational reasoning (i.e., visual interpolation and visual extrapolation). This section extends these ideas into broader location-based reasoning methods called geospatial analytic reasoning, which is reasoning about locations, identifications, relationships, and context. It is commonly used to interpret the shapes of objects (including shadows), determine the relationship of objects to nearby entities, establish the location of an unseen entity during interpolation and extrapolation, and even estimate the time of day and time of year in which a picture was taken. These uses are summarized below as principles of geospatial reasoning, geospatial analysis baselines, analytic interpolation, analytic extrapolation, and deductive and inductive geospatial reasoning.25 7.3.4.1  Principles of Geospatial Reasoning

Geospatial reasoning emphasizes the location mindset for researching any given topic and begins at a point on the geographic grid. The practitioners first estab25. The importance of reasoning is underscored by the U.S. Office of Personnel Management’s classification of an intelligence job series that states “Intelligence Research Specialists apply a basic knowledge of a professional discipline, the principles and techniques of inductive and deductive reasoning, and a subject-matter knowledge of either a geographical area or a functional area to the production of finished intelligence reports” (Office of Personnel Management, “Position Classification Standard Flysheet for Intelligence Series”).

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lish the most important general locales related to their topic and then employ a target-based approach to research, focusing on points, lines of communication, and broad areas surrounding both. Then the practitioners should establish a general measure of regular activity related to their object of inquiry, also known as a baseline. 7.3.4.2  Geospatial Analysis Baselines

Location-based baselines of entities create a standard against which subsequent geospatial reasoning may be measured. Chapter 5 refers to visual baselines as the viewing of the same entity many times to establish a strong understanding of its features in the practitioners’ minds. Analytic baselines move outside the practitioners’ minds to create a comprehensive, location-based record of an entity at a point in time. These are carefully structured studies of an entity that incorporate extensive analysis of imagery, spatial data, and other geolocated collateral information to create a durable record of the entity, from a single piece of equipment to a facility. This record begins with a point target negation of the entity, which establishes the starting point of the entity’s timeline. The record continues with detailing the first observations of the entity and then documenting its development through time to the current period to create a timeline-based measurement standard. The practitioner then records the current status of the entity, taking special care to document lines of communication within the area of interest. Once complete, this baseline is an analytic measurement standard that may structure subsequent geospatial reasoning related to the entity. 7.3.4.3  Analytic Interpolation

Chapter 5 refers to visual interpolation as the process of a subject visually filling gaps within a given dataset during a period of observation. Analytic interpolation extends this process outside the practitioners’ minds by using imagination and analytic tools to scrutinize perceived gaps in data, and then visualize data in different ways to address such gaps. Technical tradecraft such as imagery mensuration, range adjustment, zoom, and physical rotation and GIS-based geometric and spatial calculations for geospatial data (e.g., viewshed analysis) can sometimes address perceived gaps within datasets by revealing additional information for analysis. For example, in spatial analysis, creating accurate visualizations of cell phone data accuracy (presented as individual data points with surrounding buffers representing accuracy) may help the practitioners to use geospatial reasoning to either relate two points in space and time or rule out such potential relationships.



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7.3.4.4  Analytic Extrapolation

Chapter 5 refers to visual extrapolation as the process of a subject visually filling gaps outside of a given dataset during a period of observation. Analytic extrapolation extends this process outside the practitioners’ minds by scrutinizing gaps outside the data, addressing how and why such gaps exist, and what they might mean within a broader causal process. This scrutiny informs additional collection of geospatial data, as practitioners compare data that they have with future data availability to address assessment gaps related to a project’s ongoing research questions. For example, Section 7.2.2.3 introduced analytic extrapolation of electricity infrastructure improvements at People’s Republic of China’s Lop Nor nuclear weapons test area, which contributed to a broader assessment of the facility’s expanding functional capabilities over time. 7.3.4.5  Deductive and Inductive Geospatial Reasoning

Deductive reasoning starts with the assertion of general rules and proceeds to a logically necessary conclusion. In deductive reasoning, the practitioner organizes a set of general rules on a topic, and, if these rules are true, then certain conclusions must follow as necessarily true. Logical syllogisms provide us with examples of deductive reasoning, and deductive geospatial reasoning incorporates entities and locations into these kinds of rule-based assessments. Inductive reasoning begins with observations that are specific and limited in scope and proceeds to a generalized conclusion that is possible, probable, or likely in light of accumulated evidence. Geospatial analysis is often conducted using inductive reasoning through observing, asking questions, developing hypotheses, gathering evidence, seeking patterns, and forming caveated conclusions or assessments. Inductive geospatial reasoning incorporates entities and locations into observations, the gathering of which may lead to the development of deductive broader geospatial indicators and signature analysis principles. 7.3.4.6  Geospatial Reasoning Example

Suppose that a technical surveillance team must know the height of a privacy fence in order to place a camera outside of it, which would provide operators and the analysts that support them with a view into a compound. The compound is in a mostly denied area where there will only be one chance to ingress, install the camera, and egress. The geospatial analyst supporting the team decides to use remote sensing to access the denied area, with imagery analysis as the overarching methodology, and geospatial reasoning, technical analysis, visual analysis, and the Four Cornerstones as the practices for estimating the approximate fence height. The analyst first uses visual analysis and interpolation of satellite imagery to determine that the fence around the property is continuous and appears to be a store-bought, paneled, wooden privacy fence. The analyst then performs technical analysis with ELT mensuration tools to

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measure the height of the fence and, after 10 measurements, calculates the average height as 5 feet and 8 inches. Next, the analyst reverts to visual analysis and compares the fence’s shadow to nearby shadows of objects with commonly known heights at the same orientation to the Sun. The analyst finds a nearby car, a one-story building, and a small shed, and both visually and technically compares all of the shadows using geospatial reasoning and mensuration tools. Finally, the analyst adds deductive reasoning to the analysis by noting that the standard height of the most common paneled privacy fences available at nearby hardware stores are 6 feet tall and that the fence in question is likely not a standard fence based on visual characteristics. The analyst finalizes an assessment that the privacy fence is continuous, stands approximately 5 feet 8 inches tall, and is likely custom-built and delivers this information to the surveillance team. Figure 7.17 provides an image in which practitioners can use geospatial reasoning to identify key objects and estimate their heights and the time of day and even the time of year that the image was taken. 7.3.5  Analysis: Creating Observable Keys

Chapter 5 describes applying keys during the process of observation (including mentally through object recall). This section refers to the process of creating durable observable keys as visual baselines of entities, including equipment and facilities, which become the standards by which practitioners measure, identify, classify, and then relate entities to one another. Creating keys is a first step towards creating a baseline of a facility or piece of equipment and may be used to

Figure 7.17  Practitioners can use geospatial reasoning to identify key objects, estimate their heights, and the time of day and even the time of year that the image was taken [15].



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assist BAS. Then analysis keys extend this process to include developing specific observations as indicators of broader processes that cannot be directly observed. 7.3.5.1  Creating Observable Keys

Creating an observable key is a detailed method of documentation for an entity’s observable geospatial characteristics. Creating keys to use during periods of observation entails careful and thorough geospatial analysis and is akin to creating an analytic baseline. Regardless of entity type, most observable keys document some combination of the Four Cornerstones of an entity: location, color, shape, and context. Creating a key for observation first requires carefully defining the entity of interest. For entities such as a piece of equipment, first define and classify the equipment type. Then use location to filter areas of the world, country, or region containing this equipment. Sometimes, the practitioners start with an area of the world, which then helps to define categories and types of equipment. Document facilities that typically contain this type of equipment. Document the color and shape of equipment types, including specific measurements of size that include length and width characteristics. Gather as many visual examples as possible, from as many observational perspectives as possible. Search for and document examples of the equipment in different locations. Finally, write a synopsis of these characteristics together with visual examples, making sure to address location, color, shape (including size), and context for the entity. For entities such as a facility, use the same principles with different emphasis within the Four Cornerstones. First, define and classify the facility type. Then use location to filter areas of the world, country, or region containing these facility types. Document locations of specific facilities, choose a prototypical example of the facility and then document this facility’s features according to the Four Cornerstones. Document specific facility features and specific equipment types observed at the facility. Completion of this kind of facilitybased key is a first step towards creating a baseline of the facility and may be used to assist BAS. 7.3.6  Analysis for Geospatial Collection

Analysis for geospatial collection refers to structured, location-based data gathering techniques for ongoing assessments of entities.26 Building from the principle of analytic uncertainty introduced in Chapter 6, effective geospatial analysis clarifies what remains unknown, or uncertain, about a given topic. One method for reducing remaining uncertainty is through structured strategies for collection of additional data and information. This entails applying deductive 26. The word “collection” refers to gathering all available types of data and information to address a research question or issue.

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and inductive geospatial analysis techniques to identify gaps in observations and assessments, and then expanding the base of established geospatial data and information to fill these gaps. Deductive geospatial techniques for collection use certain abstract principles to organize knowledge about an entity or issue. Common deductive organizing principles include classification, identification, and process flow. Identification of an entity classifies it within a network of other entities, especially related objects and facilities, which implies the presence of other related entities even when they are not directly observed. Similarly, process flow related to an entity entails engagement in a certain sequence of events, even when these are not directly observed. Inductive geospatial techniques for collection begins with documenting geospatial observations of entities in a structured manner. This starts with identifying the most important observations related to an entity and then structuring data collection strategies to generate more of the same types of observation to buttress the existing base of knowledge on a topic. One common method for this includes using a spreadsheet to list observation-based identifications by data type, data date, specific location (i.e., latitude and longitude in decimal degrees), along with a simple description of the observation. By listing latitude and longitude in separate columns, these observations are geo-enabled and may easily be uploaded into a GIS and layered with other geospatial data. Another method includes creating slides of observations, including data type, date, and location (latitude and longitude). Documentation of relevant observations facilitates identifying gaps in observations, which, in turn, provides structure for gathering additional location-based data. In both cases, the practitioners can reason about necessary entities and events that are not yet observable and then structure collection strategies accordingly to gather data that records them. 7.3.7  Analytic Communications and Review

Analysis is not complete without a final analytic review for quality control. Reviewers make up one of the most important tools in the practitioners’ toolsets; they hold the keys to the assessment’s quality and objectivity. Quality control entails testing the assessment, reviewing the quality of evidence, peer reviewing the work, and sharpening the analytic communication. Reviewing consists of the practitioner and peers examining the quality of the project’s data, information, sources, assumptions, assessments, and analytic communications to find errors and improve the quality of the resulting assessment. While reviewing is iterative and should be done throughout the analytic process, the final analytic review should be conducted last, before communication or production. It consists of self-review, internal review and communication, and external review.



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7.3.7.1  Self-Review

Self-review is the first step in quality control. Once the practitioners have solidified an assessment, they can conduct their own review by scrutinizing the quality of the data, information, observations, and analysis. Here are some sample questions on that checklist for the practitioners’ reviews: 1. Have I gathered or collected all of the geospatial data that will answer my key questions? 2. What level of quality are the data and information that make up my underlying basis? 3. Have I completed all of the geospatial observations and analysis necessary to answer the key questions? 4. Did I maximize the use of the principles of geospatial analysis from Section 6.4? 5. Have I revealed areas of clarity and uncertainty such that they lead to the greatest possible understanding? 6. Have I used the highest quality references, keys, and standards? 7. To what extent is my assessment analysis subjective (i.e., individual) or objective (i.e., shared with a group)? 8. What are the implications of my analysis? Once the self-review is complete, the next step is to seek review from among people in the practitioners’ organizations. These are people that may know the practitioners and are internal to the organization and are an intermediate step between self-review and blind, external review. 7.3.7.2  Internal Review and Communication

Internal communication and review are intraorganizational review processes, sometimes referred to as quality control review and are the next step in the review process that will broaden the scope of objectivity and strengthen the research and assessment. Because the internal group is usually closest to the issue, it should provide the most substantive feedback related to subject matter content.27 Internal review can range in complexity from that of a simple assessment of a single entity’s identification to more complex quality control research and 27. It is important that practitioners maintain openness during the review process, especially with the people closest to them. Defensiveness, argumentativeness, and anger have no place in the review process and reflect poorly on the analyst and the organization and potentially jeopardize the quality of the analysis. After all, some of the quality control review may involve interactive research, experiments, and testing by a group of peers in a collaborative process.

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experiments. Quality control research includes gathering the evidentiary basis of the assessment and identifying items that may have been missed. Quality control experiments include testing an assessment in nature, in a laboratory, or on an ELT or GIS to replicate the circumstances that led to the assessment and measure to what extent the results are similar. Internal reviewers may additionally choose to conduct structured analytic techniques (SATs) in the imaginative, diagnostic, and contrarian categories that help to ensure quality and eliminate bias [16]. Some of the more common SATs relevant to more complex assessments derived from geospatial analysis are: • Quality of information check: Evaluates completeness and soundness of available information sources. • Argument mapping: Visually depicting arguments, theses, thoughts, and ideas to test the logical connections and synergy of ideas. • Brainstorming: An unconstrained group process designed to generate innovative, unlikely (high/low), and hypothetical (what if?) ideas and concepts. • Key assumption check: List and review the key working assumptions on which fundamental judgments rest. • Devil’s advocacy/steel manning: Challenging a single, strongly held view or consensus by building the best possible case for an alternative explanation. • Team A/Team B: Use of separate analytic teams that contrast two (or more) strongly held views or competing hypotheses. Central to the internal review process is communicating one’s analysis to others. Analytic communication is an unfinished form of communication that combines saying what you see (i.e., observational communication from Chapter 5) and saying what you think. Unfinished communications provide a common language that frames collaboration and allows for quality control that allows practitioners to slowly develop assessments through writing, composing visualizations, and talking through ideas. They are integral to the analytic process and often help to refine the desired communication as the areas of clarity and uncertainty are identified, and the proper estimative language and analytic judgments are tested and come into focus. They are the interim building blocks that move the analyst towards the finished communication that will express a final assessment. Analytic communications must document basic information with efficiency and clarity to successfully facilitate peer review of ideas. This practice will ensure that the practitioners accurately express their current state of



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understanding in a timely manner and improve trust by clarifying areas of uncertainty. The level of descriptive detail indicates areas of uncertainty; the more general the description, the more information is needed to identify and relate the entity. For example: • Communication of a description: “I see a dark-toned, boxy object.” • Communication of an entity’s general classification: “I see a dark-toned sedan.” • Communication of an entity’s specific name: “I see a dark-toned Toyota Camry.” Further, practitioners must at least communicate the following: data type, data time (of collection), location, and then any ideas about interpretation and/ or meaning, including a hypothesis. Clear visualization of data further helps peers to understand the practitioners’ ideas and facilitates higher-quality feedback, especially if peers have access to the same data to independently review. Once internal review and communication are complete, it is time to engage in a blind, external review from outside the organization. 7.3.7.3  External Review

Next, external review entails engaging an outside, independent peer review in order to broaden the scope of objectivity. It requires engaging individuals outside of the team or group that produced the assessment; this introduces fresh perspectives, diverse areas of expertise, and subject matter expertise in related issues. During the external review process, the practitioner should remain open to feedback, uncertain of the outcome, and malleable with respect to the assessment. Once the external review returns, welcome alternative explanations, research, map, test, and debate them and integrate them into the assessment if necessary. After concluding the external review, the assessment should be sufficiently solidified.

