The Form of Cities in Central Canada: Selected Papers 9781442632349

Table of contents :
Acknowledgments
Preface
Contents
I. Definitions, Concepts And Measurements
Editors' Comments
1. The Area of Interest: Urban Definitions in Canada
2 Methodological Problems in Measuring Urban Expansion
II. Structural Characteristics
Editors' Comments
3. Urban Form and City Size: An Ontario Example
4. Descriptive Patterns of Urban Land Use: A Summary
5. Application of the Lowry Model of Urban Structure to Toronto
III. Growth Characteristics
Editors' Comments
6. Components of Urban Land Use Change and Physical Growth
7. Spatio-Temporal Trends in Urban Population Density: A Trend Surface Analysis
8. Measuring Accessibility Change
9. Net Migration Patterns
IV. Social Interaction and Residential Relocation
Editors' Comments
10. Community Ties and Support Systems: From Intimacy to Support
11. Ethnic Differences in the Residential Search Process
12. Discretionary and Nondiscretionary Aspects of Activity and Social Contact in Residential Selection
13. Household Relocation Patterns
V. Impact of Growth on Rural Environments
Editors' Comments
14. Subdivision Activity in the Periphery of the Toronto Urban Field
15. Migration in the Toronto-Centred Region

Citation preview

The Form of Cities in Central Canada: Selected Papers

UNIVERSITY OF TORONTO DEPARTMENT OF GEOGRAPHY RESEARCH PUBLICATIONS 1.

THE HYDROLOGIC CYCLE AND THE WISDOM OF GOD: A THEME IN GEOTELEOLOGY by Yi-Fu Tuan

2.

RESIDENTIAL WATER DEMAND AND ECONOMIC DEVELOPMENT by T. R. Lee

3.

THE LOCATION OF SERVICE TOWNS by J. U. Marshall

4.

KANT'S CONCEPT OF GEOGRAPHY AND ITS RELATIONS TO RECENT GEOGRAPHICAL THOUGHT by J. A. May

5.

THE SOVIET WOOD-PROCESSING INDUSTRY: A LINEAR PROGRAMMING ANALYSIS OF THE ROLE OF TRANSPORTATION COSTS IN LOCATION AND FLOW PATTERNS by B. M. Barr

6.

THE HAZARDOUSNESS OF A PLACE: A REGIONAL ECOLOGY OF DAMAGING EVENTS by K. Hewitt and I. Burton

7.

RESIDENTIAL WATER DEMAND: ALTERNATIVE CHOICES FOR MANAGEMENT by A. P. Grima

8.

THE ATMOSPHERIC ENVIRONMENT: A STUDY OF THERMAL COMFORT AND PERFORMANCE by A. Auliciems

9.

URBAN SYSTEMS DEVELOPMENT IN CENTRAL CANADA: SELECTED PAPERS edited by L. S. Bourne and R. D. MacKinnon

10. SPATIAL EVOLUTION OF MANUFACTURING: SOUTHERN ONTARIO 1851-1891 by J. M. Gilmour 11. THE FORM OF CITIES IN CENTRAL CANADA: SELECTED PAPERS edited by L. S. Bourne, R. D. MacKinnon and J. W. Simmons

The Form of Cities in Central Canada: Selected Papers

Edited by: L. S. Bourne R. D. MacKinnon J.W. Simmons

Published for the University of Toronto Department of Geography by the University of Toronto Press

) University of Toronto Department of Geography Published by University of Toronto Press Toronto and Buffalo, 1973 Reprinted in paperback 2014 ISBN 978-0-8020-3317-8 (paper)

Acknowledgments Any book resulting from a large scale research effort owes its emergence to many people. This volume is no exception. The editors, and the principal investigators of the research projects involved, owe a debt of gratitude to their colleagues, to present and former graduate students and to a competent research and technical staff. Firstly, the specific project (Environment Study) from which most of the papers derive was supported by a long term grant from Bell Canada. This generous support of academic research is gratefully appreciated. The assistance of project administrators in Bell CanadaTs Montreal office, in the first years Mr. C. Frost, and more recently Mr. R.F. Latham, is also acknowledged. Within the University of Toronto, Professor J.S. Dupre coordinated the formulation and initial development of the Environment Study, and acted as a catalyst throughout the project. Research support for other papers came from numerous sources: Canada Council, CMHC, Province of Ontario, Laidlaw Foundation and the Ontario Institute for Studies in Education. Cartographic materials were expertly prepared by Miss J. Wilcox and Miss J. Ejima in the Cartographic Office of the Department of Geography, under the supervision of Mr. G. Matthews. The contributions of many colleagues and graduate students, past and present, some of which are represented directly in the following papers, were most critical. Without these people the book would not have been possible. Miss Sheila Talley deserves immense credit for collating and retyping numerous revisions of the papers. Our thanks also go to Helga MacKinnon for editorial assistance in preparing the final draft.

V

Preface Do Canadian cities have a distinctive form? How has this form evolved over time; and what has been the impact of growth, transportation changes and differing life styles on the contemporary Canadian urban environment? The research summarized in the present volume is directed at these kinds of questions. This book is an anthology of research papers and reports building around a common theme: urban development in Central Canada (Ontario and Quebec). Within this context, specific interests focus on the spatial structure of the city, land use distributions, patterns of population density and intercity migration, networks of interaction, communities, and lives. What are the underlying dimensions of land use in the city? Are there identified paths of mobility which households follow in changing residence, and what are the implications for social change? What patterns of linkages characterize the city; in transportation and in neighbourhoods? There have been few empirical studies of these problems in Canadian cities. Background This is the second volume reporting on the results of research on urbanization in Ontario and Quebec. The first volume Urban Systems Development in Central Canada; Selected Papers (Bourne and MacKinnon 1972), represented products of one phase of the research. In that volume the authors were primarily concerned with studies of aggregate properties of cities, that is the urban system, of the two provinces. One objective of this emphasis was to provide a system-level framework within which studies of the form and development of individual cities could be undertaken. A third manuscript is anticipated which will attempt to draw out some of the longer term implications of these analyses for urban areas in Central Canada. Most of the following papers, as in the first volume, derive from research projects administered in the Centre for Urban and Community Studies at the University of Toronto. The largest of these projects, the Urban Environment Study, was supported vi

by grants from Bell Canada (Montreal). Objectives This book, like the initial volume, is intended to serve several different but complementary functions. First it collects together in a relatively organized and integrated format a number of diverse studies on the form of the city. Only selected topics could be covered. Most represent the particular spatial and environment bias which was the major thrust of the Environment Study. It is intended to be of interest as a general reference; a reference which is not just descriptive but one which includes a range of examples of analytical approaches. As such it is also designed as a contribution to the growing literature on urban research and policy formulation in Canada (Lithwick et. al. 1971). Equally important it is designed for student use. Few texts provide concrete data and examples of research which relate specifically to Canadian cities. Most illustrations are place-specific. Partly because of the location of the study and the authorsf familiarity with one region, and partly because of its scale of importance, the papers are largely devoted to studies of Toronto and the greater Toronto region. It may be argued that Toronto is the prototype of what smaller Canadian cities, at least those outside of French Canada, may be like in the future, for better or worse. This of course, is a subject of debate. Nevertheless, despite possible accusation of being parochial, an improved understanding of city form and growth based on our Toronto experiences cannot escape being of positive value to other areas of the country. Organization The papers are grouped into five sections: definitions, concepts and measurement; structural characteristics; growth characteristics; social interaction and residential relocation; and the impact of growth and change of a city on the larger urban region of which it is a part. Each section is introduced by short editorial comments. The first section discusses various concepts of the city, census definitions, and problems of measuring the city's areal extent. Reference is also made to hierarchical series of spatial definitions for the Toronto urban region. Section two examines cross-sectional patterns within the urban area. The emphasis of the papers in this section is on physical dimensions: land use, vii

population density and models of functional structure. Section three builds directly on this physical and cross-sectional background, examining recent growth trends in land use, population density, accessibility, and household mobility. The papers in section four provide examples of the complex social interactions which link urban residents and pays particular attention to community characteristics and to the household relocation process. Finally, the papers in section five document selected aspects of the impact of growth on the form and spatial structure of the extensive "urban field" which surrounds large metropolitan centres. The papers themselves represent a diversity of approaches to urban research. As expected in any anthology, they also vary in length, depth and analytical sophistication. Many of the papers have been released previously as research reports; these have been shortened considerably, and have been revised with a liberal pen by the editors. Other papers were drafted specifically for this volume. Most readers will find little difficulty in reading and interpreting the methodology with only one or two exceptions. Both mathematical and computational discussions have been reduced to a minimum. Postscript Most of the analyses in the following papers are based on 1961 and 1966 Census data. As was the case with the first volume, the empirical results are therefore somewhat dated. Nevertheless, it is felt that the availability of these studies in published form coinciding with the release of the detailed 1971 Census, offers a substantial basis for subsequent research on Canadian urban development; and for the identification of future trends and policy options.

References BOURNE, L. S. and MACKINNON, R. D. , eds. 1972. "Urban systems development in central Canada: selected papers." Research Publication No. 9. Toronto: University of Toronto Press. Vlll

LITHWICK, N. H. 1970. Urban Canada: problems and prospects. Report presented to Hon. R. Andras, Minister of Urban Affairs. Ottawa: Queens Printer. Also, part of this report consists of a series of research monographs: Lithwick, N. H. 1971. "Urban poverty." Research Monograph 1. Ottawa: Central Mortgage and Housing Corporation (CMHC). Smith, L. B. 1971. "Housing in Canada: market structure and policy performance. Research Monograph 2. Ottawa: CMHC. Reynolds, D. J. 1971. "The urban transport problem." Research Monograph 3. Ottawa: CMHC. Gillespie, W. I. 1971. "The urban public economy. " Research Monograph 4. Ottawa: CMHC. Goracz, A.; Lithwick, I. and Stone, L. O. 1971. "The urban future." Research Monograph 5. Ottawa: CMHC. Feldman, L. D. and Assoc. 1971. "A survey of alternate urban policies." Research Monograph 6. Ottawa: CMHC.