7.4  Conclusion Geospatial analysis is emerging as a vital skill set in the private and public sectors. Private companies, nongovernmental organizations, and governments rely on geospatial analysis to help to discover precious resources, preserve flora and fauna, dispatch emergency services, and defend national security. As the ability to derive locational data from more sources and visualize them on various media increases, the ability to systematically analyze those entities in those locations will likewise prove to be an increasingly important resource. Soon, juries

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will expect geospatial analysis as digital forensics rivals biological forensics in popularity and familiarity. Businesses will require it to understand marketing and supply chains. Governments will require it to understand their dispositions and challenges. Geospatial practitioners will become highly sought-after, and organizations will require dedicated resources staffed by professionals trained with the geospatial mindset, toolset, and skill set. At the conclusion of geospatial analysis, the formerly voluminous geospatial data has been refined into precise and concise information, and a thesis or assessment is born. It now contains statements, graphics, and even the outline of a more in-depth product that clearly represents the practitioners’ new contributions to the field. While it should answer the research question, increase understanding, and reduce uncertainty in some areas, it may also open new areas of future inquiry. Yet the data-to-information refinement process is not complete, as this analysis must now be communicated clearly to others. Chapter 8 will focus on finished geospatial communications and how to best construct and disseminate a product to a customer. To fully maximize the combination of location, visualization, and technical tools that make geospatial analysis so compelling, one must transmit these scientifically viable and philosophically sound stories to the world through the third element of the OAC framework: communication.

References [1] Maxar, Satellite image from January 10, 2019, Catalog ID: 1050010013D86800. [2] Maxar, Satellite image from September 22, 2015, Catalog ID: 10400100125FE900. [3] Maxar, Satellite image from December 18, 2020, Catalog ID: 1040010065B78B00. [4] Planet, Satellite imagery from April 25, 2020, Left Image Scene ID: 20200425_070255_ ssc12_u0001; Right Image Scene ID: 20200425_095728_ssc6_u0001. [5] Planet, Satellite imagery from July 20, 2021, Scene ID: 20210720_042626_ssc4_u0001. [6] Planet, Satellite imagery from July 26, 2021, Scene ID: 20210726_050840_ssc1_u0001. [7] ESRI, ArcGIS Software, https://www.arcgis.com/home/index.html. [8] ESRI, ArcGIS Software Light Gray Canvas, basemap, https://pro.arcgis.com/en/pro-app/ latest/help/mapping/map-authoring/author-a-basemap.htm. [9] ESRI, “How Hot Spot Analysis (Getis-Ord Gi*) Works,” ArcPro 3.0 Help Archive, https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/h-how-hot-spotanalysis-getis-ord-gi-spatial-stati.htm. [10] ESRI, ArcGIS Software Light Gray and Dark Gray Canvas, basemap, https://pro.arcgis. com/en/pro-app/latest/help/mapping/map-authoring/author-a-basemap.htm. [11] ESRI, “Summarize Center and Dispersion,” ArcGIS Software Documentation, https:// doc.arcgis.com/en/arcgis-online/analyze/summarize-center-and-dispersion.htm.



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[12] ESRI, “Create Buffers,” ArcGIS Software Documentation, https://doc.arcgis.com/en/ arcgis-online/analyze/create-buffers.htm. [13] ESRI, ArcGIS Software Streets (night) basemap, https://pro.arcgis.com/en/pro-app/latest/ help/mapping/map-authoring/author-a-basemap.htm. [14] USNI News, “Document: Office of Naval Intelligence’s Chinese People’s Liberation Army Navy, Coast Guard Ship Identification Guide,” December 15, 2022, https://news.usni. org/2022/12/15/document-office-of-naval-intelligences-chinese-peoples-liberation-armynavy-coast-guard-ship-identification-guide. [15] ESRI, ArcGIS Software Imagery basemap, https://pro.arcgis.com/en/pro-app/latest/help/ mapping/map-authoring/author-a-basemap.htm. [16] United States Defense Intelligence Agency, “A Tradecraft Primer: Structured Analytic Techniques for Improving Intelligence Analysis,” Directorate for Analysis, March 2008, https://www.dia.mil/FOIA/FOIA-Electronic-Reading-Room/FileId/161442/.

8 The Geospatial Skill Set: Communication Principles 8.1  Introduction to Geospatial Communications Principles Communications are the final skill in the OAC framework, although they are ever-present and iterative during observations and analysis in an unfinished format. Unfinished communications during observations and early analysis allow practitioners to talk themselves through steps, document their work, and collaborate with peers. Further, these communications continue to transform during analysis as the practitioner and peers distill and solidify an assessment. Finished communications emerge during the final phase of communications as practitioners complete the assessment and disseminate or present it to an audience. With this act, an iteration of the data-to-information transformation is complete. This chapter outlines the definition, purpose, and principles that guide the skill of geospatial communications. Geospatial communications must clearly express the most important location-based insights to an audience. Just as the location mindset elevates place as the most important variable for research, so geospatial communications must efficiently relate why place matters, and how location-based observations and analysis support broader assessments. Efficiency demands that the practitioner distill only the most important insights from their findings for finished communication to an audience of peers. To achieve this, practitioners must prioritize location and visualization to clearly communicate their distilled assessment. Resulting communications complete 171

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the move from individual, subject-based analysis to a peer-reviewed, more objective assessment.

8.2  Defining Geospatial Communication Communication is a broad category that encompasses any imparting or exchange of information. A geospatial communication is any dissemination or exchange of Earth-referenced, location-enriched information. Finished geospatial communications are assessments that include locational, entity, temporal, and sourcing information, usually accompanied by compelling visualizations. Chapters 8 and 9 apply three enduring communication modes to geospatial communications: the written word, the visual arts, and verbal persuasion. While each mode may be mutually reinforcing, each also has certain strengths for expressing geospatial information.

8.3  Purpose of a Geospatial Communication The general purpose of geospatial communication is twofold: dissemination and exchange. Dissemination is the initial one-way publishing of an assessment to a general audience without immediate feedback. Dissemination of a product within a professional community is usually the culmination of a process that integrates some amount of expert peer review and creates a durable record for long-term reference. However, once the product is published, there may be little or no feedback provided to the practitioner. An exchange is a communication with opportunity for specific, direct peer and/or audience feedback. These are similar to a market exchange: the practitioner is selling their ideas, and the audience pays with their attention and their feedback. Testing an assessment through audience feedback is a rare opportunity for any practitioner. Because critical feedback may at first be psychologically difficult for the practitioner to absorb, the practitioner must embrace a “common search for truth” perspective and accept that critical feedback may improve everyone’s understanding. This perspective contributes to a communication’s purpose and should encourage the practitioner to use their voice, whether graphical, written, or oral, for communicating geospatial information and intelligence. It also encourages the practitioner to use their ears and their mind to listen to others and contemplate feedback as part of the lifelong pursuit of reducing uncertainty and gaining understanding. More specific purposes of geospatial communications depend on audience, mode of communication, and product types, discussed in greater detail in the following sections.



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8.4  Geospatial Communication Principles Geospatial communications, like the observations and analysis categories that came before, have both principles and practices. Its principles include the need to communicate iteratively throughout the geospatial workflow, first with unfinished communications that will further the data-to-information transformation, and finally with a finished communication in the form of the assessment. They also include the fundamentals of effectively translating location and visualization into words and graphics, properly communicating uncertainty, and distilling communications in the most efficient manner that aids in decisionmaking. The following geospatial communication principles will provide a foundation for practitioners to build on when it is time to deliver an assessment to an audience: 1. Know the primary audience and the purpose for communicating the assessment. 2. Use unfinished communications iteratively as a bridge to finished communications. Communications build from unfinished communications during observations and analysis into finished communications in writing, graphics, and presentations. 3. Distill communications. Use the less-is-more principle featuring an economy of words and visuals to make more of an impact. Those who need to hear it most often have the least time. 4. Ensure that finished geospatial communications contain fundamentals: location, time, entity, and sourcing. 5. Communicate uncertainty. Uncertainty is one of the most important aspects of a geospatial communication and should be clearly presented. 6. Perfect visual communications. Visualizations of locations and entities convey large amounts of information quickly and coherently and tell a story that words alone cannot. 7. Create opportunities for a geospatial presentation. Practice and peerreview your presentations. During an optimal geospatial presentation, one should inform, persuade, listen, and be persuaded. Lead with location and visualizations. Use the audience for objectivity. These principles frame how to begin creating effective geospatial communications of assessments. They reflect fundamental themes within geospatial communication: audience, unfinished versus finished, distillation, visualization, and presentations. Next we provide additional reflections on these themes.�

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8.4.1  Knowing One’s Audience and Purpose

Effective communication requires that one knows how to match the right material with the right audience and then communicate with the right purpose in mind. Practitioners should know the level of knowledge and expertise of their audience and cater the communication to be more technical for industry experts and more simple for laypeople. One should also know the level of the customer within the organization or industry. Because higher-level audiences generally have less time, practitioners should prepare a shorter and more broad communication for seniors and a longer, more detailed communication for junior practitioners. Additionally, practitioners must know the time that their audience has to receive the communication, and one should have multiple versions of the communication prepared for varying circumstances. Finally, the practitioner should know the purpose of the communication and custom tailored versions to inform or persuade their audience. Once the primary audience is established, there are secondary principles of style and succinctness to consider. For example, if one is communicating to a high-level audience of decision-makers outside of their organization, one should use simple and succinct language, and impactful visualizations. If one is communicating to a lower-level audience of industry experts, one can use more insider terminology and more detailed examples. Customized tailoring one’s communication to the primary audience greatly increases the odds that the communications will be well received. Knowing one’s audience is a skill that improves with time. It should be taught through mentorship to junior practitioners in a new industry or organization so that practitioners can flourish. Audiences will vary, and no single assessment or product will ever please customers at varying levels or within varying industries, and trying to expand one’s communication into a one-sizefits-all model will compromise the product’s overall observations, analysis, and communication quality. The greater an effort made from the practitioner to know their audience and purpose, the more one can mitigate these issues. 8.4.2  Unfinished Versus Finished Geospatial Communications

Geospatial communications range from informal and unfinished exchanges of ideas to finished, peer-reviewed, formal assessments. The previous chapters examined unfinished geospatial communications found in observations and analysis. These geospatial communications are informal expressions of location, time, entity, and sourcing that allow practitioners to explore and find their voice with respect to geospatial observations and analysis, test interpretations of data, and facilitate communication of assessments for self-review and peer review. Observation communications are used to communicate what the observer sees, where practitioners are encouraged to say what they see with reference to loca-



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tion, time, entity, and sourcing. Analytic communications are used to communicate what the observer thinks, where practitioners are encouraged to say what they think with reference to location, time, entity, and sourcing. Finished geospatial communications are peer-reviewed publications of geospatial analysis assessments in which practitioners say only what they mean. They are the gold standard for geospatial communications that summarize broader assessments of the most important observations. They combine the foundational elements of location, time, entity, and sourcing and integrate additional collateral information, sources, peer review, and caveats to clarify the assessment’s basis and acknowledge remaining uncertainties. 8.4.3  Distillation of Communications

Customers that most need to hear a practitioner’s geospatial communication are often the busiest people with the least amount of time. Examples include a military commander who has the power to shape operations or an organization’s director that has the budget and the power to shift resources and make immediate changes that affect the entire organization. To reach these and other target audiences effectively, practice distilling geospatial communications so that the least amount of words and visuals expresses only the most important points and nothing more. Practitioners can use the principle of distillation throughout the geospatial skill set. During observations, practitioners should refine their unfinished communications from lengthy descriptions of what they see to more narrow and relevant statements describing only the entities that are worthy of further analysis. During analysis, communications should be further distilled from more lengthy statements of what one sees, reads, and thinks to a more succinct series of statements that will be crafted into the assessment. During the final process of finished communications, sentences are distilled to the smallest number of words that will convey the most meaning to an audience. The process of communication distillation from unfinished to finished is similar to the data-to-information refinement process in that the input is high-volume and somewhat disoriented, while the output is succinct, high-quality, efficient, and effective. When the principle of distillation is followed, the practitioner can deliver the communication to a wider variety of audiences so that it has more influence over time. 8.4.4  Communication Through Visualizations

A practitioner should maximize the use of visualization in the most succinct way possible to convince an audience and convey their assessment. Pictures tell a story that words alone cannot, which harkens back to the famous saying that a picture is worth a thousand words. Most geospatial analyses occur through

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visualization and, if a visual convinces the practitioner of a thesis or assessment, allow it to similarly convince the audience. As demonstrated in previous chapters, during observation and analysis many visuals should be collected, linked to locations (where possible), layered with other data, and used as keys and building blocks towards the eventual assessment. Then the practitioners must discriminate between unfinished graphics and the finished visuals that will make the final selection for presentation to an audience and should select the very best image or images that represent the assessment. They should choose the right look angles, the highest quality, and the best representation of what convinced them of the eventual assessment. This further entails framing the right focal point of an entity or event that supports the assessment, understanding when to use insets to show both a broader perspective and a critical detail on the same graphic, and a knowledge of when and how to use the written word in titles and interpretation to support the visualization and guide the audience. Chapter 9 will expand upon these themes in particular.� 8.4.5  Presentation

Presentations are a rare form of communication that offer the practitioner unique opportunities for organizational exposure and feedback. The practitioner should seek out such opportunities to present their work whenever possible. To start, short presentations in sit-down meetings within their own organization may be appropriate. Then the practitioner should prepare stand-up presentations to a higher level or more formal audience. At all stages, mentorship for presentation is essential, as there is no substitute for practice under an experienced eye and in front of an audience. Indeed, practitioners can find their voice through practice and repetition, and as they perfect their geospatial communications, they should get increasingly closer to their audience until they are presenting directly to them. The geospatial presentation comprises a story or assessment with visual aids in front of a live audience. This allows the practitioner to test their assessment and their presentation skills in an environment that can yield direct and immediate feedback. This feedback will test the assessment and create new venues of uncertainty and inquiry. In the practitioner’s career, few things will bring their own professional career and the message of their assessment more notoriety than the geospatial presentation. This principle is expanded upon with a deeper examination of the practices of the geospatial presentation in Chapter 9.

8.5  Foundations of a Finished Geospatial Communication A finished geospatial communication must clearly convey four foundational elements: a location, a time, entity, and data sourcing. When producing im-



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agery-based communications, the practitioner should focus on visualizations of the image, location, time of collection, notes (often referred to as callouts) about one or more entities shown within the image, and image source. When producing map-based communications, practitioners should similarly focus on visualizations of the map, the locational and temporal attributes associated with each entity, and the source of the vector data. Next is a more detailed overview of these elements. 8.5.1  Location

The most important principle of any geospatial communication is to lead with location, especially for finished geospatial communications. Location communicates identification, classification, time, culture, and other important factors. Leading with location entails referencing the geographic grid (i.e., latitude and longitude) and/or local cultural information, such as a place name, an image of an iconic landmark, or an address. Text-based examples of locations in a geospatial communication include “Russia,” “1234 Maple Ave,” “29.5544 -95.8339,” or “The National Zoo.” 8.5.2  Time

A finished geospatial communication must convey time (also known as date/ time) through verb tense, visual comparisons of the same locations collected at different times, or more precise temporal information. This includes relating data collection timestamps, referencing the time from a clock, presenting before and after images of the same location to show change, or establishing a timeline of events as part of an analytic process. Text-based examples of time in geospatial communications include “is,” “was,” “will,” “2245,” “today,” and “in 2020.” 8.5.3  Entity

A finished geospatial communication must contain an entity that comprises the object or focal point of the communication. Entities can be people, groups, objects, and items, complete with their associated features and attributes. Text-based examples of entities in a geospatial communication include “John Doe,” “a Tyrannosaurus femur,” “a T-55 tank platoon,” “a silver maple tree,” or “Ukraine.” 8.5.4  Sourcing

Finished geospatial communications must contain sourcing for geospatial data and other collateral information. This allows readers to independently assess

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the observations and analysis underpinning the communication and facilitates peer-review processes that improve the objectivity of communications. Sourcing can include listing data sources such as sensors, references to already published sources, and references to individual communications such as interviews and emails. Examples of sourcing include “Maxar WV3 imagery,” “United States Government,” “Intelligence Community Directive 203: Analytic Standards,” and “Email interview with author.”

8.6  Conclusion The principles of geospatial communications lay the foundation for the practices of geospatial communications in Chapter 9. These principles frame effective geospatial communication practices by focusing on knowing the audience, using unfinished and finished communications to their best advantage, distilling communications for efficiency, perfecting visuals, and understanding the importance of in-person presentations. This chapter ends by outlining the foundations of a geospatial communication, which include locational, temporal, entity, and sourcing details. These four elements form the basis of information that is provided to the audience and are the elements of information that help to complete the data-to-information transformation. It is these principles to which the practitioner should adhere when conducting the practices of geospatial communications, examined in Chapter 9.

9 The Geospatial Skill Set: Communication Practices 9.1  Introduction to Geospatial Communications Practices While geospatial communications range from unfinished to finished, their hallmark is to integrate writing, visualization, and verbal presentation of locationbased insights. This chapter focuses on finished geospatial communications. It begins with writing, as text can be the most effective way to ensure that the practitioner’s interpretation of spatial and temporal information guides the customer. Next, this chapter shows how to pair clear, accurate, informative sentences with strong visual aids, including maps, images, and graphics. Finally, practitioners will learn how to find their voice by combining text and visual aids with a vocal accompaniment to deliver assessments to an audience. Taken together, this chapter shows how geospatial communications are an extension of analysis, as the production of a geospatial communication itself may lead to new analytic insights. Further, it should be noted that all geospatial communications are underpinned with a reference to geographic coordinates, although the specific terms of communication used may be relative and cultural, depending on the audience. Upon completion of the practices of geospatial communication, practitioners should have a completed assessment and a product drawn from multiple media. With the act of dissemination or presentation of that product, practitioners have completed the skill set of OAC and one iteration of the data-to-information refinement process.

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9.2  Structured Geospatial Communication Techniques A geospatial communication of a formal product or report is often the crowning achievement of a practitioner’s involvement with geospatial data and information. While geospatial communications may be analytically separated into distinct modes of expression such as writing, graphics, and presentations, in practice, these are often combined into an integrated whole, with each mode emphasized depending on the specific purpose and the audience. Building from the foundational elements of location, time, entity, and sourcing, the following structured geospatial communications techniques are practices that can aid communications. They present a chronological approach to communications that may somewhat vary depending on the audience and the mode of communication (i.e., writing, visual, and verbal): 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Distilling the geospatial communication; Assessing the audience; Writing; Applying the Four Cornerstones; Graphics; Presentations; Communicating uncertainty; Geospatial confidence communication; Building the product; Peer review.

Clear geospatial communications begin with the practice of distilling communications. 9.2.1  Distilling the Geospatial Communication

Practitioners should begin by distilling their communication as much as possible. This refines the communication and helps practitioners to express the most important aspects of the assessment in the fewest words. Practitioners should start by gathering their most important observational and analytic communications. Then they should consider the most important, generalizable meaning of these observations and express this meaning in a single sentence. This singlesentence expression of meaning becomes the practitioner’s thesis statement, for example: “The recent expansion of large swaths of an invasive species is threatening many native plants and trees all over the Southeast United States, according to imagery analysis and open source reporting.”



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First, identify and refine the location (all over the Southeast United States), time (recent), entity (an invasive species), and sourcing (imagery analysis and open source reporting). Refine these elements by providing more specific locations (North Carolina and South Carolina), time (2022), entity (kudzu), and sourcing (multispectral imagery and The Botany Journal). Next, look for extra words that are redundant, vague, or extraneous or provide little value, such as “all over” and “large swaths.” The refined assessment should read as follows: “The 2022 expansion of kudzu is threatening many native plants and trees in North Carolina and South Carolina, according to multispectral imagery analysis and The Journal of Botany.” Then the practitioner should further refine the sentence by reducing it to the fewest number of words that can still effectively communicate the assessment. This often includes referencing a dictionary and thesaurus and knowing when to combine and transpose words to eliminate prepositions and articles. For example, one can combine “plants and trees” into the word “flora,” combine “North Carolina and South Carolina” into “the Carolinas,” transform the passive phrase “the expansion of kudzu” into the active voice, and eliminate the extraneous word “many.” The final iteration of the sentence is well refined and ready for peer review: “Kudzu expanded in 2022 across the Carolinas, threatening native flora, according to multispectral imagery analysis and The Journal of Botany.” Finally, one should test the communication in an objective environment by bringing in peer reviewers to conduct the exercise of distillation alongside the practitioner. The result will be the most succinct version of the practitioner’s distilled geospatial assessment. The outcome of this practice should be a distilled geospatial assessment that presents the most succinct argument about Earth-referenced entities. Once distillation is complete, the practitioner can envision the audience for such a communication. 9.2.2  Assessing the Audience

Identifying the audience or customer of the assessment is the primary driver of geospatial communication production strategies. The audience may already be understood before the research even begins, if a practitioner’s research is directed or if they are reporting as part of a priority framework. Assessing the audience can also occur during observations and analysis as one discovers data, transforms it into information, and matches the geographical significance to leaders with interests in a similar region. Finally, a presenter can assess an audience before or during a presentation and adjust the material accordingly. The audience might be a small group of colleagues in one’s field, a group of private

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sector customers, or a government executive with the power to make public safety or national security decisions. There are two primary axes for assessing an audience: the authority level of the audience (i.e., executive status, and/or level of subject matter expertise), and the format for reaching the audience (i.e., the expected communication mode). In general, practitioners should fashion their communications for higher-level authorities to be shorter in length and broader in geographic and technical scope. Inversely, the lower the authority level, the longer and more geographically and technically detailed that one can make one’s communication. Relatedly, the practitioner should assess the subject matter expertise of the audience and from this infer the expected level of detail for the communication. When in doubt, simplify the message but maintain access to optional materials such as footnotes for written communications, extra graphics for more complex visualizations, and extra time for questions after verbal presentations. After authority assessment, most opportunities for communication conform to an established format that is clear to the practitioner, determined in part by the product type available to the participants within their organization or their specific career field. In these cases, the practitioner should study any format templates and work to perfect the craft of creating a geospatial communication according to this form. This approach properly targets the primary audience, but also accommodates the potential for further dissemination of the communication. Presenters can assess the audience and make ongoing adjustments to their communications. A presenter can assess an audience before a presentation and custom-tailor that presentation to the audience by adding new slides of more recent images or maps or reducing slide count or presenting material from another perspective. In the moment, a great presenter can change the mood, length, and interaction level of the presentation. For example, presenters can slow the pace and let people engage more with the images and maps and shift from lecture to interaction by asking questions and soliciting ideas. Some of the questions can encourage interaction with the planned presentation, and some can directly inquire as to which topics are most interesting to them. This requires the presenter to shift topics quickly and be prepared for a broader variety of material. The more the presenter knows the audience, the easier it will be to pivot outside of the prepared presentation. Once audience assessment is complete, the practitioner can select the most effective mode or modes of geospatial communication to deliver. The primary modes of communication for geospatial analysis are writing, graphics, and presentations. The following sections provide an overview of strategies for each of these general modes of communication.



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9.2.3  Writing

Writing is a fundamental component of finished geospatial communications. A piece of writing may easily stand on its own, as writing naturally conveys time, entity, and location due to the fundamental components of a sentence: subject, verb, and object. Writing provides added durability because it lasts longer than the spoken word, and it can be disseminated to disparate audiences in databases, emails, or other forms of media. A finished written geospatial communication conveys a distilled assessment and includes all the elements of a published report, such as data sourcing, descriptions of location and spatial orientations, and timeframe. Written products are then disseminated as durable publications to particular communities, depending on format and organizational protocols, with or without possibilities for feedback. Great geospatial communications begin with fundamentally strong sentences and paragraphs.� 9.2.3.1  Writing Organization: Sentences

The most important part of the written document is the distilled assessment that the practitioner has derived from their geospatial observations and analysis. Usually, a practitioner can write this assessment in a single sentence. In unfinished form, this sentence can refer to some combination of location, time, and entity. However, in finished form, this sentence must refer to location, time, entity, and sourcing. Whether finished or unfinished, the sentence should be organized in an active voice in a manner similar to the below examples: • Unfinished geospatial communication: “Analysis of imagery showed two small, light-toned vehicles near the corner of Danforth and Greenwood Streets on Feb 1st.” • Unfinished geospatial communication: “Analysis of video stills from surveillance cameras showed two white Honda Civics near the corner of Danforth and Greenwood Streets at 9:15AM on February 1st.” • Finished geospatial communication: “Analysis of Planet satellite imagery of Toronto and surveillance video from a nearby store from February 1st at 9:15AM revealed two Honda Civic passenger vehicles near the corner of Danforth and Greenwood Ave at the time of the nearby bank robbery.” • Finished geospatial communication: “The Toronto Police Service assesses with high confidence that the suspects who committed the recent bank robbery near Danforth and Greenwood were driving white Honda Civics.”

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Sentences should always be written in an active voice, make location central, and generally support their paragraph’s topic sentence and the product’s assessment. Next, the practitioner should examine the organization of the paragraphs that make up successful geospatial communications. 9.2.3.2  Writing Organization: Paragraphs

Authors can deliver geospatial communications in single-paragraph or multiparagraph products that require organization and follow certain structures. Proper organization generally requires introducing the reader to a summary of the issue, following this with a body of more detailed supporting information, and then concluding with an outlook of new information and future expectations. Writing models that fit well with geospatial communications are the what, so what, what next model for introductory paragraphs and the inverted pyramid and timeline methods for body paragraphs. These organizational models may be applied to all pieces of analytic writing, from single paragraph to multiparagraph products, although specific mechanics will differ based on specific product formatting. Although paragraphs in geospatial documents can follow various formats, they share commonalities in basic structure and overall organization with a document. To start, each paragraph must have a strong topic sentence that introduces the theme of the paragraph, with support from subsequent sentences that provide more detail about that theme. In terms of multiparagraph organization within a document, the first paragraph is usually a summary, the body paragraphs are for background and analysis, and the final paragraph is the conclusion or outlook. The summary paragraph stands on its own and often follows some variation on the what, so what, what next format. This format requires three sentences that state clearly the geospatially focused assessment first (what), the importance or relevance to the customer (so what), and what the customer can expect to happen next (what next). The subsequent body paragraphs often follow the inverted pyramid format that frontloads a strong topic sentence and then details supporting evidence prioritized by importance. When using the inverted pyramid method, the paragraph may conclude with the last, least important detail of evidence that nonetheless still supports the topic sentence. Finally, a conclusion paragraph can either summarize the product in a fresh way, relate it to broader events or things to come, or provide an outlook that informs the reader of what to expect next instead of restating the assessment.� 9.2.3.3  Writing Style Points

Writing style varies by organization, but certain elements of professional geospatial communication remain constant. The following style points promote clarity in writing: using a style guide, assessing voice, avoiding passive voice, word choice and tense, and point of view.



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Style Guides

Geospatial analysis style guides are documents that establish standards for writing, formatting, and designing for geospatial communication, especially related to scientific and industry-related terminology. The commonly used public style guides include The Chicago Manual of Style, The Associated Press Stylebook (AP style for journalism), and Publication Manual of the American Psychological Association (APA style), the Council of Scientific Editors (CSE) Scientific Style and Format: The CSE Manual for Authors, Editors, and Publishers, and the New Oxford Style Manual for academics. Most organizations have their own private style guides to establish an organizational identity and ensure accuracy and consistency in their production. A recent publication from an organization could serve as a first approximation of that organization’s style guide. Assessing Voice

Authors of professional documents that represent organizations should make sure to follow the organization’s style guide to assess the proper voice that the author should assume. In general, in professional analytic writing, one should use a more generic, formal style that emphasizes analytic conclusions without revealing the personality of the author. However, some authors who represent themselves or work for an organization that condones individuality in their style guide may choose to take a more personal or familiar tone with their audience. Avoiding Passive Voice

Geospatial communications should avoid the use of passive voice, as it obscures the identity of the subject and creates uncertainty for the reader. Instead, authors should use active voice in their geospatial communications to mitigate this uncertainty. Active voice produces a sentence in which the subject performs an action, while passive voice produces a sentence in which the subject receives an action. Using active voice directs the reader to the subject of the sentence, simplifies things for the communicator, and provides a structure that helps the communicator to have more control over the communication. To change a sentence from the passive voice to the active voice, determine who or what performs the action and use that person or thing as the subject of the sentence. Word Choice and Tense

Authors of geospatial writing should always use the appropriate word choice and tense. Authors may at times choose to use first-choice words, which are words that are the most common and most easily understood. At other times, authors may be required to use specialized vocabulary to express difficult topics; in such cases, authors should clearly establish and define specialized terms in the clearest way possible for readers. Practitioners should become acquainted

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with words common to geospatial writing related to directions, distances, and proximity descriptions. For example, when describing nearby objects that they observed on satellite imagery, many authors struggle with whether to describe things in terms of direction (north of, just south of, behind, in front of ) or distance (10m west of, 2m away from). In this case, consider the primary audience and the purpose for communicating. If the audience only needs to know that one object was near another, the authors should keep their word choice simple. If the audience requires that the practitioners detail the exact direction and distance because only that configuration will support the assessment, then proceed with more specific word choice. Authors should also consider appropriate word tenses and try to separate tense changes. Products that feature events from the past, or pictures (which are all from the past), should emphasize past tense. While those products may also feature writing that uses present or even future tense, the author should separate any tense changes by paragraph or section in order to clearly demarcate discussion of events in the past, situations in the present, or predictions for the future. Point of View

Authors should also carefully choose the point of view from which they write. Authors who are writing a paper that represents an organization might use the first person point of view to maintain an active voice. For example, an assessment from the World Health Alliance might read: “The World Health Alliance assesses that airborne diseases will increase as humans urbanize and live in closer proximity to each other. Our assessment is based on...”1 Authors might also choose to write in a third-person point of view when simply reporting on the locations and entities within a target area or broad area search for a database remark or a log of activity. 9.2.4  The Four Cornerstones for Geospatial Text

The Four Cornerstones for observations and analysis also applies to communications. Using location, color, shape, and context will add precision that will greatly improve a geospatial communication. Within those categories are specific words for unfinished geospatial communications to describe what the practitioner is seeing (observation) and thinking (analysis), as well as words for finished geospatial communications that include location, time, entity, and sourcing information. Use of the Four Cornerstones provides a road map through which practitioners can navigate using geospatial communications.