IX

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Contents ACKNOWLEDGMENTS PREFACE I 1 2

II 3 4 5

III 6 7

v vi

DEFINITIONS, CONCEPTS AND MEASUREMENTS Editors1 Comments The Area of Interest: Urban Definitions in Canada L.S. Bourne and J.W. Simmons Methodological Problems in Measuring Urban Expansion G. Gad

4 5

16

STRUCTURAL CHARACTERISTICS Editors1 Comments Urban Form and City Size: An Ontario Example C.A. Maher Descriptive Patterns of Urban Land Use: A Summary L.S. Bourne Application of the Lowry Model of Urban Structure to Toronto P.D. Harper

35 37 46 63

GROWTH CHARACTERISTICS Editors1 Comments Components of Urban Land Use Change and Physical Growth L.S. Bourne and M. J. Doucet Spatio-Temporal Trends in Urban Population Density: A Trend Surface Analysis F.I. Hill

81 83 103

8 9 IV 10

11 12

13 V

14 15

Measuring Accessibility Change R.D. MacKinnon and R. Lau Net Migration Patterns J.W. Simmons

120 138

SOCIAL INTERACTION AND RESIDENTIAL RELOCATION Editors! Comments Community Ties and Support Systems: From Intimacy to Support B. Wellman, P. Craven, M. Whitaker, H. Stevens, A. Shorter, S. Du Toit, and H. Bakker Ethnic Differences in the Residential Search Process G. Gad, R. Peddie, and J Punter Discretionary and Nondiscretionary Aspects of Activity and Social Contact in Residential Selection W. Michelson Household Relocation Patterns J.W. Simmons and A. Baker

150

152 168

180 199

IMPACT OF GROWTH ON RURAL ENVIRONMENTS

Editors1 Comments Subdivision Activity in the Periphery of the Toronto Urban Field G. Hodge Migration in the Toronto-Centred Region F.I. Hill

219 221 229

The Form of Cities in Central Canada: Selected Papers

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I Definitions, Concepts and Measurements

Editors' comments The point of departure in a set of papers concerned with the city is inevitably one of definition. This is not an easy matter to solve. In an often-used expression ! T .. .the city means different things to different people. M It may be represented as a social community, an economic system, a set of institutions, a political creation, a spatial entity or some complex merging of these and other measures. Whatever the criteria, it is clear that no single definition of the modern metropolis will suffice for all purposes. Instead what is needed is a set of definitions composed of interlocking building blocks, blocks which can be aggregated or disaggregated as conditions change, or the focus of the analysis is altered. The first paper in this section, by Bourne and Simmons, outlines the underlying concepts of urban definitions, including a review of current Canadian census aggregations. The paper concludes by suggesting a hierarchy of urban definitions, using Toronto as an illustration, embracing a wide range of areal units. This summary should assist in clarifying discussions in the rest of the volume while making the obvious point that alternative definitions can be equally valid in different analyses. The second paper identifies the specific difficulties in applying a particular set of urban definitions. Although urban land use is widely used as a unit of measurement, and its expansion widely claimed as a major urban problem, Gad indicates that it is extremely difficult to conceptualize, measure and compare rates of change. With emphasis on Ontario, particularly Toronto, Gad reviews existing and potential sources of data, undertakes pilot studies to evaluate various data compilation methodologies, and compares estimates of land conversion from rural to urban use. Despite these assertions, the papers in following sections refer to different urban boundaries or "areas of interests. Tt In most instances the availability of data determines the specific boundaries employed. The reader is cautioned to interpret the analyses in each paper in light of these varying definitions. Things will improve in the future. The geocoded 1971 census 4

will in large part permit the researcher to obtain data for virtually any area of interest.

1

The area of interest: Urban definitions in Canada L. S. Bourne and J. W. Simmons

Recently, the Minister of State responsible for Housing and Urban Affairs predicted that the population of Canadian cities would double by the year 2000. * Toronto and Montreal are projected to have nearly 6,000,000 inhabitants each, Vancouver over 2,000,000, and several others—Ottawa, Hamilton, Calgary, Edmonton and Winnipeg will approach 1,000, 000. What is meant by "Toronto" or "Montreal" or "Edmonton?" How are these cities defined, and what meaning do these definitions have? This volume1 s concern with research on the form and structure of Canadian cities also raises the question of what the city is and how it should be defined as a spatial entity. It can easily be shown that the results of any study, in particular as these results are filtered by the mass media, may be altered by changing the location of the city's boundary, the dividing line between urban and rural. While none of the following papers News Release, remarks by the Minister of State for Urban Affairs, Central Mortgage and Housing Corporation, Ottawa, 1970.

5

attempt to "find the boundary," this paper raises some of the problems involved, summarizes the range of current definitions in common use, and suggests how further distortions might be best avoided. Alternative Images of the City Various concepts have been put forward to describe and measure the extent of urbanization of the landscape (Goheen 1968). Terms such as megalopolis, conurbation, spread city, metropolitan region, and even ecumenopolis, are becoming part of our everyday vocabulary. 2 Most of these are products of the twentieth century, as the rapid growth and spread of cities into the countryside, increasingly blurred the age-old separation of town and country. As Berry and Horton (1970) have shown, most models argue that the city extends far beyond its visible built-up areas of suburban housing tracts, shopping centres and the like, and each subsequent revision of the definitional criteria has extended the boundaries further and further out into rural and recreational areas (Friedmann and Miller 1965). As cities grow to encompass the vast majority of inhabited national territory, the appropriate image might then be that of a nation-city (Simmons and Simmons 1969). In this nation-city each individual city might be considered as a node within an urbanized landscape—much as the Central Business District (CBD), apartment clusters, industrial parks, exhibition grounds and suburban shopping centres are viewed in the city of the recent past. One city might act as the downtown district of the nation-city, another as the oil refining district, another as the concentration of hospital or military or recreation. But even the ultimate idea of a nation-city requires the delimitation of boundaries between its elements or nodes. Although the notion of f rural f may disappear in this extreme case, it is still of interest to identify where, for example, Hamilton begins and Toronto stops—and this is indeed the main problem in heavily urbanized parts of southern Ontario and Quebec. For the most part the Canadian urban system embraces a wide range of urban conditions from the isolated settlement—Timmins or Sept Ties—to the fGolden HorseshoeT conurbation, around Lake Ontario. Even in these instances certain institutions—the national TV network or local newspapers—impute their own. urban 2

See, for example: Pearson (1961), Doxiadis (1966), and Berry and Horton (1970).

6

boundaries. The alternative is to open the range of definitions, and to liberalize the interpretation of boundaries in recognition of the complexity of the post-industrial urban landscape. Background to Urban Definitions in Canada A variety of concepts defining the city already exists in Canada. Most commonly these relate to the municipal or incorporated city, town and village; or to the set of definitions employed by the various Federal Government agencies charged with providing statistical data. As cities in Canada are creatures of, and are responsible to, the provincial governments, those governments in their planning and publicity tend to concern themselves with cities as they are legally defined. In fact, provincial policies directed at the cities often conflict in definition of the area of interest with those used by the Federal Government. One example is the recent process of reorganization of local government in Ontario involving the establishment of regional government units which are intended to approximate more closely the spatial realities of economic and social integration in the province. In no case do these units conform to those of the Federal agencies, whose criteria are essentially the same. Whichever concepts are preferred, it is obvious that the availability and widespread use of published data on cities dictates the significance of the definitions used in the Census (Dominion Bureau of Statistics, now Statistics Canada). In Canada these definitions have generally followed, somewhat belatedly, those employed by the U.S. Bureau of the Census. The basic criteria in both censuses include reference to: 1) the minimum of 1, 000 population for a village or town to be classified as urban 2) the political or municipal "city" unit, usually under a single elected governing body 3) some version (or versions) of the expanded or "spread city"—most recently the Census Metropolitan Area (CMA). The U.S. Census recognized the scale of the sprawling city, and thus the need for a new definition of the urban unit, with the "Metropolitan District" concept of 1910. Subsequent revisions of the concept lead in the 1950 Census to the introduction of the "Standard Metropolitan Area" (SMA) and the more limited definition of the continuously built-up or "Urbanized Area. " The former definition was again expanded in the 1960 census and titled 7

the "Standard Metropolitan Statistical Area" (SMSA) and in case where two SMSA's abbutted, the "Standard Metropolitan Consolidated Area" (SMCA). Census Definitions 1961 In Canada, formal introduction of the "Census Metropolitan Area" (CMA) did not occur until the 1951 Census, and extensive publication of statistical data for metropolitan units was not forthcoming until 1961. The Canadian definition emerged as a compromise between the two United States1 concepts of SMA and urbanized area. The CMA in 1951 was designed to measure ".. .an expanding social and economic entity, with numerous ties of interdependence between its principal parts." Specifically, the CMA in the 1961 Census was defined as including: 1) a central city of 50,000 population or over, 2) an urbanized core of at least 100,000 total population 3) and including surrounding municipalities closely related as to population density and labour force structure with the centre core, subject to certain conditions. These conditions included: a) an urbanized area with population of at least 1000 persons per square mile, b) a labour force outside of the central city of which at least 60 per cent is engaged in non-agricultural activity. Although the criteria used in defining this spatial entity, unlike the U.S. Census, were the subject of little public debate, they were nonetheless revised in 1966 and again in 1971 as outlined below. Although the U.S. Census definition of an urbanized area was not directly incorporated, the concept of "Major Urban Area" (MUA) was introduced to measure the spread of suburbs and development beyond the municipal boundaries of cities of less than metropolitan status, and to indicate instances in which smaller cities through close proximity created a pooled labour market. Generally then MUA f s were between 40, 000 and 100, 000 in population, yet little use has been made of them since by the public, other users, or the Census itself. Census Definitions 1971 In the 1971 Census, Statistics Canada again redefined the basic building blocks of urban definitions. Although areas representing these definitions are not utilized in any of the following papers, 8

their introduction here is useful for future reference and as a comparative context for studies based on previous definitions. The Census Metropolitan Area (CMA) is now defined as the main labour market area of a continuous built-up urbanized area having 100,000 or more total population. ^ The main labour market area corresponds to a commuting field or zone where people could normally change their place of work without changing their place of residence. It includes: 1) municipalities completely or partly inside the continuous built-up area, and 2) municipalities lying within a 20-mile radius of the limits of the continuous built-up area, if: a) the percentage of the labour force in primary activities is smaller than the national average b) the percentage of population increase for 1956-1966 is larger than the average for the 1966 CMA. When only a) or b) is met but not both, municipalities are included if they are served by a highway of two lanes or more. Each CMA consists of the following parts: 1) an urbanized core, 2) the largest city, 3) remainder and 4) fringe and rural parts. The essential difference between the CMA definitions as previously used and those of 1971 is the openness of boundary conditions. First, the proportion of the population resident in the fringe and engaged in non-agricultural activities has been dropped from 60 per cent to "above" the national average. Moreover, the criterion of contiguity among urbanized "building blocks" is replaced by proximity (20-mile radius). The recent growth performance of outlying areas is also considered in deciding on their inclusion in the metropolitan area. As expected these changes result in most 1971 CMA T s being larger in area than in 1966, except in those instances where cities are isolated by largely rural regions, i.e., Winnipeg and Calgary. Second, the 1971 Census drops the prerequisite criterion of a central city of at least 50,000 population serving as the core of the built-up environment. This revision resulted, for example, in the identification of cities along the Saguenay River in Quebec centred on The CMA in the Canadian census is also considered to be the equivalent of the Conurbation in the U.K., the Agglomeration in France, the Urban Agglomeration used by the United Nations agencies, and the Stadt region in Germany.