1. Another example might read as: “The Global Forestation Foundation assesses that deforestation in the developing world has increased threefold in the past decade,” and not as: “It is assessed that deforestation in the developing world has increased threefold in the past decade.”



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9.2.4.1  Communication of Location

Communicating location defines a geospatial communication. Communication of the location category includes descriptions of points, lines, and areas. Practitioners should remember the principle of leading with location and use the target method to orient the communication from the precise point to the surrounding area. The following are several examples. General Location Communications Location (points): “321 Maple St,” or “44°52 43.3 N 18°48 47.0 E” Location (lines): “State Highway 208,” or “Interstate 95,” or “Trans-Siberian Railroad” Location (areas): “Lake Anna” or “Blue Ridge Mountains” Observational Communication of Locations (Unfinished) Location (points): “I see a large, white, rectangular structure at 38.0813, –77.7950.” Locations (lines): “A witness saw the suspect’s vehicle driving southwest on State Highway 208 towards Lake Anna.” Location (areas): “I see a large body of water in front of the structures.” Analytic Communication of Locations (Unfinished) Location (points): “I see a large, white, rectangular structure at 38.0813, –77.7950 and I think it is a house.” Location (lines): “We think the suspect may be traveling on Virginia State Highway 208 towards a housing development near Lake Anna.” Location (areas): “The large, white, rectangular structure is probably part of a larger housing development. Additionally, I see a large body of water in front of the structures and I think it is Lake Anna.” Finished Geospatial Communication “Analysis of commercial satellite imagery and aerial photography posted to social media from February 3rd, 2022, revealed a vehicle matching that of the suspect’s in a development of approximately 12 similar houses on the north shore of Lake Anna, Virginia.”

9.2.4.2  Communication of Color and Shape

Communicating color and shape (including tone, size, shadow, texture, and pattern) presents the audience with the most readily accessible and objective elements of the entity. This will help to quickly connect with an audience and elicit their confidence in the assessment. Observation communication is the most general and relies on describing basic observations of an entity’s color and shape. Analysis communication conveys a preliminary assessment regarding what that color and shape may indicate beyond the initial observation. A finished geospatial communication of color and shape expresses an objective assessment of these features, along with data sourcing, time, location, and spatial orientation. The following are several examples.

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Sample Words for Color Color: “red,” “green,” “blue,” “yellow” Tone: “dark-toned,” “light-toned,” “shiny” Sample Words for Shape Size: “Large,” “short,” “tall,” “small,” “__-meter-long” Shape: “Boxy,” “square-shaped,” “rectangular,” “round” Shadow: Describe the shape and size of the shadow to reveal clues. Texture: “Smooth,” “rough,” “scaly” Pattern: “Configuration,” “distribution,” “repeating,” “formation” Observational Communications of Color and Shape (Unfinished) “I see a small, boxy, smooth object.” “I see a large, dark-toned object that is casting a long, boxy shadow.” “I see a red object.” “I see a dark-toned object.” Analytic Communications of Color and Shape (Unfinished) “I see a curved, multicolored entity in the sky that I think is a rainbow.” “I see a light-toned area that I think is a reflection.” “I see a small, boxy, white, smooth object and I think it is a box-body truck.” “I see a large, dark-toned object that is casting a long, boxy shadow and I think it is a shipping container.” “I see a 12-m-long, dark-toned object with a shadow pattern that matches the configuration of a LD-2000 Close-in Weapon System.” Finished Geospatial Communication of Color and Shape “Analysis of surveillance photographs and video camera footage revealed that a white truck and a large, rectangular, probable shipping container arrived behind the warehouse between 9PM and 11PM on August 3, 2022.”

Once the authors have described the entity’s color and shape, they are ready to communicate context beyond the entity, using location to link the entity to broader meaning. 9.2.4.3  Communication of Context

Communicating the context category includes clearly relaying visual, temporal, and collateral (i.e., non-georeferenced) information that provides a broader perspective. Visual context refers to visual data and information that is not visible from the original point location. Communication of visual context relies on proper characterization of the broader circumstances that place the primary entity in perspective. Characterization of visual context can be more subjective than that of color, shape, and location, so it requires highly accurate communication. Temporal context should communicate the important visual and technical measurements and interpretations that reveal time, which, in turn, may provide a causal structure for a practitioner’s assessment. Last, assessing collateral information is less precise than analysis of georeferenced data. Further, while collateral information can strengthen one’s communication by providing



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corroboration and context, it must still be consistent with locations of interest. Finally, similar to the color and shape examples above, communications of context range from simple to more complex. Sample Words for Context Visual context: “beyond,” “during,” “-wide” Temporal context: “are,” “were,” “yesterday,” “at 12 noon,” “on August 3, 1974” Collateral: “according to,” “analysis of,” “claimed,” “stated” Observational Communication of Context (Unfinished) Visual context: “I see people throughout the city wearing masks.” Temporal context: “I see businesses closed on Main Street.” Collateral: “I read an article announcing government-mandated lockdowns and safety measures.” Analytic Communication of Context (Unfinished) Visual context: “I see people all over the city wearing masks and I think it is due to recent COVID outbreaks.” Temporal context: “I saw businesses closed on Main Street today and I think it is due to government COVID mandates.” Collateral: “I read an article outlining government COVID mandates and I think it corroborates my observations of people wearing masks and businesses closed on Main Street.” Finished Geospatial Communication Using Context “Analysis of open source news articles, satellite imagery, and closed-circuit televisions revealed that since the beginning of the COVID-19 pandemic in March 2020, the citizens of Springfield have enabled numerous protocols to attempt to slow the spread of the virus.”

9.2.5  Graphics

Graphics are visual geospatial communications that contain a large amount of information in a small space. They incorporate and support written text, may accompany verbal communications, and connect to audiences in ways that verbal and text communications alone cannot. Geospatial graphics may also be disseminated as durable publications to particular communities, depending on format and organizational protocols, with or without possibilities for feedback. 9.2.5.1  Geospatial Graphics

Geospatial graphics communicate transformed geospatial information for the viewer. Just as a main argument’s written thesis statement must be expressible in a single sentence, so each geospatial graphic must be distilled to express a single geospatial observation, or a single set of geospatial observations (such as for a map). To qualify as a geospatial graphic, it must contain the foundational elements of location, time, entity, and sourcing on imagery or maps. Further, all geospatial graphics should contain the following five organizational elements:

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title, focal point, interpretation, orientation, and sourcing. The title should be a concise assessment that generally refers to a location, time, and entity. The graphic’s focal point is akin to a visual thesis statement; it is a visualization centered on a single theme or idea that supports the product’s assessment. The focal point should dominate the visual hierarchy, and variables such as position, size, shape, value, color, orientation, and texture should all reinforce the focal point. Interpretation includes callouts, which are text boxes that identify key elements of information on the image, and sometimes also includes an analysis section that delivers an assessment. Orientation refers to relating the graphic’s focal point to other context in the graphic, presenting imagery such that entities appear right-side up for the viewer (also known as “up is up”), and including a correctly oriented north arrow to convey objective orientation according to the geographic grid. Sourcing refers to including imagery sensor details, time of data acquisition, and any relevant collateral sources used to aid interpretation. Together, every graphic’s title, interpretation, orientation, and sourcing should all support its focal point, which should, in turn, support the product’s assessment. The following are examples of specific types of geospatial graphics illustrating these organizational themes: imagery, spatial, and infographics. An imagery graphic contains a literal picture (photograph from a camera, video still, satellite image) and attributed information that orients the audience. Figure 9.1 provides practitioners with an example of an imagery graphic showing title, focal point, interpretation, orientation, and sourcing. Spatial graphics are nonliteral visualizations of geographic information, usually in map format, that are composed of vector and raster data.2 Map graphics adhere to some additional design principles, such as the “rule of five” for colors3 and the use of contrast to emphasize a graphic’s focal point.4 For example, Figure 9.2 shows the location of a newly discovered probable underground facility at Lop Nor Nuclear Weapons Test Area in a map graphic combining vector and raster elements, complete with title, frame, time, north arrow, legend, and scale bar. Additionally, imagery and map graphics may include infographics. An infographic is an abstract presentation of charts, graphs, tables, or other data 2. Map graphics can include all of the fundamental components of a map such as a title, frame bounding the geographic information, north arrow, scale bar, legend, and citation. Map graphics may also use some of these elements to provide a useful frame of reference, but not adhere to all of the more strict map graphic standards from the field of cartography. 3. The “Rule of Five” is a guideline to use no more than 5 colors when composing a map graphic, because the human eye has difficulty differentiating between any more than 5 to 8 colors. In some workplaces, this is linked to a U.S. federal government accessibility law (United States Government, “Section 508 of the Rehabilitation Act of 1973.” Section 508. gov, March 2022. https://www.section508.gov/manage/laws-and-policies/). 4. Contrast refers to placing certain design elements in opposition to one another. Examples of contrast are dark versus light, thick versus thin, contemporary versus traditional, and large versus small.



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Figure 9.1  An example of an imagery graphic showing title, focal point, interpretation, orientation, and sourcing [1].

Figure 9.2  The location of a newly discovered probable underground facility at Lop Nor Nuclear Weapons Test Area in a map graphic combining vector and raster elements, complete with title, frame, time, north arrow, legend, and scale bar [1].

interpretations that provide context to geospatial graphics (both imagery and map graphics). Figure 9.3 provides an example of a map graphic with bordering infographics.

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Figure 9.3  An example of a map graphic with bordering infographics [2].

9.2.5.2  Geospatial Graphic Organization

The main principle of effective graphic composition is to visually introduce the reader to the location of interest and then follow with more detailed visualization of specific imagery or spatial analysis results. This principle holds for products of any size, from individual graphics to a report containing multiple graphics. Organization of graphics may be further categorized according to the classic geospatial concepts of area (overview), point (facility and/or equipment), and line (both spatial and temporal). This is especially effective for organizing multiple graphics in longer reports. Area graphics show the broad area context surrounding more specific targets of focus. Given the scale, often tens of kilometers, area graphics often include imagery base map or map backgrounds. Further, to orient the reader, it is standard practice to include a map overview inset on individual graphics and an area overview graphic in reports with multiple imagery graphics. Point graphics detail the specific target of the practitioner’s geospatial research, usually focusing on facilities and equipment. Line graphics relate points and areas in space and time. Spatial line graphics include line-ofcommunication (LOC) overviews that show how certain man-made infrastructure, such as a road, connects points (facilities and/or equipment) to each other or within and between broader areas. Temporal line graphics show sequences of movement within and between facilities, typically as part of a process. 9.2.5.3  The Four Cornerstones for Geospatial Graphics

The Four Cornerstones of location, color, shape, and context add precision to visualizations in a manner that greatly improves geospatial communications. Location information must be featured prominently in a geospatial communication and could be established generally on maps or specifically through the



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use of geocoordinates. Color and shape should be displayed prominently, as they are the most important observational features and may leave a lasting impression. Context then connects individual graphics to broader location-based assessments; this is usually done through the effective use of titles, insets, callouts, and analytic notes on geospatial graphics. For example, Figure 9.2 is a geospatial graphic that illustrates the use of location, color, shape, and context in a combination of imagery and maps. Note how this graphic efficiently incorporates the following elements: location, time, and entity; title, focal point, interpretation, orientation, and sourcing; and areas, lines, and points. Also note how a practitioner can interpret the graphic using the Four Cornerstones of location, color, shape, and context. Figure 9.2 highlights the fact that it is a completed geospatial graphic that illustrates the use of location, color, shape, and context in a combination of imagery and maps [1]. 9.2.6  Presentations

Verbal geospatial communications can be a highly convincing component of a finished geospatial communication. Spoken words are effective for persuading an audience and receiving their feedback. However, the spoken word alone is ephemeral, and is best integrated with durable visual and written modes of communication, as the sum of the whole is more compelling than the separate parts, and different methods achieve in concert that which each alone cannot. This integration creates the most compelling form of geospatial communications: the geospatial presentation. Geospatial presentations are live exchanges between the presenter and the audience and present opportunities for immediate feedback for an assessment. 9.2.6.1  The Geospatial Presentation

Fear of public speaking is common, and the individual speaker may either detract from the presentation or enhance it. However, unlike other forms of public speaking, the geospatial presentation contains objective, visual information that naturally relates to audiences for three reasons. First, the visual nature of geospatial information appeals to most of the audience’s innate visual learning tendencies. Second, audiences can easily understand the accuracy of locational information and may more easily accept it as an objective form of information. Last, the powerful combination of visual and locational information minimizes the number of words needed to relate to the audience. Whether presenting satellite imagery, maps, applications, infographics, pictures, or videos, geospatial information is a powerful and convincing accompaniment to presentations. During a presentation, no slide or graphic can ever take the place of the presenter, who takes center stage as the main storyteller and uses graphics in a

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subordinate role. In order for the presenter to deliver their best performance, the following elements should be perfected: preparation, presentation, and post-presentation. 9.2.6.2  Preparation and the Four Cornerstones

The preparation phase is the vitally important first step: proper preparation prevents poor performance. The presenter must begin to prepare the presentation by framing, centralizing, and supporting the assessment, and conceptualizing how one will keep the audience engaged and informed. The preparation phase takes the most time and involves building, formatting, improving, and practicing the presentation. The first step is to build the assessment slides. Assessment slides are geospatial graphics, so they must have a clear focal point, properly orient the audience so that it is their primary reference point (position), clearly display the assessment in the title and interpretations, and show sourcing information. Further, geospatial presentations must highlight the Four Cornerstones: make location information prominent, demonstrate color and shape, and use imagery and/or maps and text to orient the audience to broader locational context. When possible, create visual contrasts; for example, choose dark backgrounds that fall away and allow the brighter reference materials to stand out. For the focal point, choose colors that highlight or orient the audience towards the assessment and allow for a visually pleasing synthesis if one is displaying multiple data sources. If displaying imagery, only select the portion of the image that aligns with the story and craft the most desirable orientation, resolution, and zoom level. If it takes multiple portions of the image, use insets and callouts to achieve the goal. Build towards the introduction slides and then towards the conclusion slides. Finally, build the outlook slide and finish with the title slide. Next, the presenter should begin practicing the vocal accompaniment of the working draft. One should start silently and go over a notional version of the vocal portion. As one reviews the slides, take notice of the fact that many of the slides will need moving, improving, and adjusting. Do not worry about timing or transitions yet; instead, get a feel for the main point that needs to be delivered for each slide. Silent practice helps to organize one’s thoughts and is the first step towards the presenter finding their voice in preparation for the live audience. One of the most important principles of perfect delivery is vocal practice. This allows the presenter’s brain to connect the thoughts to the voice for the first time. The presenter may notice vocal pauses and fillers that one does not hear in one’s head during practice in silence. Sounds like “er” and “um,” the word “like,” and phrases such as “you know,” “sort of,” “kind of,” and “I mean” should be avoided at all cost. As the presenters finds their voice, they become



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more confident and begin to emphasize the right words at the right time. This is one of the ways that presenters perfect communication to an audience. Next, the presenters should begin to practice the vocal portion out loud to work on timing, transitions, and oral delivery. This helps the presenters to test their knowledge of the slides, especially transitions between them. Once ready, the presenters should welcome peer review by inviting someone to act as the audience so the presenters can deliver it to a live person. Once the presenters makes final adjustments and are consistently delivering the desired effect, they are ready to move on to the dress rehearsal. The dress rehearsal should replicate the final briefing as closely as possible, matching the time, location, and technical setup. This helps to reduce any fear or nervousness that the presenters might feel about the presentation. Additionally, this is the presenters’ last chance to assess technical issues, timing, props, volume, purposeful pauses, water management, and other last-minute adjustments. The presenters should scan the room as they present, ensuring that they can see all of the audience and that their voice is reaching them. Once complete, the presenter should solicit feedback from the practice audience, internalize final feedback, and implement final adjustments. 9.2.6.3  Presentation

Presentation is the second step and represents the culmination of the presenters’ geospatial communication. It is the point at which the presenters’ geospatial assessment reaches the customer; this step can be the most intimidating, yet also the most fulfilling. As a best practice, the practitioners should divide their presentations into “Introduction, Body, and Conclusion” sections. Introduction

As the presenters step out on stage, they should scan the room and try to make eye contact with the audience. Situate the water, computer, phone, and props and then take control of the clicker (if available). Depending on the audience and time allotted, the presenters may choose to begin the introduction with a geographic or geospatial attention gainer (or hook) to connect with the audience and earn their attention. Give a cursory introduction and then begin with a location-based attention gainer, such as: • “Who traveled more than 100 miles to get here?” Then allow a locationbased dialogue to ensue. • “Where is your favorite place on Earth? What things at that location make it the best?”

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• “Who allows their cell phone to track their location?” This should spark a brief but interesting dialogue about geospatial data, tracking, security, apps, and privacy. This will break the ice and implant the importance of location. Once complete, the presenters should formally introduce themselves and the presentation’s title. Pause for 3 to 5 seconds for audience comprehension and let the anticipation build. If there is an overview slide, advance the slide and outline the entire presentation with bullets driven by animations so each bullet builds one at a time and fades once the next builds. This will capture the focus of the audience on one point at a time. Try not to use more than five bullets on any slide. When ready, advance the slides from the introduction to the body. Body

In the body section, the presenters will be displaying the main point of the presentation: the assessment. Here, one can capitalize on the principles of slow thinking and strategic pauses. Use slow thinking to slow the oral delivery. Fast thinkers and speakers may be surprised to learn that they can halve their cadence and deliver perfectly to an audience. A strategic pause is a 5 to 10-second use of silence for effect that focuses the audience and allows them to single-task while absorbing the visual aids. Strategic pauses are especially effective during geospatial presentations because geospatial graphics awaken the big data sensor and captivate the mind. Allow the audience to absorb the compelling material and to allow the graphics to speak for themselves. Because the presenters have prepared presentation graphics with specific focal points, they can use strategic pauses to allow the audience to engage with the material. Use strategic pauses at the beginning and end of each slide to allow the audience to single-task and immerse themselves in the visual presentation. When the presenters transition to a new slide, they should pause for 5 to 10 seconds to gather thoughts and allow the audience to visually engage with the slide. While they observe, the presenters should orient themselves, breathe, and recall the message. The presenters can observe the audience during this silent period to gauge their attention and become familiar with their faces. Next, deliver the message with confidence from the title of the slide to the graphics and interpretation. Once complete, strategically pause again for 5 to 10 seconds. Repeat this process throughout the body section. Presenters can also employ strategic questioning to enhance the presentation. Build in questions for the audience to facilitate interaction and to give the presenter a chance to drink water and rest the voice. These techniques are key when presenting to an audience, and one can perfect them with practice. Continue these processes until one reaches the conclusion section.



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Conclusion

Finally, advance the slide to the conclusion section and provide the audience with an outlook slide that summarizes the main points and/or forecasts what they can expect next, where they can go if they want more information on the subject, and the presenter’s contact information. Open a question and answer session. If there are questions, make eye contact, repeat the question, speak loudly, and use the entire room to move closer to the audience. If there are no questions, consider preparing questions in case the audience does not immediately comment or ask questions. Once completed, thank the audience for their time and finish. 9.2.6.4  Post-Presentation

The last step, post-presentation, is the most important for self-improvement and the future of the assessment. After the presentation, remain available for those who want to ask more questions. Begin to internalize the strengths and weaknesses of the performance. Ask those who attended for feedback. Hand out, attach, or offer electronic surveys or feedback mechanisms. As soon as possible, post or email the slides for the audience to consume and follow up if possible thanking them for their time and providing links or opportunities for follow-up. Revisit the slides and make improvements based on feedback. As new information arises, update the slides to maintain their accuracy. Read comments, surveys, and feedback to continually improve. Finally, practice the presentation as the story evolves and improve the craft of presenting. 9.2.7  Communicating Uncertainty

All assessments are bounded by a frontier of uncertainty. Uncertainty is the condition in which there are gaps in knowledge. Open and accurate communication of uncertainty in assessments sketches these boundaries clearly for an audience, which builds trust in the overall assessment as it also frames areas for future research. Communication of uncertainty often requires estimative terms of probability and likelihood: probability that a proposition is true, and likelihood that an event will occur. Terms of probability and likelihood communicate the extent to which knowledge is bounded by various limitations. Uncertainty is common in geospatial observations and analysis due to spatial, temporal, and technical limitations. Spatially, observers are likely some distance removed (remote) from the object of focus and are relying on fallible sensors. Temporally, there may have been a passage of time since the object of research was collected and processed, or one is attempting to predict an event that has not yet occurred. In the technical realm, the limitations of equipment, hardware, and software can present conditions that render uncertainty in one’s observations and analysis. The most successful geospatial communicators factor

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in these limitations when crafting their geospatial communications and reflect uncertainty with the proper descriptors and caveats. Because geospatial communications can lead to lethal military and public safety operations or massive investments in industry, properly communicating uncertainty is of the utmost importance. Yet communicating uncertainty with clarity is a major challenge for practitioners. Assigning specific estimative language can help to meet this challenge. Specific estimative language can mitigate the effects of uncertainty on decision-making and improve the range of possibilities and choices. In this way, acknowledging limitations of knowledge (i.e., uncertainty) through specific estimative language frames gaps in one’s knowledge and sharpens one’s assessments by reducing total uncertainty to a range of known possibilities. 9.2.7.1  Uncertainty Language

Uncertainty language consists of categories and specific words that will help practitioners to express their uncertainty in text and vocal geospatial communications. This section presents three categories of uncertainty language: descriptive, estimative, and confidence. Descriptive

Descriptive language helps to frame and mitigate uncertainty by communicating one’s observation of the observational attributes of an entity. The strength of observational descriptive words lie in their closeness to objectivity (i.e., multiple subjects can independently agree on the description) and their breadth of everyday usage (i.e., communication universality). Descriptive language is used to describe the observational attributes of entities such as location, size, shape, color, texture, and shadow. Words from the descriptive language family include “blue,” “red,” “large,” “small,” “round,” “pointy,” and “square.” Descriptive words can be used in lieu of an entity assessment when the object is not yet identifiable, as a complement to an assessment once partial identification is achieved, and even as an addition to an assessment when certainty is achieved. Estimative

Estimative language is used to caveat the classification, identification, and analysis of an entity beyond that which is readily apparent or the likelihood that the event will occur. Estimative words fall into the subcategories of probability (probable, possible) and likelihood (less likely, more likely, highly likely). The use of estimative language conveys degrees of uncertainty in identifications, assessments, or predictions. Categories in the estimative word family can help to communicate degrees of probability that a proposition is true or the likelihood that an event will occur. Estimative language is often referred to as “caveats”



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because they provide a warning to the reader about certain stipulations, conditions, or limitations. The author should use caveats to convey uncertainty. Probability words are a word type in the estimative family such as “possible” or “probable” that caveat an identification, assessment, or prediction and communicate uncertainty. “Probable” is generally used to communicate greater than 50% chance of a proposition being true, and “possible” generally communicates between 0% and 50% chance of a proposition being true. While the word “possible” conveys less than 50% chance, authors should still use it to convey that the plurality of the evidence, albeit scant, points to the selected proposition. In other words, a “possible” silver pistol may only have a 45% weight of evidence supporting that assessment, but all other categories of evidence are weighted at 30%, 20%, and 5%. The relative weight of the evidence thus points to the identification of the object as a silver pistol.5 Other probabilistic words include modal verbs such as “may,” “might,” “can,” “could,” and “would,” and other verbs such as “suggests,” “indicates,” and “reveals.” Likelihood terms are a word type in the estimative family that also convey the author’s assessment of the chance that an event will occur. These include phrases such as “almost no chance,” “very unlikely,” “unlikely,” “roughly even chance,” “likely,” “very likely,” and “almost certainly.” Likelihood words should be used to caveat assessment predictions. The author of geospatial communications may include estimative verbs such as “suggested,” “indicates,” “revealed,” “appeared,” and “showed,” to communicate the level of uncertainty between a source of information and an assessment. “Suggests” communicates the most uncertainty and communicates that heavier analysis and reason are required to link the source data to the assessment. “Indicates” implies less uncertainty and communicates that lighter analysis and reason are required to link the source data to the assessment. “Reveals” can communicate some uncertainty or certainty in the analytic link between the source data and the assessment. When communicating uncertainty, it is used when an unobservable motive or explanation is derived from observable entities, events, or phenomena.6 “Appears” is used to convey visual uncertainty when connecting the source data to the assessment. It is used to caveat an assessment that one can see, but may contain some uncertainty.7 “Shows” implies little to no uncertainty between the source of information and the assessment and is used to communicate that the source data visually, empirically, or axiomatically demonstrates the assessment. “Shows” is used to communicate an 5. A possible silver pistol cannot also be a probable black shotgun. 6. For example: “Russian tanks driving south towards Crimea in battle formation revealed Russian intentions to invade.” When denoting certainty, it is used to describe something being suddenly made observable, for example, “The Russians removed the tarp, revealing a T-55 main battle tank.” 7. For example: “The T-55 main battle tanks appeared to be operational based on their turret positioning, formation, speed, thermal signatures, and crew disposition.”

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irrefutable visual connection between the source information and the assessment.8 “Shows that” implies little uncertainty between the source data and the assessment, but implies some element of analysis or reason separating the two.9 Once the author has become familiar with estimative language and separated it into categories of words of probability and likelihood, the next step is to measure the degree to which they represent uncertainty and rank them on a scale. Figure 9.4 shows the strength of uncertainty words and provides a menu from which to choose the words of estimative language that best represent one’s uncertainty. While these words cannot have perfect quantitative assignment, this chart presents a best attempt to visually represent the strength of their meaning. Confidence Levels

Confidence levels is language that authors use to caveat assessments when there is uncertainty regarding the quality of source information underpinning an assessment. Authors should use confidence levels to caveat assessments when their audience or style guide requires the communication of the quality of source information and to clearly communicate levels of uncertainty in the assessment.10 Figure 9.5 presents uncertainty language in the descriptive, estimative, and confidence categories. When using a confidence level, separate or distance it from the estimative language in order to provide the most clear geospatial communication to

Figure 9.4  The strength of uncertainty words, along with a menu from which to choose the words of estimative language that best represent one’s uncertainty.

8. For example, “The video shows John Doe at the crime scene.” 9. For example, “The video evidence shows that John Doe is the suspect.” 10. For example, “We assess with moderate confidence that North Korea is preparing to test a nuclear weapon.”



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Figure 9.5  Uncertainty language in the descriptive, estimative, and confidence categories.

the audience. Mixing confidence levels and estimative language serves to create more uncertainty and confusion. If one has estimative and confidence language in nearby sentences or in the same summary, ensure they are separated to maximize clarity.11 Confidence levels are covered in more depth in Section 9.2.8. 9.2.7.2  Uncertainty Words: Workflows and Examples

The following is a sample workflow beginning with a geospatial observation, then analysis, and finally communication using descriptive, estimative, and confidence language: 1. Observe an entity. (a) Use object recall, and object and attribute differentiation to identify the entity with certainty: i. Use affirmative uncaveated language to communicate the assessment to an audience. ii. Use descriptive language (of location, color, shape) if necessary to show your work. If not able to identify the entity, go to step 2. 2. Analyze the entity. 11. For example, “We assess with moderate confidence that North Korea is preparing to test a nuclear weapon. Our assessment is based on communications intercepts during preparations, human source reporting stating a test was imminent, and medium-quality satellite imagery showing possible preparations at a site involved in the North Korea nuclear weapons program.” Not “We assess with moderate confidence that North Korea is possibly preparing to test a nuclear weapon.”

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(a) Compare observation with other observations of imagery, maps, identification keys, or documents: i. If not able to identify with certainty, use descriptive language (of location, color, shape, context) to communicate features and estimative language in a caveated assessment. ii. If able to identify the entity, identify the object affirmatively within an assessment. Then go to step 3. 3. Analyze relations. (a) Analyze the relationships between the primary and related entities. (b) Execute steps 1 and 2 as necessary. 4. Analyze context. (a) Analyze the broader spatial, temporal, and collateral context. (b) Use this context to frame a finished geospatial communication. (c) Decide whether the finished geospatial communication requires confidence language based on the reporting publication requirements and customer expectation. Examples of Geospatial Communications Using Uncertainty

A geospatial communication using the descriptive language might read: “Analysis of imagery revealed a large, square-shaped, dark-toned object on the highway facing south.” A geospatial communication using the estimative language of probability might read as: • “Analysis of imagery revealed a probable T-55 tank on the highway facing south.” • “Geospatial analysis of homicides in Baltimore revealed that densities of homicides shifted from neighborhood X in the spring to neighborhood Y in the summer, possibly due to gang Z shifting their narcotics operations to neighborhood Y.” A geospatial communication using the estimative language of probability with only modal and other verbs might read as: “Analysis of geospatial ecological data suggests that birch trees in Planting Zone 3 should be planted in March, but could also survive if planted in April.” A geospatial communication using the estimative language of likelihood might read as: “Analysis of seismic and ground penetrating radar revealed that the void is unstable and will very likely collapse if not immediately attended to.”