9

Chicoutimi as a metropolitan area, although none of the individual cities approached 50,000 inhabitants. The second basic unit, the Census Agglomeration (CA) is a new concept. It represents a statistical area having an urban centre with a population over 1,000 and an adjacent built-up area of at least 1,000 population and combined with a minimum density of 1,000 persons per square mile. These agglomerations are made up such that: 1) complete municipalities are included to ensure comparability with other sources of information. CA!s may then include some rural population, 2) population of urbanized core must be at least 2,000, 3) the largest urban centre and its adjacent urban part must constitute a continuous built-up area with no separation greater than one mile. For the 1971 census approximately 100 CA T s have been defined in Canada, distributed as follows: Maritimes, 20; Quebec, 35; Ontario, 25; Western Provinces, 20. The definition of Census Agglomerations replaces the categories Major Urban Area (MUA) for centres over 25,000 and Urban Areas (UA) for centres smaller than 25, 000 in the 1961 and 1966 censuses. These two definitions included the largest urban centre as well as the cities, towns, villages and the built-up areas of rural municipalities in close proximity. In 1966, the population of what is now equivalent to a CA was strictly urban, whereas in 1971 complete rural municipalities or census subdivisions are also included. Delimiting the Metropolis: Toronto Example To illustrate the definitional problems involved we appropriately refer to Metropolitan Toronto as an example. As in most developed economies with high rates of mobility, social and demographic change, and little direct government interference, the increasing integration of the metropolis with its hinterland has blurred the traditionally sharp distinctions between urban and rural. The authors, in another paper (Simmons and Bourne 1972) have documented the almost total dominance of the provincial hinterland by the metropolis. Similar trends have taken place in Quebec in relation to Montreal and in the relationship of British Columbia to Vancouver. Where then does the city end? The suggestion here is that we do not look for a single com10

Figure 1.1 Delimiting Toronto: concept and hierarchy prehensive framework of definitions. Instead we should employ a flexible set of concepts to define the spatial city and its region. Each concept is a reflection of certain forces which are acting to change landscapes and life styles. Each concept describes a spatial entity, each nesting within the next and larger entity in the form of an integrated hierarchical scheme. Figure 1.1 summarizes the set of existing definitions of the Toronto urban area. They include boundaries for: 1) 2) 3) 4) 5) 6)

the City of Toronto the built-up or developed urban area the Municipality of Metropolitan Toronto (Metro) the 1961 Census Metropolitan Area the 1971 Census Metropolitan Area the Metropolitan Toronto and Region Transportation Study (MTARTS)

Each of these of course is useful in its specific context, but taken together they do not provide a continuous scale against which we might evaluate use of a single area of interest. A possible alternative hierarchy for the Toronto region is suggested in Figure 1.2. It consists of nine definitions ranging 11

* Populations for the City, Metro, CMA and the province are preliminary census figures; other figures are estimates by the authors.

Figure 1.2 Definitions of the scale: Metropolis and region in scale from the urban core of one square mile to the province (the tenth level is the nation). Population varies from just under 3,500 residents inflie core to a metropolitan area of over 2.6 millions and a tributary area of 6. 7 millions in a provincial population just over 7. 7 millions. The nine levels represent different types of urban phenomena, and are the result of different criteria: some are political (City, Metro, Province) or statistical (CMA), while others are defined loosely in physical terms—high densities (the core); and land use (urbanized area), or in terms of interaction—recreational travel behaviour (urban field), or the provision of high-order services (hinterland). Although most of the papers in this volume utilize those areas for which published data are available (political units, CMA f s in 1961 and 1966), the other scales offer a useful basis of comparison. In reference to any given urban area, certain scales of particular importance may be identified. In the Toronto example these are: 1) The Central Area, including the historical core or central business district and the surrounding fringe. Land uses are predominantly commercial or institutional within the core and 12

highly mixed, including residential, in the fringe. Growth takes place by rejuvenation to successively higher densities in the core, by extension of core activities into the adjacent fringe, and by expansion of the fringe. In Toronto all processes are apparent; the central area is growing and its boundaries are quite unstable. 2) The Urbanized Area (population 2,350,000), in standard terminology is the relatively continuous expanse of land builtupon for urban purposes. Growth takes place by extension of its margin (development) or by internal readjustment (redevelopment). At present the urbanized area boundary is roughly approximated by the municipality of Metro Toronto (Figure 1.1), although major extensions occur to the north (Richmond Hill) and west (Bramalea-Mississauga). 3) The Urban Field (population, including urbanized area, 3, 800,000) is arbitrarily defined as the area within an hour T s travel time of the urbanized core. The limits are somewhat larger than the daily "urban system" (Doxiadis 1969)--the commuting zone, but are contained within the area utilized by urban residents for less frequent outings and which are often described as the "urban field. TT This area, which bears the brunt of the growing metropolis, extends as far as Bowmanville on the east, Barrie on the north and Hamilton on the west. 4) The Hinterland (population 6,700,000) designates that broad zone which Toronto dominates in providing higher-order services. It encompasses virtually all of the developed areas of the province, with the exceptions of Ottawa and eastern Ontario (Brockville and beyond) which are linked to Montreal, the extreme north and northwest which are served by Winnipeg, and possibly the Lake St Clair area oriented to Windsor-Detroit. The form of growth that takes place, however, is articulated by the area's close links with the fringe, urbanized area, and the core of the metropolis. The Cohesive Forces These varying definitional scales attest to the differing degrees of linkages between the region and the urban core. These linkages underlie most definitions of the "spread" city. Evolving transportation networks and communication facilities, flows of people 4

At present, the core is bounded by the CNR tracks, University Avenue, DavenportYorkville, and Jarvis Street. The fringe of the central area extends outward about half a mile in each direction.

13

and goods, institutional systems, and, increasingly, governmental control, have strengthened the integration of the metropolis and its surrounding. For the Toronto region these relationships have been described elsewhere. Kerr and Spelt (1965), and Spelt (1968) describe the historical and situational advantages of Toronto; relationships which are extensively amplified in the Ontario Economic Atlas (Dean 1969). MacKinnon and Hodgson (1970) also demonstrate the expanding and centralizing role of Toronto in the provincial transportation network, and Simmons (1970) illustrates how it dominates the hierarchy of flows of goods and information. It is also worth commenting on the process of political controls within this context. These controls both reflect and reinforce the spatial extension of Toronto; the province itself is in part defined by its relationship to Toronto. Only the recent emergence of Ottawa to metropolitan status disturbs this pattern today. Further, public investment decisions of all kinds also have entrenched these ties from the very beginning. The weak influence of lower levels of government is apparent in the absence of any significant political structure between the province and the individual municipality. For over a century the City of Toronto slowly expanded its boundaries to their present size, but it was not until 1953 that assorted outlying suburbs and newly urbanizing townships were grouped, by the Province, into a metropolitan federation. At about the same time a series of other political developments began, at least in part as a response to the further extension of Toronto. These include: 1) the Metropolitan Toronto Planning Board was given jurisdiction over a region roughly equivalent to the 1961 CMA and extending well beyond the limits of political control; 2) the metropolitan economic development region was defined as encompassing Metro Toronto and York County as well as the surrounding counties of Halton, Peel and Ontario; 3) the Metropolitan Toronto and Region Transportation Study (MTARTS) was initiated in 1962 to plan future forms of transportation and land use in an area approximating the urban field as defined above; 4) during the late sixties new municipalities were created 14

on several of TorontoTs margins—i.e. , Mississauga on the west, York on the north—under the ProvinceTs regional government program; 5) in 1970 the plan of the Tor onto-Centred Region was announced, outlining the future pattern of urbanization (in vague terms) within 75 miles of Toronto's core. No integrating political structure for this region has yet been defined. In this fashion, political control, the power to locate new public facilities and to limit, or encourage urbanization, has rapidly solidified the government's expanded area of interest. Increasingly, development decisions in the Province are made with respect to the metropolitan core. In one sense the "dominance" of Toronto is strengthened; in another the Toronto "viewpoint" is weakened and subdivided as areas on the periphery with differing interests are integrated into the metropolitan system. In either case interdependence grows at an accelerating rate. The city then becomes more and more a complex set of spatial entities, each representing a different "area of interest."

References BERRY, B. J. L. and HORTON, F. E. 1970. Geographical perspectives on urban systems. Englewood Cliffs, N. J.: Prentice Hall. DEAN, \V. , ed. 1969. Ontario Economic Atlas. Toronto: University of Toronto Press. DOXIADIS, C. 1969. "Ekistics: a scientific approach to the problems of human settlements." Report to the panel on science and technology, U.S. House of Representatives. Reprinted in Science and technology and the cities. Washington, D. C.: GPO. , et. al. 1966 "Developments toward ecumenopolis--the Great Lakes Megalopolis.' Ekistics 22 no. 128: 14-31. FRIEDMANN, J. and Miller, J. 1965. "The urban field." Journal of the American Institute of Planners 31: 312-19. GOHEEN, P. G. 1968. "Metropolitan area definition: a re-evaluation of concept and statistical practice," in L. S. Bourne, ed. Internal structure of the city. New York: Oxford Univ. Press 1971, pp. 47-63. KERR, D. and SPELT, J. 1965. "The changing face of Toronto—a study in urban geography." Memoir 11. Ottawa: Geographical Branch. Mines and Technical Surveys. MACKINNON, R. D. and HODGSON, J. M. 1970. "Optimal transportation networks: a case study of highway systems." Environment and Planning 2: 267-84.

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ONTARIO ECONOMIC COUNCIL. 1972. Ontario: a society in transition. Toronto: OEC. PEARSON, N. 1961.