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A geospatial communication using descriptive and estimative language and likelihood might read as: “Analysis of geospatial information revealed a large, probable mineshaft behind the recently closed mineshaft, indicating that Country X is likely continuing mining operations.” A geospatial communication using a confidence level might read: • “The Department of Defense assesses with high confidence that Country X is mobilizing for war.” • “Global Threats assesses with moderate confidence that Country X will invade Country Y before the end of the dry season.” 9.2.8  Geospatial Confidence Communication

Geospatial confidence communication allows practitioners to measure the quality of the evidence that will form the basis of their assessment and then clearly communicate this assessment to an audience. Use of this framework involves promoting the visual and locational elements of one’s evidence to a central role, assigning it levels of confidence, and then integrating either estimative or confidence language into the assessment to ensure transparency. Preparing geospatial confidence communications corresponds to the structured analytic technique (SAT) “quality of information check,” which requires the practitioner to assess the quality of each source in their work according to a standard. However, it expands upon this SAT by building a geospatially focused information quality assessment model, complete with language to accurately communicate this assessment. Geospatial confidence communications have one guiding principle: quality of evidence determines confidence. The use of confidence levels differs from a subjective assertion of confidence by an individual trying to convince others of a point of view. The geospatial confidence model outlined below starts by outlining quality levels for visual and spatial data, then applies this to observations and analysis, and delivers it in communications. This model borrows from various United States Intelligence Community documents, which separate confidence levels into three categories: low, moderate, and high [3]. 9.2.8.1  Geospatial Confidence Levels

Practitioners can use the following confidence levels to describe the quality of their geospatial evidence: • Low confidence: Low-quality visual and/or spatial data that yields varying analytic processing and mostly subjective interpretation;

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• Moderate confidence: Moderate-quality visual and/or spatial data that yields consistent analytic processing that has a plurality of objectively similar interpretation; • High confidence: High-quality visual and/or spatial data that yields analytic processing and interpretation that is almost uniformly or fully validated by rigorous peer review. 9.2.8.2  Quality of Visual Data (Including Imagery)

Low-quality visualizations, including those on imagery or video, are visualizations in which the observer can interpret the existence or shapes of certain larger entities but cannot identify or classify them more specifically. For example, the observer could interpret the shape of a large vehicle, but cannot identify the vehicle type. One can also interpret the outlines or existence of other large entities such as airfields, lakes, manufacturing plants, and buildings. Moderate-quality visualizations, including those on imagery or video, are visualizations in which the observer can interpret the shapes and larger attributes of larger entities and broadly identify certain smaller entities. For example, observers can interpret the size and shape of the vehicle such that they can identify the broad classification of vehicle type (truck, car), building (apartment building, single-family home, rowhouse), and even the outline or shape of a person, but cannot identify specific details of the vehicle (model) or person (height, sex, clothing details). One can also interpret the larger equipment in manufacturing plants, infrastructure elements, vegetation, and terrain features. High-quality visualizations, including those on imagery or video, are visualizations in which the observer can interpret the details of a vehicle and identify the vehicle make and model and positively identify a person and his or her more detailed attributes. One can also interpret smaller pieces of equipment and terrain features, especially the groupings of indicators or signatures that identify entities. 9.2.8.3  Quality of Spatial Data

The quality of spatial data depends on many factors such as the quality of sourcing, locational information, accuracy of temporal information, attribute data, and geoprocessing tools. Most important is the quality of the location data, which relies on accurate sensor calculations and map projections. Quality is also reliant on locational data validation, formatting, and completeness. Next, if a timeframe is required, the quality of the temporal data must be accurate, complete, and contain the proper date ranges. Finally, the attribute data must be accurate, complete, and relevant. Spatial data quality has follow-on implications: it allows or disallows follow-on geoprocessing and visualization. Low-quality spatial data may have imprecise and/or unvalidated locational data, vague or incomplete temporal information, and missing attribute data.



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It also may have characteristics such as missing or no sourcing, metadata, and item details. Once mapped, the points, lines, polygons, and pixels may tell a story that is confusing and interpreted in mostly subjective, differing ways. Moderate-quality spatial data may have more precise and/or validated locational data, mostly complete temporal information, and attribute data. It also may have characteristics such as available sourcing, metadata, and item details. Once mapped, the points, lines, polygons, or pixels tell a mostly coherent story that can be corroborated and interpreted objectively. High-quality spatial data has precise and/or fully validated locational data, complete temporal information, and robust and accurate attribute data. It also has characteristics such as readily available and fully transparent sourcing, metadata, and item details. Once mapped, the points, lines, polygons, and pixels tell a coherent story that is corroborated with other data and objectively interpreted. 9.2.8.4  Confidence Level Examples

Some low-confidence examples are: • An eyewitness provides testimony featuring a fleeting glance of a crime scene at night from a distance. • An imagery analyst conducts an observation of an entity on a low-quality image, numerous visual variables interfere with the observation, and the image can be interpreted in multiple ways. • A city planner sees shaded areas on an online map that purports to represent housing zones, but the map lacks sourcing information and contains no metadata. The shaded areas have a vaguely familiar distribution that practitioners can interpret in different ways. Some moderate-confidence examples are: • Two eyewitnesses observe the same crime and partially corroborate each other’s testimony. Criminal analysts at the police department also observe a series of clear, close-up photographs from the crime scene that reveal the suspect’s identity and location. • An imagery analyst makes multiple observations on moderate-resolution images or videos with few visual variables interfering with the observation. The images or videos are also interpreted clearly and similarly by the majority of peer reviewers. • A city planner finds a dataset on a municipality’s open data website with some sourcing information and metadata. The dataset seems to match

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other verifiable depictions of housing zones and tells a mostly coherent story of their locations, but has no date of publication. Some high-confidence examples are: • Numerous independently attesting and corroborating eyewitness accounts provide the same details of a suspect at a crime scene. Also, clear, focused video recordings and photographs emerge from the surveillance camera that show that suspect at the crime scene committing the crime. Digital data (GPS, cell phone) from the crime scene corroborates the location of the suspect at the crime scene. • An imagery analyst conducts observations of entities on high-resolution images or videos with few to no visual variables interfering with the observation. The image is able to be interpreted clearly by the practitioner and the peer reviewers, and all are able to identify the entities as the same object. • A city planner uses a dataset in an assessment from a municipality’s open data website with excellent sourcing information and metadata. The dataset is verified by the data owner, and it tells a coherent story of the location and subordination of housing zones within the city that is then verified objectively by other city planners. Once practitioners have drafted versions of text and graphics and decided on the proper estimative language and/or confidence levels to use for their assessment, they are ready to build the final product. 9.2.9  Building the Product

Now the practitioners must put the aforementioned techniques together and build the product. This process begins by choosing product types and understanding general product organizational structures. The practitioners’ production strategy should begin with an efficiently worded geospatial assessment. Then practitioners should look outward towards the audience to determine the audience’s receptive requirements. The practitioners should evaluate which product type plays to the strengths of one’s evidence basis and assessment in order to best drive the intended purpose. Authors must balance selecting the product type that most effectively communicates the assessment with the product types available to them in their organization. The selected product types will focus on those that highlight geospatial analysis and assessments. Many organizations have production suites that include set products or templates such as:



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• The remark (simply constructed text and graphics of varying length); • Executive summary (single paragraph of text); • Intel note (single paragraph chapeau and bullets accompanied by a single slide with text and graphics); • Analysis report (multiple paragraphs, text, and graphics). Each product type has a different structure, length, and sourcing expectation (i.e., single-source versus multisourced), but products that require multiple paragraphs usually follow the basic framework of an introduction, a body, and a conclusion. Each of those sections also requires a specific structure, examined next. 9.2.9.1  Introduction

The introductory paragraph is the first paragraph in a paper that introduces the reader to the subject and provides the most important elements of information. One of the most common introductory paragraph headings used for geospatial communications is the summary paragraph. The summary paragraph should lead with location and use the bottom-line-up-front principle. An effective construct for creating a summary is an observation-based approach: lead with location and present the most important observations first, explain why they are important, and then provide an observation-based forecast that frames future research expectations. Colloquially, this is known as the what, so what, what next method. Here is an example of that construct: “[Organization] identified extensive facility construction and infrastructure expansion throughout the Lop Nor Nuclear Weapons Test Area (also known as Lop Nor) during 2019–2021, including new construction areas that are linked to existing nuclear test support facilities. These observations indicate that China is significantly investing in its Lop Nor Nuclear Weapons Test Area, and may be preparing for future nuclear weapons tests, which would mark a new phase in the modernization and/or expansion of China’s nuclear weapons stockpile. Additional monitoring of these developments is necessary to determine the function of these changes, and all assessments of the Lop Nor Nuclear Weapons Test Area should be further compared with those of other nuclear weapons-related facilities in China to understand how these fit within China’s nuclear weapons research, development, and production programs.”

Another effective construct for the summary is the assessment, basis, forecast construct. Authors should begin by stating the assessment, then reveal the basis (evidence and sourcing), and finally forecast what will happen next. Similar constructs are common when making broad assessments that require

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confidence levels, and frequently appear in U.S. government documents. The authors assess using the proper caveats to communicate an appropriate level of certainty for their analysis, reveal the basis for this assessment, and then forecast what to expect next to frame subsequent research expectations. Authors can also expand the basis to include both the evidence and source quality. Many basis statements only include the source quality, but the addition of evidence details will provide more transparency to an audience. Here is an example of the assessment, basis, forecast construct: “[Organization] assesses with high confidence that China is significantly investing in infrastructure upgrades and expansion at Lop Nor Nuclear Weapons Test Area. Our assessment is based on GEOINT analysis of highquality imagery of new roads, electricity lines, and facility construction; multiple high-quality open-source media reports; and a high-quality opensource United States government assessment published by the Department of State. [Organization] expects China will continue infrastructure upgrades and expansion at Lop Nor during the next year.” 9.2.9.2  Body

A body paragraph proceeds the introduction paragraph and contains the supporting evidence and details that support the introduction. Many reports that feature geospatial communications have a body that may have paragraphs with headings such as “Background” and “Analysis.” The Background section is the author’s chance to inform the reader about things that happened in the past in order to frame the current assessment. When using multiple sentences to describe a timeline of events in the Background section, authors can employ the timeline construct in either chronological or reverse chronological order. The Background section should not contain new analysis, only historical and geographical facts, and well-supported, substantiated, and corroborated historical assessments. Here is an example of a Background paragraph [4]: “Lop Nor (罗布泊) is a dried lake bed within a remote desert region located in the People’s Republic of China’s Xinjiang province, south of the provincial capital Urumqi and east of the city Korla. In the early 1960s, as China developed its strategic nuclear weapons program, it chose Lop Nor as the area to test nuclear weapons devices. There are four historical nuclear weapons testing areas in the Lop Nor region, and one possible new test area.”

The Analysis section should contain a more in-depth review of new assessments or findings. Each paragraph within an Analysis section should use the



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inverted pyramid method that leads with a strong topic sentence and follows with an elaboration of supporting evidence. Each topic sentence should be an assessment that leads with location and provides the bottom-line-up-front. The elaboration section consists of sentences that support the topic sentence. For efficiency, the authors should use the minimum amount of supporting evidence necessary to support the topic sentence and then: “Analysis of geospatial data from July, August, and September 2021 showed construction of probable utility poles for carrying electricity transmission lines from an electricity substation area at 41.6265 88.3564, running east along a main facility road past the Nuclear Test Area Headquarters and towards the probable Vertical Shaft Test Support Facility. Analysis of Planet imagery from 26 July 2021 showed construction of utility pole footers near the Eastern Possible Future Test Area, running along the 2021 graded road west towards the probable Vertical Shaft Test Support Facility. This electricity transmission line construction links Lop Nor’s new Eastern Possible Future Test Area to established electrical power infrastructure serving the Vertical Shaft Test Support Facility and the Nuclear Test Area Headquarters.”

Figure 9.6 is the accompanying graphic for this text that refers to electrical power infrastructure improvements at Lop Nor. 9.2.9.3  Conclusion

The conclusion is the final section of the paper. It can consist of paragraphs with an “Outlook” or “Conclusion” heading, or many others. Many geospatial analysts prefer to use Outlook sections because they allow an analyst to forecast

Figure 9.6  Electrical power infrastructure improvements at Lop Nor [1].

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what will happen next, explore alternative explanations, and fulfill other specific customer requirements forecast towards possible emerging threats and decisions. Outlook sections also do not have any of the repetition found in many standard Conclusion sections. Here is a sample Outlook section: “China’s infrastructure improvements and new probable underground facility at the Lop Nor Nuclear Weapons Test Area may be intended to support some type of nuclear weapons test-related activity. In particular, improved transportation infrastructure facilitates transfer of material through the Vertical Shaft Testing Area towards the newly constructed areas in the east. While this new construction may include a nuclear weapons test-related tunnel, additional information and analysis is needed to bolster this characterization.   “Alternatively, China’s infrastructure improvements and new underground construction areas could be related to environmental remediation efforts at Lop Nor, including possible nuclear contamination testing and storage. However, such remediation would probably entail additional and varied construction efforts, such as concrete capping of existing underground nuclear device test locations and nuclear test site decontamination and/or decommissioning. There has not yet been indication of this type of activity. Additionally, the excavation of a new underground facility to store contaminated materials from previous nuclear tests would also entail excavating and transporting contaminated surface soil from historical nuclear test areas to the new storage area. There has not yet been indication of this type of activity. Therefore, while environmental remediation cannot yet be ruled out, this currently seems an unlikely goal of recent infrastructure improvements and new underground construction efforts at the Lop Nor Nuclear Weapons Test Area.   “China’s infrastructure improvements at the Lop Nor Nuclear Weapons Test Area continue a long-term pattern of improvements and expansion at several Chinese nuclear weapons-related facilities spanning multiple parts of the nuclear weapons cycle. Changes at the Lop Nor Nuclear Weapons Test Area should be compared with those of other nuclear weapons-related facilities in China to understand how these improvements fit within China’s nuclear weapons research, development, and production programs.” 9.2.9.4  Title: Circling Back

Once the author has completed the entire paper they should then focus on the title. The title is the first thing people read, and it begins their engagement with the product, including the decision whether or not to further engage. A great title of a geospatial communication should lead with location, then an assessment, and then a forecast. Here is a sample title: “Expansion of Nuclear Weap-



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ons Testing in China: A Review of Chinese Efforts to Expand the Lop Nur Test Area from 2019–2021.” 9.2.9.5  Product Type Selection

Practitioners should select or create the product type that most efficiently and effectively conveys the assessment to the audience. Product types, such as the examples featured early in this section, can range from more simple to complex. (Regardless of type, keep in mind that all products should include graphics, because visualization is one of the key strengths of geospatial production.) The following examples demonstrate three levels of product type: simple, moderate, and complex. Product Selection: Simple