"Conurbation Canada." Canadian Geographer 4: 10- 7.

SIMMONS, J. W. 1970. "Patterns of interaction within Ontario and Quebec. " Research Paper No. 41. Toronto: Centre for Urban and Community Studies. University of Toronto. — , and BOURNE, L. S. 1972. "Toronto: focus of growth and change. " In L. Gentilcore, ed. Geography of Ontario. Toronto: University of Toronto Press. , and SIMMONS, R. 1969. Urban Canada. Toronto: Copp Clark. SPELT, J. 1968. "Southern Ontario." In J. Warkentin, ed. Canada; a geographical interpretation. Toronto: Methuenpp. 334-95.

2

Methodological problems in measuring urban expansion G. Gad

Rapid expansion of urban areas, and the associated phenomena of "urban sprawl" and "suburbanization" have been the cause of growing concern. Figure 2.1 displays the scale of growth in Toronto over the last half century. Researchers, government agencies, and planners have all made investigations into different aspects of the problem. Their primary concerns have been the physical, economic, and aesthetic impact of the spreading cities on agriculture and areas of natural beauty; and on the detailed 16

Figure 2.1 Growth of developed area: Metropolitan Toronto Source: After Metropolitan Toronto Planning Board (1968c). 17

problems of the urban fringe. However, there is little data which measure the areal magnitude of urban expansion and changing rates of physical growth. The objectives of this paper are principally methodological: to review methods by which the developed area of the city and parameters of land consumption and absorption have been determined in previous studies; and to assess data sources available in Ontario-Quebec, and how these data sources could be used most effectively. In addition and in view of the difficulties of obtaining land consumption and absorption rates, figures derived in previous studies are assembled for comparative purposes. Rhodes (1968) for example warns that "it seems likely that the data collection and preparatory costs for urban modelling will continue to rank as a very high proportion of total study costs.!! Under these circumstances, a search for comparatively easy and fast methods of data collection seems to be justified. Concepts, Definitions, and Requirements for Data Sources Before attempting a review of the literature, it is appropriate to clarify some of the basic terms relating to concepts of the urban area and measurements of intensity of land consumption and land absorption. From the research considered here four different notions of the "urban arean emerge: Developed area Represents the "land actually used for urban purposes" (Clawson', Held, Stoddard[1960]p. 63). This includes land occupied by residences, other buildings, schools, streets, railroads, and parks. Similarly Hind-Smith defines "urban development" as the: "uses of land by housing, industry, commerce, recreation facilities serving the urban population, highway services related to the city, transportation facilities, utilities, and all £oads, railway lines and off-street parking facilities within the limits of the foregoing uses" (Hind-Smith[l962]p. 156). Area withdrawn In contrast to the developed area the "area withdrawn" marks the outer edge of land affected by urban expansion. Clawson and associates define "land withdrawn by the urban area from other uses" as vacant areas within the developed area, or outside of it if rather clearly withheld from other uses (Clawson; Held, Stoddard[1960]p. 63). Hind-Smith is more specific. The indicators of land affected by urban development are: undeveloped subdivisions, non-farm ownership of land, farm land for sale for urban purposes, and non-farm assessment. The area defined by 18

these factors identifies the limits of "urban shadow" (Hind-Smith [1962] p.157). The use of the notion here relates to an area which includes both the developed area and the belt indirectly affected by urban expansion. Incorporated area In the North American context this means the area defined by the legal or political boundaries of an urban place. Census or planning areas In most cases, census or planning area delimitations are based on the concept of the "economic city," an area with strong functional dependencies, e.g., commuter or goods flows. Definitions of these areas vary from place to place and from time to time. In the United States the "standard metropolitan area" and the "urbanized area" are used and in Canada the "Census Metropolitan Area" and the "Major Urban Area" are in use. If the areas defined above are related to population, various density or intensity measures may be obtained. In contrast to net or gross residential or employment densities, those measurements based on total urban area should be called "overall population densities" (Ontario Department of Municipal Affairs 1969). Usually densities are expressed in terms of persons per acre or square mile and sometimes in the reciprocal of this relationship, i.e., in acres per 1000 population. The latter intensity measure may be called the "land consumption rate" (Boyce 1963). If the incremental growth of the urban area and the urban population are related, the "land absorption coefficient" (Niedercorn and Hearle 1963) is obtained, expressed in acres of land per 1000 population increase. A number of requirements have to be met in order to arrive at reasonable land use time series data. It is mandatory to examine data sources—and completed research—with regard to the area measured (i.e., developed, withdrawn, incorporated, census area). If population figures are related to developed area, the constancy of study area boundaries has to be assured, otherwise varying land absorption coefficients will result because of the existence of density gradients. It is also necessary to establish that land use definitions do not vary from place to place and time to time, and that spatial reporting units for area and population are sufficiently similar to permit relationships to be computed. 19

Data Sources and Methods for Estimating the Urban Area There are four major sources from which time series data might be obtained: repeated ground surveys, census questionnaire surveys, air photos and assessment records. Repeated ground surveys Comprehensive ground surveys are seldom taken. The British Land Utilization Survey of the early 1930Ts (Stamp 1950) and the follow-up study in the 1960Ts (Coleman 1965) are unusual examples. Best and Coppock (1962) have used a wealth of material on urban land use that has been compiled by local planning departments in surveys required under the British Town and Country Planning Act of 1947. In Ontario, the Department of Municipal Affairs (Ontario Department of Municipal Affairs 1969) collected figures from planning boards for the developed areas of 51 urban places varying in size from 700 - 1,700,000 population (see Paper 3). Unfortunately, the survey dates for individual cities are scattered over a ten year period, and the areal unit of collection is the incorporated area. There are no figures for an earlier period and therefore estimates of change cannot be obtained. Only a few local planning agencies can rely on data for a series of years. Hind-Smith (1962) quotes local planning survey figures for two temporal cross-sections, although air photos were used as additional data sources. The study includes four cities (London, Kingston, Stratford, and Lindsay) and developed area figures are given for 1951 and 1960. In the United States, Bartholomew's (1955) research on land use in American cities was also based on ground surveys. Data collection, carried out by the staff of Bartholomew's research office, extended over a period of 17 years (1935-52) and covers 97 municipalities. Niedercorn and Hearle (1963) used questionnaires to obtain land use data from local planning commissions in U.S. cities. Whether the data were originally collected by means of ground surveys is not specified, but only assumed here. They received 48 usable replies; 22 cities reported data for more than one year. This brief outline has considered only publications which aimed to achieve data compilations for sets of cities. No doubt land use figures for individual cities for one or more points in time are stored in quite a number of municipal planning departments, consultants1 reports or students' theses. The methods mentioned in this section however, have serious disadvantages. 20

It is obvious that centrally controlled ground surveys such as the first and second British land use surveys, despite the advantage of uniformity in land use criteria, are wasteful in time and energy. The land use picture of a single year was beyond the scope of both surveys. Because of enormous organizational problems, surveys of this kind are not possible in a quick succession and do not yield useful time series figures. Surveys carried out by local planning departments have the disadvantage that they are often not compatible in information content (i.e., each planning department sets its own definitions of land uses) and not comparable in time. A further complication is that most planning departments operate in politically bounded spatial units. If annexations occur, or if neighbouring cities are excluded in one survey but included in the next one, land use figures are substantially changed. Census surveys Census data for urban land use are scarce. In some instances, researchers had to be content with figures referring to some political or special census area for cities. In other cases, inferences about the growing urban area were drawn from agricultural statistics. Best (1968) sums up the use and limitations of the British Agricultural Census for the purpose of inferences about the areal expansion of urban areas. The primary sources of data are the yearly agricultural returns which contain a "change in occupancy section. n If land transfer of a significant proportion occurs and is not between farmers, the intended future use is investigated by special census personnel, and the data are compiled, summarized, and published on a national basis. * The agricultural census figures of Canada and the United States do not contain a change in occupancy section which would explain to what use abandoned farmland is converted. This does not prevent some researchers from using agricultural census figures when the expressed concern of their attempts was to find out "how many acres of land are removed from agricultural production per 1000 population increase" (Bogue 1956). BogueTs pioneer study included 147 Standard Metropolitan Areas (SMATs) in the United States. The farmland figures for each SMA were assembled by adding statistics for the component counties from •'-Best (1968) does not discuss whether the change figures are for developed area or for area withdrawn, although from the data description it appears that they relate to area withdrawn.

21

the Census of Agriculture 1930, 1940, 1950, and 1954. A serious problem arose when it was discovered that in 60 out of 147 SMA's agricultural land had actually increased rather than decreased in the time period from 1930 to 1954. Bogue proceeded to derive land absorption coefficients by selecting specific regions for which there was an overall decline in agricultural land in the SMA T s. The land absorption coefficients obtained in this way are based on land withdrawn from agriculture. Since the census allows for computations of the total area for each SMA it is possible to estimate the area withdrawn (SMA minus area in farms). Crerar (1962) adopted BogueTs method and modified it slightly for the Canadian situation. Metropolitan areas with over 100,000 population were examined. Moving outward from the central city each suburban area or rural municipality was scrutinized to determine if it had lost agricultural land between 1951 and 1956. If it had lost farmland it was considered to be part of the metropolitan region. The lost agricultural land within this region was then related to population growth. As in BogueTs study, several limitations of this approach became apparent: analysis of the Vancouver region was not possible because the urban area encroached not only on farmland but also on forests; in Alberta it was impossible to examine the situation around Calgary and Edmonton since there was a large scale revision of census district boundaries between 1951 and 1956; the loss of farmland around Ottawa and Quebec City was extremely large and seemed to be out of proportion to urban growth and urban land speculation; and since there was a redefinition of farmland in the 1951 Census it was impossible to extend the analysis to years before 1951. The difficulties encountered by Bogue and Crerar in working with the agricultural census figures of the United States and Canada impose certain restrictions on the use of this source for time series data. Firstly, it is not possible to measure directly the size of developed areas, and secondly this approach assumes that all land not in farms (or farms and forests) is urban, and further that all abandoned farmland is awaiting urban development. Thirdly, the "decrease in farmland" approach requires an agricultural setting for the expanding city and assumes that Three explanations were given: a) land reclamation; b) transfer of grazing land to private ownership; c) different census reporting practices (Bogue 1956).