A customer sends a request for information and a practitioner is tasked with providing an answer (direction origin; deductive approach). The practitioner collects the data, conducts observations and analysis, and solidifies an assessment. The practitioner then must choose a product type that most effectively and efficiently conveys the assessment and decides on a simple “Slide with Analyst Notes.” The practitioner-turned-author must then follow the organization’s style guide and create a slide with a title, a graphical focus, and interpretation in the form of a tone box with analyst notes containing the assessment. Product Selection: Moderate

During a practitioner’s daily data research duties, one conducts geospatial observations and analysis that reveal an alarming trend. The practitioner determines that it should be shared with the organization’s leadership (discovery origin; inductive approach). One conducts more research and derives an assessment. One must choose a moderate product type that most effectively and efficiently conveys the assessment. The practitioner decides on an “Intel Note” to fulfill the customer’s need to see a graphic and a write-up with a moderate amount of detail. The Intel Note allows the author more writing space to convey more complex thoughts and supporting evidence, and either an embedded or a separate compelling graphic. The practitioner follows the organization’s style guide and creates a two-page paper with a title, a summary paragraph (or chapeau), supporting bullets, an outlook section, and a graphic. Product Selection: Complex

The practitioner is tasked with conducting a long-term research project and delivering an in-depth report of findings to a panel of industry experts. The practitioner conducts research that yields complex findings. The practitioner-

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turned-author must choose a product type that most effectively and efficiently conveys the findings, and one decides on a complex research paper. The author follows the organization’s style guide and produces an expansive multisection, multipage paper. The research paper begins with a title and an introduction section with headings such as “Abstract,” “Findings,” or “Summary.” The research paper then proceeds with a body section that includes such headings as “Background,” “Analysis,” “Results,” “Discussion,” “Methods,” or others depending on the field. The research paper ends with a conclusion section that may be titled “Conclusion,” “Outlook,” “Recommendations,” and others depending on the field or industry. 9.2.10 

Multilayered Peer Review for Communication

After completing a draft of any type of geospatial communication, practitioners must conduct a multilayered review including self, internal (including peer and supervisors), and external, in that order. Self-review should become an ingrained habit for the practitioner, as this leads to a more polished communication for peers to review. For writing, editing should focus on both style and substance. Important components include clarity of the assessment (can it be summarized in one sentence?), supporting text organization (does each paragraph support the main argument, and does each sentence support the topic sentence of each paragraph?), and copy editing to improve grammar and spelling. For graphics, the practitioner should start with a fresh mind, then see if each clearly expresses its stated aim. For graphics, consider if the main idea of each graphic is clear; weigh the use of space in the graphic (e.g., is it too crowded, and is the zoom level sufficient?); and assess the effectiveness of symbols and callouts for expressing location-based information. For presentations, test the hook used to grab attention, gauge the clarity of the overall message, and diagnose the smoothness of moving from point to point throughout the presentation. Internal review consists of peer and supervisory review of a communication is an important opportunity for testing an assessment and receiving feedback on the quality of observational, analytic, and communication tradecraft. Internal review consists of substantive revisions and copy editing and can be done by direct colleagues and those in the organization in peripheral fields. The practitioner should adopt a mindset of wanting critical feedback without feelings of pride or apprehension; regardless of others’ intentions, critical feedback will only improve the practitioner’s final work. Peer-reviewed products are the culmination of objective research processes that transform data to information and move from inside the practitioner’s mind into the world for others to observe and assess.



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External review consists of sending the product to counterparts and stakeholders who can improve the communication by providing feedback from varying perspectives. These are often colleagues whose expertise also intersects in relevance with the issue or who are stakeholders in the implications of the findings. External review should focus on substantive revisions. Such a review may take time, so one must balance speed and quality by allowing a deadline such as 1 week for external colleagues to review. Once the external reviews are completed, the geospatial communication should be well layered with review and ready for publication and dissemination. 9.2.10.1  Communicating Objectivity

One of the achievements of peer review is the attainment of objectivity. Thoughts inside a practitioner’s mind are located inside a single subject, where the word subject refers to an individual. In this way, individual thoughts are subjective. Thoughts that are refined over time, written and visualized as products, and presented to others become objects in the world, outside of an individual’s mind. To the extent that these objects are available for peer review and assessment, they are more objective, meaning that they are objects that may be perused by others and independently tested for veracity. The process of communicating a finished geospatial communication thus culminates the move from subjective suppositions to more objective propositions about the world.

9.3  Conclusion One iteration of the OAC framework concludes by achieving the goal of a geospatial communication: dissemination and exchange. If the practitioner succeeds in both, the assessment will ripple out through communications channels and may change the world. Further, communication in the form of publication followed by speaking, lecturing, and instructing are career and informationbroadening ventures that enhance the practitioners, their audiences, and the subject matter. The closer the audience is to the assessment, the tighter the feedback loop will be, and the more a practitioner will grow in analytic maturity, perspective, and understanding. Although communication is the final component of the OAC framework, it does not represent the end of the geospatial data-to-information refinement process. Far from complete, practitioners usually discover that their assessment was merely one puzzle piece in a lifelong journey of curiosity and research. If uncertainty persists, the OAC framework will exist as an open window into discovery and refinement for diligent citizen scientists or professional and practitioners alike. For as long as there are people and things, there will be fascinating places that can help humanity to unlock their meaning.

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References [1] Planet, Satellite imagery from July 20, 2021, Scene ID: 20210720_042626_ssc4_u0001. [2] ESRI, ArcGIS Dashboard in ArcGIS Software with Dark Gray Canvas basemap. [3] United States National Intelligence Council, National Intelligence Estimate, “Iran: Nuclear Intentions and Capabilities,” November 2007, https://www.dni.gov/files/documents/ Newsroom/Reports%20and%20Pubs/20071203_release.pdf. [4] Lewis, J., and L. Xue, China Builds the Bomb, Stanford, CA: Stanford University Press, 1988.

10 Outlook 10.1  Geospatial Advancement As technology becomes more available and accurate and the Information Age progresses, big data enriched with locations will continue to inundate humanity and require geospatial solutions. Therein, the value and importance of having a geospatial mindset, toolset, and skill set will increase. As new people and organizations adopt the location mindset and explore the opportunities that geospatial data, analysis, and information afford them, the collective geospatial user group will expand and evolve. More online and free resources will become available, and trade groups will form and flourish. Geospatial endeavors will become increasingly vital to business, academic, and government processes. Governments will expand and enact laws that increasingly commission, task, and rely on geospatial resources. Demand for better data and information will rise, costs will decrease, access will increase, and the private sector will thrive in supporting the government and population’s need for better data and information. As the need for instant, accurate, visual information increases, geospatial data, analysis, and the associated career fields will increasingly answer that demand. As the United Nations, the United States, and many other countries create laws, mandates, and publications highlighting the benefits of geospatial data and information, it will grow in popularity and utility. For example, as the Geospatial Data Act ages and the effects ripple through the U.S. government and the World Wide Web, more geospatial jobs and geospatial data will be created and shared. This data will be posted on public websites and used worldwide by a huge variety of customers for issues involving weather, climate, public health, 215

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transportation, security, and an increasingly vast list of issues. Local governments will also host and share more geospatial data on open data websites to increase transparency, improve efficiency, and allow others to create and present models and solutions. Private sector websites will host and share more content to fulfill legal requirements or to attract more customers. This collaboration of government, private sector, academia, and citizenry has great potential to unite humanity with a common geo-enriched operating picture.

10.2  Visualizing the Next Geospatial Horizon The future of geospatial data is one characterized by speed, availability, and accuracy improvements. The speed at which geospatial data is created and transmitted will be improved by computing and transmission enhancements. Geospatial data and products will evolve from more static and historical to more streaming and real-time, from more 2-D to more 3-D, and from more systemspecific to more interoperable. Users will evolve from more isolated desktop computers to more web-based, interactive, mobile, and shared environments. These factors will increase the ability for practitioners and the general public to engage more in the data-to-information refinement process and will deliver vital geospatial information into the hands of worldwide customers in record time. More public and private sector websites will host geospatial data and information, making it more available than ever before. The availability of storage and software tools will continue to improve, and more countries and citizens will gain access. The availability of the geospatial data will improve as streaming data makes it into the hands of more users in more places. Governments and private companies will increasingly rely on geospatial data streams to provide them with real-time information. Dashboards, applications, and other innovative interfaces that host this data will become commonplace. Geospatial data will become more available in increasingly mobile environments. Mobile phones, vehicles, and smart devices will continue to expand, creating a dearth of geo-enabled entities that will enrich the landscape of devices that generate locational information. This information will become more available to both the users and the data collectors that wish to track the locations of others. Finally, accuracy will be improved over time as more devices come online and money is invested in the Earth and space-based systems that measure and calculate location. GPS, routers, cell towers, and all of the other electronic systems that help us to pinpoint location will improve in technicality and increase in frequency. Other smart devices that measure location will become increasingly interconnected and deliver more accurate locations. While the speed, availability, and accuracy of geospatial data are quickly increasing, meaning and insights derived from this data must be carefully



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gleaned with structure and clarity. The location mindset provides an approach, the systems, sensors, software, hardware, and people provide the toolset, and the observation, analysis, and communication techniques presented in the OAC framework provide the skill set required to succeed. Humans will remain the most important tool in the toolset, providing reason, nimbleness, and mentorship in ways that computers will never provide.

10.3  Location: A Central Feature of Our Future Humanity’s interpretation of the present and future through clear eyes is vital to the decisions that governments will make to shape the world. For even politics leads with location and varies according to locale, as captured in the popular phrase, “all politics are local.” The more humanity can unlock the powers of locational data and information to inform local meaning and clarify decisionmaking, the more this can positively affect lives, from local culture to international politics. This groundswell of clarity commences by guiding research questions down to earth and starting with the smallest, most absolute points and a location mindset. Everything that happens or exists on Earth does so in a location. Those minute locations contain outsized importance, as each is surrounded by concentric circles of relationships and context. Then, armed with a geospatial mindset, toolset, and skill set, each practitioner and citizen scientist can transform the data at those locations into information that can help humanity to paint the most accurate picture of the issues that face our localities, our cultures, our countries, and our planet. In the search for meaning, location is central to understanding the world.

About the Authors Aaron Jabbour has enjoyed a decades-long career as a geospatial analyst, geospatial team leader, geospatial supervisor, and geospatial information officer. Mr. Jabbour has worked for numerous public safety, national defense, and national security-focused organizations in the United States, Europe, and Asia. Mr. Jabbour has performed a wide variety of geospatial analysis and operations, spanning the tactical, operational, and strategic levels, and has won numerous awards for analytic tradecraft. Mr. Jabbour has traveled the world in search of the most perplexing puzzles and difficult geospatial challenges and strives to share those lessons learned with others. Mr. Jabbour has also lectured at geospatial conferences, instructed hundreds of students in various organizations, and has served as a mentor to employees. Mr. Jabbour has a passion for all things geospatial and finds it thrilling to solve for where and see the spark in others who do the same. Renny Babiarz is the vice president of analysis and operations for AllSource Analysis, where he manages and contributes to geospatial analysis projects for government, nongovernment, and commercial customers. He is also an adjunct faculty member for the Johns Hopkins MS in Geospatial Intelligence (GEOINT) program. Dr. Babiarz has a PhD in political science specializing in China’s nuclear weapons program from Johns Hopkins University, an MA in Asian studies specializing in China from the University of Hawaii at Manoa, and certificates in Chinese language and GEOINT analysis. Additionally, he worked in public service as a GEOINT analyst for the National GeospatialIntelligence Agency and has private sector research experience with Science Application International Corporation (SAIC) and AllSource Analysis.

219

Index

Aggregation, 135–37 Analytic extrapolation, 161 Analytic interpolation, 160 Analytic tools, 122 Appearance, entities and, 157–58 ArcGIS Online, 135–36, 137, 138–39 Areas, 71–73, 131–32 Argument mapping, 166 Attention, 79 Attribute data, 39 Attribute differentiation, 23–25 Audience assessment, 181–82

body, 208–9 conclusion, 209–10 introduction, 207–8 product type selection, 211–12 templates and, 206–7 title, 210–11 See also Structured geospatial communication techniques

Cerebral grid, 21–22 Change analysis, 128, 129 Change-over-time on GIS, 103 Change-over-time on imagery, 102–3 Cholera map (1854) example, 112–13, 114 Choropleth map, 135–37 Classification, entities related by, 157 Coherent change detection (CCD), 101–2 Collateral analysis, 154–55 Collection of data, 13, 42–43, 45–46 Color analysis of, 149–50 category, 73–74 communication of, 187–88 Color vision deficiency (CVD), 73 Communication analytic, 164–67 lines of, 128–31 See also Geospatial communication(s) Conclusion building the product, 209–10 presentation, 197

Body building the product, 208–9 presentation, 196 Brainstorming, 166 Broad area search (BAS) about, 95–96 analysis, 132 area, eye altitude, framing, 97–99 attention, significance, observation, 99–100 imagery-based, 96–100 observational notations for, 100 point target surroundings and, 131–32 search setup, 87 Buffers, 134, 139 Buildings related to locations, 148–49 Building the product about, 206–7

221

222

Geospatial Data, Information, and Intelligence

Confidence levels examples, 205–6 geospatial, 203–4 high confidence, 204 language, 200–201 low confidence, 203 moderate confidence, 204 quality of evidence and, 203–4 Construction, mental, 25–28 Content Standard for Digital Geospatial Metadata (CSDGM), 95 Context analysis of, 152–55 category, 75–77 collateral, 154–55 communication of, 188–89 examples, 112–13 principle, 112–13 temporal, 153–54 understanding, 112 visual, 152–53 Creating observable keys, 162–63 Danville murder case example, 1–3, 8, 18, 28 Deception, 59, 63 Deductive geospatial reasoning, 161 Description-to-image linking, 144–46 Detail observation, 79 Devil’s advocacy/steel manning, 166 Differentiation, object and attribute, 23–25 Dimension, 80 Direct observations, 55 Direct sensors, 44–45 Disinformation, 63–64, 65, 103 Dissemination, 4, 172, 179, 182, 213 Distance (zoom and scale), 80–81 Distilling communications, 180–81 Documentation, observational, 89 Electronic Light Tables (ELT) about, 4, 13 illustrated, 14 imagery analysis tools and, 123 visualizations, 41–42 Entities appearance and, 157–58 classifications, 155–59



finished geospatial communications and, 177 identification, 110–11 measurements and, 158–59 relationships, 155–59 space and, 155–57 time and, 157 Entities analysis about, 146–47 buildings, 148–49 of color, 149–50 of context, 152–55 as foundational, 147 humans, 148 of locations, 147–49 shadows and, 151 of shape, 150–52 size, 151 texture and, 151–52 vehicles and vessels, 148 Equipment, 126–27 Estimative language, 198–200 Exchange, 172, 174, 193, 213 External communication, 90 External review, 165–67, 213 Facilities, 124–25 FAIR, 34 Finding locations, 141–42 Finished geospatial communications about, 176–77 entity and, 177 foundations of, 176–78 location and, 177 sourcing and, 177–78 time and, 177 unfinished geospatial communications versus, 175 Fixed point target, 124–25 Focal point control about, 82 hard focus, 82 rest, 83–84 revisit, 84 shifting focus, 83 soft focus, 83 See also Structured geospatial observation techniques (SGOTs) Focused attention, 56