22

farmland is generally on the retreat. Increases of agricultural land through reclamation or the shifts of land from nonagricultural, non-urban to urban uses also make this approach awkward if not impossible. Urban areas in the census The population census of the United States provides area figures for three major types of "urbanlike11 areas: 1) for the SMA or, since 1960, the Standard Metropolitan Statistical Area (SMSA); 2) for the Urbanized Area; and 3) for the Incorporated Area (Clawson-, Held and Stoddard 1960). The SMATs and SMSA!s contain large amounts of agricultural and other non-urban land uses. The share of these non-urban uses is considered to amount to more than 50 per cent of the area of SMA's and SMSATs. Unless some accurate way of subtracting these non-urban uses is found it is of no value to consider SMA T s or SMSATs as estimates of urban development. The definition of the Urbanized Area is more tightly drawn around the edge of the developed area, but it includes some nondeveloped land and might exclude some smaller municipalities. Data for Urbanized Areas have been provided since the 1950 Census only and the definition has changed slightly between 1950 and I960. 4 Boyce (1963) has used 1950 and 1960 "Urbanized Area n figures for a regression analysis of area and population and a comparison of densities. Pickard (1967) and Hoyt (1968) also used "Urbanized Area" data for their projections of urban population densities and land area requirements for urban growth. The Incorporated Area for cities with more than 25, 000 or 30,000 population is shown in the United States Census since 1900. Clawson and associates have made extensive use of incorporated area figures. Land consumption rates and their changes were computed on that basis. Since Clawson and associates could prove that density/size relationships have been constant for the last 50 years they then used the 1950 land consumption rates to make inferences about the growth of the total urban area of the United States by applying these densities to total urban population at various points in time. See also Van der Linde (1969). Attempting to find prediction equations for changes in "improved land" acreages Van der Linde found that distance to urban areas has little or no influence. 4 For the changing definition of the "Urbanized Area" see Boyce (1963).

23

The Canadian Census shows area figures for incorporated cities, towns, and villages, extending back to 1900. Apart from the common disadvantage of incorporated area data (the problem of over- and under-bounding) another serious drawback of this set of figures is that many important suburban areas have only recently been incorporated and therefore are not included in the earlier census. Use of these data would also require that small incorporated places which are now part of a large metropolitan area are added to the central city at an appropriate time; i.e., when the growth of the central city has affected growth in the smaller place. Surveys from air photographs Textbooks on air photographic interpretation invariably contain sections emphasizing the importance of this survey method for urban land use studies. On closer examination it turns out that there are exceptionally few surveys covering large areas such as whole cities. Nevertheless, if air photographs are available, urban features can be interpreted in great detail (see Green [1957] and Mumbower and Donoghue [1967]) and fairly accurate land use figures can be obtained. The degree of accuracy, however, depends on the quality of the photographic material, the economic feasibility of enlargements and the time available for interpretation and measurement. The last constraint necessitates a search for more efficient methods. In earlier surveys (e.g., Pownall [1950]) blocks of homogeneous land uses or "functional" areas were marked on the photograph and their areas measured with a planimetric instrument or by means of a grid overlay. Studies of this kind have been confined to smaller cities and especially to sub-areas of a single city. Exceptions, known to the author, are the study of the Niagara Fruit Belt by Krueger (1959) and a large survey of Chicago directed by the Northeastern Illinois Planning Commission (1965). Krueger used 1934 and 1954 photographs to determine the changing land use pattern in the Niagara Fruit Belt. For both years a full survey of the whole area was carried out. Every developed property was measured and the developed area was recorded as a proportion of the area of grid cells which were used as a spatial reference system. Krueger!s figures are comparable to a later ground survey carried out by the Department of Municipal Affairs in 1958 (Sinclair 1961). On the other hand, the staff of the Northeastern Illinois Planning Commission decided on a dot 24

sampling design to obtain land use figures for a large area. In a pilot study (Gad 1970) which measured urban expansion of a suburban segment of Metropolitan Toronto, several problems of interpretation and measurement in using air photographs were identified: 1)

2)

It is difficult to trace property boundaries, particularly in the fringe area and when large institutional or industrial land use parcels are involved. These parcels must be considered developed even if only a small fraction of them is covered with buildings. Parks and recreation areas, which are part of urban development, cannot be differentiated from some types of vacant land or wood lots; and it proved to be very difficult to separate farmland from vacant land when attempting to measure the area withdrawn.

If these problems are overcome and if air photographs are available for required time periods they should provide an excellent source of data for both time series analysis as well as historical studies. Air photographs offer several advantages: land use can be interpreted for different cities by the same persons with a consequently high grade of compatibility of information; the photographs impose no constraints as far as areal units of a recording system are concerned; repeated sampling or measurement under different specifications is possible; and different scales of analysis are offered. 5 Surveys using assessment rolls Assessment rolls register every parcel of land, and—since the practice of tax collection has quite a long history—they should provide another excellent source for time series data on urban land use. It appears that little use has been made in large scale surveys. One exception is Bourne's (1967) use of time series assessment data in Toronto provided in a pre-processed form by local authorities. Russwurm (1969) used assessment data on a large scale in attempts to measure the impact of expanding urban areas on agriculture in southwestern ^As far as availability of photographic coverage of Ontario and Quebec cities is concerned there are promising avenues by approaching local planning boards. For example the areas of Metropolitan Toronto and Quebec City have been covered annually, with some exceptions, by air photo surveys since 1947. Sets of air photographs for Metro Toronto (1947 to 1968) are now on deposit at the University of Toronto Library.

25

Ontario. Another example of the use of assessment rolls is shown in Hind-SmithTs (1962) study of the "urban shadow" phenomenon around four Ontario cities. The great advantage of assessment data is that it is available in a continuous annual flow. Until now the rolls have been accessible to everybody at virtually no real cost. In order to get some idea of their usability a small pilot study was carried out by the author in Etobicoke and in the City of Toronto. The following problems were encountered: 1)

2)

3)

4) 5)

As each property is described by the exact frontage and an approximation of the depth and rear width, the area has to be computed and the relevant properties have to be added to give aggregate land use figures. In some entries, descriptions of the area of the properties were missing. It is extremely difficult to aggregate properties into spatial units that are useful for further analysis since the spatial reference systems of the assessment rolls consist of blocks and wards which do not necessarily correspond to census tracts or other statistical reporting units. Different land uses cannot be separated in some cases. If a developer bought a large amount of farmland and part of it is developed, then the property would have to be split into three land use categories. The assessment roll indicates that different amounts of taxes are paid, but it does not give the area figures for the different uses. Tenants are listed in the assessment rolls. This implies that a particular sample might fall on a tenant and not on a property description. It is very difficult to hold a unit of property constant over time given the multitude of boundary changes and subdivisions which occur in the urban development process.

These problems make the use of assessment rolls very cumbersome and time consuming. It would take about 10 man-days to calculate the developed area of a municipality of the size of Etobicoke. Russwurm (1969), for example, in his work on the urbanized area around London (Ontario) needed 150 man-weeks to extract and code land use information for a 2,500 square mile area from assessment rolls. Unless some efficient way of sampling is found the time involved is prohibitive for time series data of developed area estimates. Russwurm hinted at the difficulties associated with sampling assessment data: 1) if a random 26

sample is to be taken it is very difficult to enumerate the population (i.e. , the properties), and 2) the search procedure for the particular properties of the sample is extremely awkward. In Ontario, however, preparations are underway to collect assessment data centrally and store the information on magnetic tape. This will enable detailed time series analysis of developed areas, but it does not help to obtain figures for the past which are needed to estimate long term trends. Findings of Previous Studies Although it is difficult to obtain a coherent picture of changing rates of land consumption for urban development from the above studies, a synthesis of various statistics is attempted here. One must bear in mind that the figures below are extracted from a variety of sources and methods of collection. TABLE 2.1 SUMMARY OF LAND CONSUMPTION RATES (Developed acres per 1,000 population) Source and date of survey City size (pop. ) 10, 000 20, 000 50, 000

100, 000 200, 000 500, 000 1,000, 000 1,700, 000

Bartholomew1 1940's (U.S.)

Niedercorn2 1960 (U.S.)

191 106 92 102

100 80 80 50

Hind-Smith3 1951 (Canada)

85 67

Hind-Smith3 1961 (Canada) 175 120 106 97

D.M.A.4 1950-1960's (Ontario) 115 105 93 85 78 69 63

Metro Planning Board5 (Toronto) 1963 1966 1968

63.0 61.8 60.7

Bartholomew (1955). Niedercorn and Hearle (1963). 3 Hind-Smith (1962). 4 Based on equation from Maher and Bourne (1969). 5 Developed area figures from Metropolitan Toronto Planning Board (1968a) and unpublished data. Population figures for 1961 and 1966 from Metropolitan Planning Board (1968b). 1963 and 1968 population figures were interpolated and extrapolated, respectively. 2

Table 2.1 displays land consumption rates. All the ratios are based on the "developed area," which has been measured within the boundaries of incorporated areas. An exception occurs in Hind-SmithTs 100, 000 category. He seems to have used an "Urbanized Area" concept in the case of London. Table 2. 2 shows more detailed figures for the Metropolitan Toronto Planning Region. The land consumption rates are difficult to interpret and rather inconclusive. A comparison of Bartholomew's and Niedercorn T s and Hearle T s data for example shows a rise between 27

TABLE 2.2 LAND CONSUMPTION RATES IN THE METROPOLITAN TORONTO PLANNING REGION*

Municipality or area

Acres per 1,000 population 1963 1966 1968

City of Toronto York East York North York Etobicoke Scarborough

33.2 39.3 52.8 91.2 •96.1 98.5

33.4 38.7 52.4 82.9 90.4 91.1

33.3 38.7 52.1 81.6 87.9 91.6

Metro Toronto 63.0 Fringe area of Metro planning region 320.8

61.8 293.0

60.7

98.3

85.9

-

Total Metro planning region

-

*See footnote 5 of Table 2. 1

the 1940Ts and 1960fs. Hind-SmithTs figures in contrast show both increases and decreases during the 1950Ts. The Metro Toronto land consumption rates declined slowly in the 1960Ts. TABLE 2.3 SUMMARY OF LAND ABSORPTION COEFFICIENTS (acres per 1,000 population increase) Source and date of survey City size (pop.) Average 10,000 20,000 50,000 100,000 1,700,000

Niedercorn1 1940-1960 (U.S.)

Krueger, Sinclair2 Krueger, Sinclair2 1934-1954 1954-1958 (Niagara area) (Niagara area)

91 143 95

76 75

Hind-Smith3 1951-1961 (Canada)

Metro Planning Board4 (Toronto) 1963-66 1966-68

102 206 164 72

49

40

iNiedercorn and Hearle (1963). Krueger (1959), Sinclair (1961). The 1954-58 population change was interpolated on the basis of the 1951 and 1956 population figures quoted by Krueger. 3 Hind-Smith (1962). Metropolitan Toronto Planning Board (1968a; 1968b). For details see footnote 5 of Table 2.1. 2

Land absorption coefficients (see Table 2.3) do not provide conclusive evidence for increasing or decreasing urban land consumption either. Very few statistics are available to calculate absorption coefficients for more than one time interval. The Krueger/Sinclair figures are an exception but are based on two 28

municipalities in the Niagara Fruit Belt only. The generality of these declining absorption coefficients is doubtful. Similarly, the Metro Toronto coefficients, which show a remarkable decline, might be caused by redevelopment rather than by increasing development densities of the urban fringe. If the Metropolitan Toronto boroughs are considered separately, extremely low absorption coefficients emerge for the inner boroughs (about 2035 acres per 1000 population increase) and highly variable ones for the outer boroughs. The data mentioned above, however, confirm that land consumption rates decline with increasing city size. The Toronto figures show in addition the internal variation of land consumption rates. Land absorption coefficients also decline with increasing city size and vary spatially within an urban area. The latter circumstance poses the question as to the usefulness of employing uniform land absorption coefficients in estimating the extent of urban area growth. Obviously "infilling" in older suburbs will take place at much higher densities than new development at the fringe. The Scale of Urban Expansion These various rates of change all translate into massive expansion of urban areas in Canada. For example, at the recent annual growth rate of 50,000 persons a relatively low land absorption coefficient of 40 acres per 1000 population increase means that 2000 acres of land are developed within the boundaries of Metropolitan Toronto each year. Of Metro T s 154,500 total acres in 1966, 116,400 acres were developed, indicating that if the above rate of land conversion continues it will exhaust the supply of land by 1985. The "fringe" of the Metropolitan Toronto Planning Area showed an annual increase in the early 1960 f s of 12, 000 persons, which with land absorption coefficients of about 150 acres per 1000 population increase, resulted in 1800 acres being developed each year. As Metro fills and development spreads increasingly into the fringe the scale of land conversion will accelerate rapidly in these outer areas. Estimates based on land consumption rates show that the 63 places over 10,000 population in the southern Ontario/Quebec urban system increased their developed area from 396, 000 in 1951 to 605,000 in 1966. To see this expansion in perspective, one should keep in mind that in 15 years only slightly more than the equivalent of the area of Metropolitan Toronto has been 29

added and that the total developed area for places over 10, 000 population adds up to about four Torontos in 1966. The estimatesfor "area withdrawn,!! or in other words, the loss of agricultural land, naturally are the highest in the whole set of figures discussed here. Bogue (1956) arrived at different estimates based on loss of agricultural land in 147 Standard Metropolitan Areas of the North East and Midwest of the United States: the losses range from a low estimate of 172 acres to a high estimate of 264 acres per 1000 population. Crerar T s (1962) figures for Canadian cities show even wider variation; in the case of Windsor, 192 acres of agricultural land were lost per 1000 population increase, in the Toronto-Hamilton area 374 acres and in London 458 acres. Ottawa and Quebec City show ratios of 1000 acres lost per 1000 population increase. Conclusions and Future Prospects This attempt to compare different methods and to extract land consumption rates and land absorption coefficients from existing literature does not lead to conclusive answers about either the extent or the measurement of urban expansion. Many problems underly the patchwork of figures: there are few longitudinal studies, data have been collected under different terms of reference and different circumstances and have been analyzed and published in different ways. Reliance on expensive ground surveys has posed decisive restrictions on the scope of data collection. The search for other data sources on the expansion of urban areas is necessary. Most promising among these sources, because they are available not only for the present time but reaching back into the past, are air photos and assessment records. The preservation of data sources compiled in the past gains added importance when linked to and viewed in the context of, recent technological innovations which hold great hope for the future. Remote sensors applying multi-band photography are surveying instruments of great potential (Moore and Wellar 1968). It is expected that further progress will be made with automatic interpretation of multi-band photography (Dalken.d.). Present research is concerned with identification of building materials (Schneider 1967), and other projects include pattern recognition and geo-statistical analysis with the help of laser beams (Curry and MacDougall 1971). Progress in geo-coding would improve spatial reference systems and might overcome the awkward problem of changing spatial units for data collection and storage. 30

It would also speed up the interpretation-storage process. Urban information systems in general provide the obvious means for storage and processing of data collected by these devices (see U.S. Department of Housing and Urban Development [1968];and Hodge and McCabe [1968]). Since urban information systems should be user oriented they would imply that the data collection processes become better focussed than is the case now. Urban information systems are also the means by which data for past years could be linked with future data and these time series for the past would give greater scope and depth to data series to be collected.

References

BARTHOLOMEW, H. 1955. Land use in American cities. Cambridge, Mass.: Harvard University Press . BERRY, B. J. L.; GOHEEN, P. G. and GOLDSTEIN, H. 1968. "Metropolitan area definition: a re-evaluation of concept and statistical practice." Working Paper No. 28. "Washington, D. C.: U. S. Bureau of the Census. BEST, R. H. 1968. "Extent of urban growth and agricultural displacement in post-war Britain." Urban Studies 5: 1-23. , , and COPPOCK, J. T. 1962. The changing use of land in Britain. London: Faber and Faber. BOGUE, D. J. 1956. "Metropolitan growth and the conversion of land to nonagricultural uses." Scripps Foundation Studies in Population Distribution No. 11. Oxford, Ohio: Scripps Foundation. BOURNE, L. S. 1967. "Private redevelopment of the central city." Research Paper No. 112. Chicago: Dept. of Geography, University of Chicago. . 1969. "Measuring land use and structural change. One element of an urban information system." Plan Canada 10. 2: 7-15. BOYCE, R. 1963. "The changing patterns of urban land consumption." Professional Geographer 25: 19-24. CLAWSON, M. 1971. Suburban land conversion in the United States. Baltimore: Johns Hopkins for Resources for the Future Inc. .1 HELD, B. and STODDARD, C. H. 1960. Land for the future. Baltimore: Johns Hopkins Press. COLEMAN, A. 1965. Land use survey handbook. An explanation of the second land use survey of Britain on the scale 1:25.000. Fourth ed. London.

31

CRERAR, A. D. 1962. "The loss of farmland in the growth of the metropolitan regions of Canada." Resources for Tomorrow. Conference Background Papers. Ottawa: Queen's Printer, pp. 181- 95. CURRY, L. and MACDOUGALL, E. B. 1971. "The statistical information content of remotely sensed imagery. " Report submitted to the Natural Aeronautics and Space Administration, Washington. Toronto: University of Toronto. Mimeo. DALKE, G. N.d. "Automatic processing of multi-spectral images. " CRES Report No. 61-16. Lawrence, Kansas: University of Kansas. GAD, G. 1970. "A review of methodological problems in estimating urban expansion." Research Report No. 25. Toronto: Centre for Urban and Community Studies. University of Toronto. GREEN, N. E. 1957. "Aerial photographic interpretation and the social structure of the city." Photogrammetric Engineering 23: 89-96. HIND-SMITH, J. 1962. "The impact of urban growth on agricultural land: a pilot study." Resources for Tomorrow. Conference Background Papers Supplementary Volume. Ottawa: Queen's Printer, pp. 155- 79. HODGE, G. and MCCABE, R. W. , eds. 1968. "Land use classification and coding in Canada: an appraisal." Plan Canada June, Special Issue. HOYT, H. 1968. Urban land use requirements 1968-2000. The land area required for the future growth of the urban population of the United States. Washington, D.C.: Homer Hoyt Institute. KRUEGER, R. 1959. "Changing land-use patterns in the Niagara Fruit Belt." Transactions of the Royal Canadian Institute 32, No. 2. MAKER, C. A. and BOURNE, L. S. 1969. "Land use structure and city size: an Ontario example." Research Report No. 10. Toronto: Centre for Urban and Community Studies. University of Toronto. METROPOLITAN TORONTO PLANNING BOARD. Land Use Division. 1968a. Metropolitan Plan Review. Report No. 1, Existing Land Use, 1966. Toronto: MTPB. . 1968b. Metropolitan Toronto key facts. Toronto: MTPB. . 1968c. Metropolitan Toronto 1968. Toronto: MTPB. MOORE, E. G. and WELLAR, B. S. 1968. "Experimental applications of multiband photography in urban research." Transactions. Illinois State Academy of Science No. 61. MUMBOWER, L. andDONOGHUE, J. 1967. "Urban poverty study." Photogrammetric Engineering 33: 610- 18. NIEDERCORN, J. H. and HEARLE, E. F. R. 1963. Recent land-use trends in fortyeight large American cities. Santa Monica, Calif.: Rand Corporation. Memorandum RM - 3664.-1 - FF. NORTHEASTERN ILLINOIS PLANNING COMMISSION. 1965. Metropolitan planning guidelines, phase one; background documents. Chicago: NIPC. ONTARIO DEPARTMENT OF MUNICIPAL AFFAIRS. 1969. Urban land uses in Ontario. Areas and intensities. Toronto: ODMA. PICKARD, J. P. 1967. "Dimensions of metropolitanism." Research Monograph No. 14. Washington, D. C.: Urban Land Institute. POWNALL, L. L. 1950. "Aerial photographic interpretation of land use in Madison, Wisconsin." Photogrammetric Engineering 16: 414-26.

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RHODES, T. 1968. "Data requirements for urban land use models." Journal of the Town Planning Institute 54: 281-83. RUSSWURM, L. H. 1969. The development of an urban corridor system. Toronto to Stratford area 1941-1966. Report to the Regional Development Branch, Ontario Department of Economics and Development. Toronto. SCHNEIDER, C. H. P. 1967. Material identification in urban areas from grey tone variations in multispectral photography. Evanston, 111.: Northwestern University, Dept. of Geography. SINCLAIR, M. H. 1961. "The Niagara Peninsula." Resources for tomorrow. Conference background papers. Ottawa: Queen's Printer 1: 485-503. STAMP, L. D. 1950. The land of Britain—its use and misuse. 2nd ed. London: Longmans. U. S. DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT. 1968. Urban and regional information systems. Support for Planning in Metropolitan Areas. Washington, D. C.: GPO. VAN DER LINDE, R. 1969. "Urban-rural relationships: a survey of research and an empirical test." Research Report No. 16. Toronto: Centre for Urban and Community Studies. University of Toronto.