Index Four Cornerstones about, 68 analyzing entities using, 146–55 color category, 73–74 context category, 75–77 example, 78 of geospatial graphics, 192–93 for geospatial text, 186–89 illustrated, 69 location category, 68–73 presentation preparation and, 194–95 shape category, 74–75 use of, 78 See also Structured geospatial observation techniques (SGOTs) Functional relationships, 159 Galilei, Galileo, 65–66 Geocoordinates, 31–32, 34, 41, 46–47, 57–58, 70 Geographic Information System (GIS) about, 4, 13 change-over-time on, 103 color and, 150 help information, 64 illustrated, 14 uploading data to, 133 visualizations, 41–42 Geolocation, 22, 134 Georeferenced imagery, 37 Geospatial advancement, 215–16 case for, 1–5 defined, 3 information, 4, 42 intelligence, 4 mindset, 1–8 Geospatial analysis about, 3 baselines, 160 conclusion, 167–68 context and, 112–13 defining, 108 delineating, 4–5 foundational principles of, 109–15 identification and, 110–11 imagery analysis, 116–17 location initiation of, 18–19 methodologies, 115–17

223 practices, 119–68 principles, 107–18 as a profession, 120–40 purpose of, 108–9 questions propelling, 108–9 relation and, 111–12 SGATs, 141–67 spatial analysis, 117 spatial and geospatial thinking and, 12 spatial and imagery, 4–5 as subdiscipline, 6 uncertainty and, 113–15 Geospatial change observations about, 100–101 CCD, 101–2 change-over-time on GIS, 103 change-over-time on imagery, 102–3 Geospatial collection analysis, 163–64 deductive techniques for, 164 human, 45–46 inductive techniques for, 164 by sensor, 42–45 Geospatial communication(s) audience assessment, 181–82 building the product, 206–12 chronological approach to, 180 of color and shape, 187–88 conclusion, 213 confidence, 203–6 of context, 188–89 defining, 172 dissemination, 172 distillation of, 175 distilling, 180–81 exchange, 172 finished, foundations of, 176–78 Four Cornerstones, 186–89 graphics, 189–93 introduction to, 171–72, 179 knowing audience and purpose and, 174 of location, 187 multilayered peer review for, 212–13 objectivity, 213 practices, 179–213 presentations, 176, 193–97 principles, 173–76 purpose of, 172

224

Geospatial Data, Information, and Intelligence

Geospatial communication(s) (continued) structured techniques, 180–213 through visualizations, 175–76 uncertainty, 197–203 unfinished versus finished, 174–75 writing, 183–86 Geospatial data about, 4, 31–32 background, 32–33 categories, 34–38 collection of, 42–43, 45–46 as embedded in our everyday, 38–41 exposure and accessibility of, 38 future of, 216 geocoordinates, 31–32 imagery pitfalls, 62–64 interpretation and assessments and, 63 IoT, 39 map pitfalls, 64–65 physical rotation of, 87–88 preparation, 133 raster, 35–37 setup, 41–42 spatial, 38 tabular, 34–35 uploading, 133 vector, 37–38 See also Geospatial toolset Geospatial Data Act (GDA), 33–34, 49 Geospatial datasets, 35, 38 Geospatial debriefer, 144 Geospatial Focus Area (GFA) workflow, 138–39 Geospatial observations defining, 51–52 direct, 55 external versus internal, 94–95 introduction to, 51 location initiation of, 18–19 optimizing conditions, 56 practices, 67–104 principles, 53–60 of process flows, 90–91 purpose and general practice, 52–53 reference to resolve, 60 SGOT, 67–94 slow, 78–80 time of observation, 79 tradecraft examples of, 95–103

visualization and, 55–56 Geospatial reasoning about, 159 analytic extrapolation, 161 analytic interpolation, 160 deductive, 161 example, 161–62 geospatial analysis baselines and, 160 imagery analysis and, 131 inductive, 161 principles of, 159–60 Geospatial sensors about, 42–43 direct, 44–45 Earth-based, 44 human collection and, 6 remote, 43–44 See also Geospatial toolset Geospatial skill set about, 7–8, 51 location mindset and, 49 this book, xvii See also Geospatial analysis; Geospatial communication(s); Geospatial observations Geospatial thinking about, 21–22 defined, 22 in history, 20–21 improving through reasoning, 23–28 purpose and practice, 22–23 Geospatial toolset about, 7 conclusion, 49 data, 31–42 hardware, 47 introduction to, 31–32 people in, 48 sensors, 42–46 software, 47–48 systems, 46–47 See also specific elements Geospatial workflow, 6 Global Positioning Systems (GPS), 16, 17–18 Google Maps, 155 Graphics about, 189 Four Cornerstones of, 192–93



Index

geospatial, 189–93 organization, 192 orientation, 190 spatial, 190 See also Structured geospatial communication techniques Ground image-to-satellite image, 143–44 Hard focus, 82 Hardware, geospatial, 47 Heat maps, 137–38 High confidence, 204 Hot spot analysis, 137–38 Human intelligence (HUMINT), 45 Humans related to locations, 148 Identification, entity, 110–11 Ignorance, 59 Imagery analysis about, 3–4, 116–17, 120–21 analytic tools, 122 conducting, 4 geospatial reasoning and, 131 imagery analysis tools, 123 spatial analysis tools, 122–23 spatial analysis tradecraft and, 140 target-specific practices, 123–32 technical practices, 122–23 tradecraft, 120 visual practices, 121–22 See also Geospatial analysis Image-to-map linking, 142–43 Indicators of the observed and unobserved, 93–94 Inductive geospatial reasoning, 161 Infographics, 190–91 Information Age about, 5 flourishing of, 8 visual data and, 60–61 Internal communication, 88–89 Internal review, 165–66, 212 Internet of Things (IoT) about, 17–18 data, 39 phenomenon, 39–40 Introduction building the product, 207–8 presentation, 195–96

225 Key assumption check, 166 Layering locations, 146 Likelihood terms, 199 Lines, 70–71 Lines of communication about, 128–29 assessment, 129 electricity lines, 130 with geospatial reasoning and imagery, 131 identification through, 130 Linking locations about, 142 description-to-image, 144 ground image-to-satellite image, 143–44 map-to-image and image-to-map, 142–43 See also Structured geospatial analysis techniques (SGATs) Listening, 90 Literal interpretation, 63 Locational data, 6–7 Locational data-to-information refinement process about, 5–6 achieving, 8 diagram, 32 Location category about, 68–69 areas, 71–73 illustrated, 69 lines, 70–71 points, 70 See also Four Cornerstones Location mindset about, 6–7, 11–12 collection and, 13 as foundational thinking, 11–12 introduction to, 11–19 prioritization and, 12 in research, 12 spatial and geospatial thinking and, 20–28 strength of, 7 transformation and, 13 visualization and, 13–14

226

Geospatial Data, Information, and Intelligence

Locations about, 11–12 analysis of, 147–49 buildings related to, 148–49 as central feature of our future, 217 contextualizing, 13 finding, 141–42 finished geospatial communications and, 177 geospatial, as universal, 15 geospatial observations and analysis and, 18–19 as highly accurate, 16 humans related to, 148 importance of, xv–xvi as indicator or signature of identity, 19 layering, 146 linking, 142–46 OAC and, 52 pairing visualizations and, 56–58 prioritization of discovery of, 12 prioritizing, 6–7 table of types, 70 transforming, 13 vehicles and vessels related to, 148 as widely available, 14–15 Low confidence, 203 Lundahl, Arthur, 113, 114 Mapping grade receivers, 16 Maps choropleth, 135–37 heat, 137–38 linking, 142–43 pitfalls of geospatial data on, 64–65 scale, 81 spatial distribution of symbols on, 72 symbol size variation, 75 Map-to-image linking, 142–43 Measurements, entities and, 158–59 Mensuration tools, 123 Mental construction about, 25–26 as innate and learned, 26 practice of, 27 spatial thinking and, 28 use example, 27 Mental rotation, 25, 26 Moderate confidence, 204 Moving point target, 126–27

National Aeronautics and Space Administration (NASA), 103 Negators, 92 Notation and documentation, 89 Object differentiation, 23–25 Object recall, 25 Observable keys about, 91–92 creating, 162–63 illustrated, 92 indicators of the observed, 93 indicators of the unobserved, 93–94 negators, 92 signatures, 94 types of, 93 See also Structured geospatial observation techniques (SGOTs) Observation, analysis, and communications (OAC) about, 7 framework, 51, 53, 213 location and, 52 Observational agnosticism, 80 Observational notations, 88–90, 100 Observational perspective about, 80 dimension, 80 distance (zoom and scale), 80–81 from external to internal, 81–82 time, 82 See also Structured geospatial observation techniques (SGOTs) Observational reasoning about, 84 visual baseline, 84 visual extrapolation, 85–88 visual interpolation, 85 See also Structured geospatial observation techniques (SGOTs) Observational uncertainty, 58–60 OpenStreetMap, 155 Organization, of graphics, 192 Organization, this book, xvi–xvii Path dependency, 154 People, in geospatial toolset, 48 PLAN CDF, 97–98, 124–25, 126, 131, 132, 143–45



Index Point of view, writing, 186 Points, 70 Point target analysis practices, 124 Polygons, 37, 38 Presentations about, 176, 193 body, 196 conclusion, 197 geospatial, 193–94 introduction, 195–96 post-presentation, 197 preparation, 194–95 Prioritization, 6, 12 Process flows, observation of, 90–91 Product type selection, 211–12 Quality of information check, 166 Raster data, 35–37 Recall, object, 25 Reference to resolve, 60 Refinement, 56 Relationships analyzing for, 155–59 by appearance and measurement, 157–59 by classification, 157 determining, 111–12 functional, 159 in space, 155–57 in time, 157 Remote sensors, 43–44 Rest, 83–84 Review analytic communication and, 164–67 for communication, 212–13 external, 165–67, 213 internal, 165–66, 212 self-review, 165 Revisit, 84 Rotation of geospatial data, 87–88 mental, 21, 23, 25–27, 53, 84 physical, 87–88, 121 Scale, 81 Self-review, 165 Sensory load balancing, 79–80 Shadow analysis, 127–28

227 Shadows, 74, 127–28, 151 Shape analysis of, 150–52 communication of, 187–88 Shape category, 74–75 Shifting focus, 83 Signatures, 94 Size, 74 Slow observations about, 78–79 attention, 79 detail orientation, 79 observational agnosticism, 80 sensory load balancing, 79–80 time of observation, 79 Snow, John, 112–13, 114 Soft focus, 83 Software, geospatial, 47–48 Sourcing, finished geospatial communications and, 177–78 Space, relations in, 155–57 Spatial analysis about, 4, 117 conducting, 4–5 customized workflows, building, 138–39 data preparation and uploading, 133 geocoding and geolocation, 134 imagery analysis tradecraft and, 140 tradecraft, 133–39 See also Geospatial analysis Spatial analysis tools about, 122–23 aggregation, 135–37 buffers, 134, 139 heat maps and hot spot analysis, 137–38 using, 134–39 Spatial data about, 38 quality of, 204–5 Spatial thinking about, 20 cerebral grid and, 21–22 defined, 20 in history, 20–21 improving through reasoning, 23–28 mental construction and, 28 purpose and practice, 21–22 Structured analytic techniques (SATs), 166

228

Geospatial Data, Information, and Intelligence

Structured geospatial analysis techniques (SGATs) about, 141 analytic communications and review, 164–67 analytic tools in, 122 entities analysis, 146–55 find, link, and layer locations, 141–46 geospatial collection analysis, 163–64 geospatial reasoning, 159–62 observable keys creation, 162–63 relationships analysis, 155–59 Structured geospatial communication techniques about, 180 audience assessment, 181–82 building the product, 206–12 confidence, 203–6 distillation, 180–81 Four Cornerstones, 186–89 graphics, 189–93 presentations, 193–97 uncertainty, 197–203 writing, 183–86 Structured geospatial observation techniques (SGOTs) about, 67–68 focal point control, 82–84 Four Cornerstones, 68–78 observable keys, 91–94 observational notations and communications, 88–90 observational perspective, 80–82 observational reasoning, 84–88 observation of process flows, 90–91 slow observations, 78–80 Style guides, 185 Summarized center and Dispersion, 139 Synthetic aperture radar (SAR), 73 Systems, geospatial, 46–47

line of communication, 128–31 moving point target, 126–27 point target analysis, 124 shadow analysis, 127–28 See also Imagery analysis Technical practices, imagery analysis, 122–23 Temporal context, 153–54 Texture, 74–75, 151–52 Time finished geospatial communications and, 177 observation and analysis and, 79, 82 relations in, 157 Title, 210–11 Tobler’s Law, 152 Transformation, 13 Transporter erector launchers (TELs), 126

Tables of organization and equipment (TO&E), 112, 157 Tabular data, 34–35 Target category, 76 Target-specific practices about, 123–24 areas, 131–32 change analysis, 128, 129 fixed point target, 124–25

Vector data, 37–38, 75 Vehicles and vessels related to locations, 148 Visual baseline, 84 Visual context, 152–53 Visual data, quality of, 204 Visual extrapolation, 85–88 Visual interpolation, 85 Visualization(s)

Uncertainty about, 113–14, 197–98 communicating, 197–203 confidence and, 115 explaining away, 114–15 image-based visual data and, 63 observational, 59–60 principle, 113–15 words, 200, 201–3 workflows and, 201–3 Uncertainty language about, 198 confidence, 200–201 descriptive, 198 estimative, 198–200 Unfinished geospatial communications, 174–75 United Nations Committee of Experts on Global Geospatial Information Management (UN-GGIM), 33 United States Geological Survey (USGS), 102



Index

about, 13–14 communication through, 175–76 ELT and GIS and, 41–42 faulty, 61 geospatial change observations, 100–103 high-quality, 204 low-quality, 204 moderate-quality, 204 next geospatial horizon, 216–17 in observation, 55–56 pairing locations and, 56–58 pitfalls of, 60–65 unsuccesful geocoding, 65 Visual practices, imagery analysis, 121–22 Voice, writing, 185

229 Web mapping, 48 Wings, engines, fuselage, and tail (WEFT), 94 Workflows, 201–3 Writing about, 183 editing and, 212 organization, 183–84 paragraphs and, 183–84 point of view, 186 sentences and, 183–84 style points, 184–86 voice, 185 word choice and tense, 185–86 See also Structured geospatial communication techniques Zooming, 80–81