33

II Structural Characteristics

Editors' comments Once the urban area of interest has been established and boundaries defined, attention can then shift to descriptions of the organization of space within this area. The papers in this section introduce examples of empirical research on the spatial structure of cities, again using Ontario and Toronto data as the basis for analysis. The term spatial structure is not ne^ yet its meaning is often confused. In fact it has no common definitional basis or conceptual framework. A review of the literature suggests that the term refers most frequently to nthe change in, arrangement and extension of, urban land uses" (Post 1964); or differently worded, to nthe spatial distribution of producers of various goods and services and of consumers in cities and towns of various sizes" (von BOventer 1962). These concepts are, however, too limited and static in nature. Spatial structure should be extended to represent all of the following topics: 1) land use distribution and arrangement 2) organization, concentration and intensity of activities and human occupance 3) formal networks of interaction, flows and communication linking human behaviour and physical artifacts 4) locus of decision-making powers 5) values and norms interwoven with the above physical attributes The following papers focus on aspects of the first three topics. This section emphasizes cross-sectional characteristics; in effect a snapshot of spatial structure at one point in time. In the first paper, Maher attempts to set a context by relating selected urban attributes to city size. Data from 51 Ontario cities are used to correlate population density, developed area and land use composition with urban population. In the second paper, Bourne examines the patterns of land use composition within one city—Metro Toronto, as one approach to the descrip35

tion of urban form. Attempts at exploring the complex relationships in urban structure are often made through the use of analytical models. In the third paper, Harper summarizes the application of one such model to Toronto: the Lowry "model of metropolis.Tf This model is essentially an allocation procedure: it attempts to replicate the observed form of the city through a series of structural equations and iterative computer steps. Given an initial set of assumptions regarding the relationships between land use, employment and accessibility, as defined by the structural equations, the model allocates land to different uses. The resulting distribution of uses can then be compared with the actual distribution and tests of their sensitivity to variations in the assumptions (parameters) can be made. Construction of the model, although highly generalized in this instance, offers a valuable learning experience in understanding the underlying determinants of urban form as well as a useful operational planning tool.

References

BOURNE, L. S. 1971. "Patterns: descriptions of structure and growth." Introduction, Section II in Internal Structure of the City; Readings on Space and Environment. New York and Toronto: Oxford University Press pp. 69-74. FOLEY, D. L. 1963. "An approach to metropolitan spatial structure," in Explorations into Urban Structure. Philadelphia: Univ. of Pennsylvania Press pp. 21-55. POST, R. B. 1964. "Criteria for theories of urban spatial structure: an evaluation of current research. " M. A. Thesis. Chapel Hill: Department of City and Regional Planning. University of North Carolina. VON BOVENTER, E. 1962. "Towards a unified theory of spatial economic structure." Papers and Proceedings of the Regional Science Association 10: 163-91.

36

3

Urban form and city-size: An Ontario example C.A. Maher

Comparative analyses of the relations between urban form and city size are relatively rare (Manvel 1968; Alonso 1970). As Gad pointed out in Paper 2 land use data for example tend to be incomplete and incompatible between surveys and among individual cities. Although aggregate indices of form such as land consumption were shown to vary with city size groups, the nature of these relationships has not been clearly established. This brief paper explores the association between a set of attributes of Ontario cities and city size. These attributes include population and land use densities, size of developed area, and land use composition. This paper acts as a link between the summary by Gad on aggregate land use statistics and the detailed analysis of intra-urban land use patterns to follow. Data and Methods of Analyses The data utilized relate to 51 urban areas, all in the province of Ontario, ranging in population from 1,652,300 (Metropolitan Toronto) to 700 (Tottenham). * A list of the urban areas included is given in Table 3.1. For each, estimates are available for total developed area, population density, and generalized land use composition. Included in the data are: year of survey, survey boundaries, population size, total developed area (gross population density), and acreages in residential, commercial, industrial, and institutional usage. The cities were chosen solely on the basis of data availability, rather than on any inherent characteris-

The data were initially provided by the Research and Special Studies Section of the Community Planning Branch in the Ontario Department of Municipal Affairs. This receipt is gratefully acknowledged. The staff of the Research and Special Studies Section undertook their own analysis on these data, the results of which have since been published in Department of Municipal Affairs (1969).

37

TABLE 3.1 CITIES AND DATA USED IN ONTARIO CASE STUDY

City

1 Toronto Metro 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51

Ottawa Hamilton Windsor London Lakehead St. Catharines Oshawa Brantford Oakville Kingston Sarnia Sault Ste Marie Guelph Brampton Belleville Timmins Waterloo Woodstock Barrie Stratford Richmond Hill Owen Sound Port Colborne Whitby Lindsay Cobourg Georgetown Port Hope Bowmanville Ingersoll Markham Dryden Espanola Streetsville Dunnville Picton Aylmer Acton Walkerton Stouffville Gravenhurst Kingsville Kincardine Caledonia Beamsville Woodbridge Durham Shelburne Cayuga Tottenham

Population (in OOO's) 1,652.3 276.8 264.1 187.4 171.1 100.8 84.4 77.1 55.2 53.0 52.9 51.2 50.3 37.1 33.7 32.0 29.0 28.0 22.2 20.9 20.2 18.0 17.5 14.8 14.8 11.3 10.4 9.3 8.1 7.3 7.1 6.7 6.3 5.5 5.3 5.2 4.9 4.6 4.3 4.1 3.4 3.2 3.1 2.9 2.7 2.5 2.5 2.4 1.3 1.0 .7

Land use: acres Commercial Residential Industrial 2,888 921 548

1,518 879 392 555 460 341 133 243 215 217 152 72 230 86 185 167 138 147 81 68 93 94 125 72 69 80 91 90 72 61 53 22 32 43 40 14 26 10 50 39 42 12 25 23 13 10 15 7

64,082 8,500 6,149 9,032 9,784 6,224 4,082 5,240 3,100 4,515 1,774 2,495 2,730 2,045 1,531 2,098 1,074 1,688 1,419 1,118 1,145 1,083 890 952 908 726 623 630 785 426

1,008 531 627 330 473 368 353 345 216 310 334 300 266 260 185 208 191 175 95 98 46

Open

21,524 2,356 7,608 3,445 1,577 1,931 1,616 3,290

18,523 3,408 2,194 1,006 1,132 1,467

963

801 160 762 650 280 256 385 155 206 483 218 170 252 20 106 26 58 75 125 43 170 84 156 155 172 137 123 49 28 32 13 29 21 90 18 26 55 26 161 54 -5 5

1,397 1,206 2,394 1,396 748 500 814 471 396 336 226 169 179 295 716 251 330 279 95 135 82 203 57 230 182 51 150 41 121 217 76 38 119 51 98 10 20 62 207 8 27 39

530

1,210

Gross density Persons per acre 15.2 16.8 14.8 11.6 10.4 8.5

11.5 7.1 9.8 7.9

11.0 8.2

10.0 11.0 11.0 8.7

15.8 8.6 9.2

12.3 10.3 13.5 12.3 8.1 7.1 7.2 8.1

10.9

5.7 9.3 4.7 7.5 5.5 7.2 7.9 8.4

10.5 8.0 9.4 8.2 8.4 5.5 8.3 6.1

10.3 8.4 5.5 4.8 6.8 5.2 6.7

tics of the cities as a representative sample. Also, the sample is not the same as that used in other studies on the same area, but the results should be applicable to this larger system. Several difficulties become immediately apparent. The 38

validity of a land classification is in doubt because the basic land use information was obtained largely from individual local authorities, each with somewhat different criteria and classificatory procedures. It may be regarded as sufficiently consistent for the purposes of this study only because of the high level of aggregation employed. Also the accuracy of the data varies depending on the definition of the urban area, on the level of aggregation and on variability in the data recorded. One example from the present study is variability in area definitions. In some cases measurements were made for the metropolitan area (Toronto), and in others (Ottawa) only on a city basis. Some caution must therefore be exercised in the interpretation of the results. The basic method -of analysis used is correlation and linear regression, initially utilizing raw data, and subsequently logarithmic transformations of these data. The general form of the relationship between urban form and size is that of the allometric growth equation (Nordbeck 1965), in which the increase in size of a statistic Y is equal to a constant fraction of the relative growth of a variable X, such that Y - aXb and in logarithmic terms Log Yi = log a + b log Xj + e. ^ Structural Correlates of City Size The simple correlations between all variables are shown in Table 3.2. From this table, developed area, population density and four land use measures were selected and regressed on the total population of each city. The resulting six regression equations are given in Table 3. 3, and a scatter diagram showing the line of least squares fit between developed area and total population is given in Figure 3.1. As expected, there is a strong almost exact positive correlation (0.987) between population size and the total developed area. The regression equation describing this relationship for the 51 Ontario cities is: LogTDEV = -0.42 + .87 log TPOP

[1]

From the regression coefficient (+. 87) we note that the amount of land consumed for urban purposes increases proportionately 2

This equation is used in Nordbeck (1965).

39

TABLE 3.2 INTER CORK ELATION MATRIX BETWEEN LAND USE VARIABLES: ONTARIO CITIES

1

2

3

4

5

6

7

8

9

-.27 -.14 -.09 .03 -.08 .04 -.08 -.12

.58 .25 .13 .49 .05 .23 .69

.58 -.29 .84 .24 .30 .46

10

11

12

13

14

.64 .04 .46

-.06 .09

-37

1 2 .99 3 .99 .99 4 -.08 -.14 -.01 5 .68 .59 .55 6 .95 .96 .96 7 -.13 -.12 -.12 8 .65 .89 .59 9 .93 .95 .91 10 .29 .31 .25 11 .09 .01 .09 12 .89 .91 .89 13 .25 .26 .25 14 .24 .19 .23 15 .66 .53 .57

-.3-8 -.08 .00 -.03 -.31 -.48 .61 -.18 -.12 .27 .23

.55 .12 .67 .62 .36 .07 .55 .16 .18 .81

-.01 .39 .89 .25 .07 .88 .29 .20 .53

-.80 .23 .03 .07 -.07 .41 -.17 .08 .45

N = 51

List of variables

CODE

1 2 3 4 5 6 7 8 9 10 11 12 13

TPOP TDEV GRES PRES RESD GCOM PCOM COMD GIND PIND INDD GPAR

Total population Total developed area Gross residential acres Percentage total developed area in residential use Persons per gross residential acres Gross commercial acres Percentage total developed area in commercial use Persons per gross commercial acres Gross industrial acres Percentage total developed area in industrial use Persons per gross industrial acres Gross parks and open spaces acres Percentage total developed area in parks and open spaces 14 Persons per gross parks and open space acres 15 Population density

PPAR PARD POPD ,

but at a lower rate than population size. Population densities increase with city size, because of higher prices paid for land in the larger cities and as an expression of differences in socioeconomic character and in the type of housing construction. From equation [1] we can derive the following equation for population densities in Ontario cities: Log POPD = 1.42 + .13 log TPOP

[2]

Here the regression coefficient measures the marginal decline in land consumed per unit of population. The second result worthy of note is the lack of a continuous and systematic relationship between the composition land use consumption and population size. In Table 3. 3 the equations for 40

Figure 3. 1 Relationship between population and total developed area: Ontario cities per cent of urban land in residential and commercial use are insignificant, while those for industrial and parkland are only of minor significance (at the .05 level). Clearly variations in the local economy and in measurement accuracy are of greater significance in explaining land use composition than is position in the urban hierarchy. The analysis was then revised to test whether land use structure varied in a step-like fashion between groups or levels within the hierarchy rather than between individual cities. The 51 cities were grouped into six size classes ( ±. 10) Und er pr edi ction Ingersoll +. 23 Oshawa +. 18 Durham +. 16 Dryden +. 16 Port Hope +. 15 Gravenhurst +. 12 Thunder Bay +. 12 Oakville +. 11 Woodbridge +. 10

Over predict! on -.22 Timmins -.18 Richmond Hill -.16 Caledonia -.14 Picton -.14 Owen Sound -.13 Barrie -.12 Ottawa -.12 Georgetown

Deviations from the Average Examination of the residuals from one regression equation (Table 3.4) provided some insight into the reasons for the variability in land use structure. Some of the residuals derive from underbounding of the urban area. Clearly this explains the Ottawa residual as the data refers only to the City of Ottawa, and not as it should to the metropolitan area. Most of the other negative residuals identify smaller centres, including primarily residential dormitory communities for Metropolitan Toronto, such as Georgetown and Richmond Hill. Dormitory communities generally have larger proportions of their total areas devoted to residential use and thus higher gross population densities overall. The equation thus underpredicts the total area in urban use in these communities because of a relatively lower level of support services and industrial development. Further generalization from these residuals is questionable. A number of smaller cities which deviate from the regression line have specialized economies—Oakville, Oshawa, Timmins, Thunder Bay, Ingersoll, Gravenhurst, for example—but the pattern is not consistent. The absence of systematic residual 43

variance suggests that no single factor or set of factors is operating to influence these relationships. The results also emphasize the difficulties of predicting small city structural dimensions (see also Siegel [1971]; Golant [1972]). Extending the analysis to measure the effects of the age of the area, the growth rate, proximity to a metropolitan area, the physical constraints on growth, is outside the scope of the present study; limitations on data for cities below 10,000 precludes the derivation of compatible variables for all cities. Comparison of Results With Those of Other Studies The above results are directly comparable to others done on a similar theme. Most meaningful for comparison is the study of Boyce (1963) on changing patterns of land consumption. Of particular interest are his regression equations of total developed area on total population for U.S. metropolitan areas. The correlation coefficients are of the same order although slightly lower than in the present study. Boyce confined his study to metropolitan areas of over 50, 000 population. As previously noted, the inclusion of small centres in this study could significantly alter the correlations obtained. TABLE 3.5 COMPARISON OF REGRESSION PARAMETERS—TOTAL DEVELOPED AREA ON POPULATION SIZE, ONTARIO AND U.S. CITIES

Boyce 1950 (U.S. cities) Boyce 1960 (U.S. cities) Present study (Ontario cities)

Correlation coefficient

Regression coefficient

r

b

0.84 0.87 0.987

.857 .8627 .8742

The most interesting comparison is with the regression coefficients derived by Boyce for 1950 and 1960 data (Table 3.5). The close association in the results suggests that a quite exact relationship may hold over a variety of areas. The critical parameter, the slope of the regression line, indicating per unit utilization of land with increasing city-size, is particularly close. The data possibly indicate that Ontario cities lag behind their U.S. counterparts in land utilization by at least a decade. Boyce also notes that overall consumption has increased over time, as 44

the regression line has shifted upward, the regression coefficient increasing only slightly. Bartholomew (1955), in his study of land use in American cities, found a definite relationship between the total developed area of a city and its population (not with the same method of analysis), as well as with increasing densities. One difference is his finding that the percentage of area devoted to commercial use also increased with city size, although a detailed analysis of his data indicates that the relationship is weak. In the present study, the relationship proved statistically insignificant. There is an equally weak yet significant relationship between percentage of the area which is industrial and population size, and similarly the percentage of area in parks and open spaces. Bartholomew found no relationship at all in these latter two categories. Conclusions and Implications Several relevant conclusions should be restated. The first is the close to perfect relationship between developed area and population size, and to a slightly lesser extent between population density and city size, for this particular set of Ontario cities. These results agree with those of U.S. cities. Unfortunately the absence of time series land use data in the Ontario example as yet precludes comparison of land consumption rates over time. It has also been shown that the factors determining urban land use composition are largely independent of city size. Only weak relationships are found between proportions of area devoted to the various major uses and city size. Other equally important considerations such as economic mix, the age and character of development, and recent growth rates must be taken into account if a fuller understanding of the evolution of urban form is to be achieved.

References

ALONSO, W. 1970. "The economics of urban size." Working Paper No. 138. Berkeley: Center for Planning and Development Research. University of California . BARTHOLOMEW, H. 1955. Land use in American cities. Cambridge, Mass.: Harvard University Press.

45

BERRY, B. J. L.; SIMMONS, J. \\ . and TENNANT, R. J. 1963. "Urban population densities: structure and change." Geographical Review 50, No. 2: 389-405. BOYCE, R. 1963. "Changing patterns of land use consumption." Professional Geographer 15. No. 2: 19-24. GOLANT, S. 1972. "Regression models of urban growth in Ontario and Quebec, " in L. S. Bourne and R. D. MacKinnon, eds. "Urban systems development in central Canada." Department of Geography Research Publication 9. Toronto: University of Toronto Press. HOYT, H. 1968. Urban land use requirements 1968-2000. The land area required for the future growth of the urban population of the United States. Washington, B.C.: Homer Hoyt Institute. MANVEL, A. D. 1968. "Land use in 106 large cities, " in "Three land research studies." Research Report No. 12. Washington, B.C.: National Commission on Urban Problems. NORBBECK, S. 1965. "The law of allometric growth." Biscussion Paper No. 7. Michigan Inter-University Community of Mathematical Geographers. Ann Arbor, Mich.: University of Michigan. ONTARIO BEPARTMENT OF MUNICIPAL AFFAIRS. 1969. Urban land use in Ontario: areas and intensities. Toronto: OBMA. SIEGEL, J. 1971. "An empirical study of the urban hierarchy and its relation to growth in southern Ontario." Research Paper No. 46. Toronto: Centre for Urban and Community Studies. University of Toronto.

4

Descriptive patterns of urban land use: A summary* L. S. Bourne

In an earlier paper, Simmons (1964) argues for an elaboration of the traditional models of urban spatial structure. These *This paper is a revised and shortened version of a previous research report (see Bourne 1970).

46

models: concentric zone, sector and multiple nuclei, describe aspects of the variation in the internal pattern of cities. Although held to be in conflict at one time they have been shown, particularly through the methods of social area analysis and more recently by research described as factorial ecology (Economic Geography 1971) to be cumulative and additive explanations of urban social structure. Nevertheless, these models do not encompass the totality of urban land use. As the literature on factorial ecology suggests, such models are only three of several dimensions summarizing regularities in spatial variations in residential land use. Simmons suggests a parallel "factorial ecology" treatment of land use distributions in which a matrix of measures of land use is reduced to a set of underlying spatial components or dimensions.! This paper briefly summarizes the results of a series of descriptive analyses of land use in Metropolitan Toronto. Despite its simplicity, it is surprising that no parallel example has appeared in the literature since assumptions regarding the distribution patterns of land use are common to most generalizations and to most planning models of urban form and growth, and given that a well developed analytical context for such an analysis already exists in studies of social area characteristics (Murdie 1969). The results discussed below are of course not an end in themselves, but are related to larger studies of land use forecasting and of identifying the relationships between social and physical attributes in the city.2 Data and Procedures The basic data source consists of the acreage of land in 29 different categories of use for each of 301 census tracts in Metropolitan Toronto, for 1964, 1966 and 1968. 3 Only the 1964 results are reported here. The total acreages in each of these 29 categories are summarized in Table 4.1. The resulting percentage distributions accord modestly well with measures of land use composition in other urban areas (Manvel 1968-, Niedercorn and Descriptions of different factor analytic models are contained in King (1969). 2

This latter project, entitled "Social and Physical Space in the Metropolis" was supported by the Canadian Council on Urban and Regional Research. 3 These data were provided by the Metropolitan Toronto Planning Board. Similar analyses were performed on the data sets for each of the three years although emphasis was given to the 1964 datum because its timing conforms to the availability of other data.

47

TABLE 4.1 BASIC LAND USE STRUCTURE (METROPOLITAN TORONTO, 1964)

No. 1 2 3 4 5

Land use category Gas stations Offices Retail Hotels Parking

Total commercial 6 7

Private schools Elementary and secondary public schools 8 Elementary and secondary separate schools 9 Universities 10 Churches 11 Hospitals 12 Government 13 Stadiums, arenas, other Total institutional 14 15 16 17

Total acres

Per cent of total area

Per cent of developed area

677.9 398.5 3321.0 201.7 288.4

2.16

3.10

.13 .19

.19 .27

4887.5

3.18

4.56

157.5

.10

.15

2566.0

1.67

2.39

214.5 215.8 724.4 526.6 131.3 1337.2 5873.3

.14 .14 .47 .34 .09 .87

.20 .20 .68 .49 .12

Parks 10650.0" 2522. 6 Golf courses 1264.4 Cemeteries Drive-ins, other private open space 6487. 1

.44 .26

3.82 6.93 1.69

.63 .37

1.25 5.48

4.22

9.93 2.35 1.18 6.05

20924.1

13.61

19.52

8346.5 356.5 1639.5

5.43

7.78

1.07

1.53

Total industrial

10341.6

6.73

9.64

21 22 23 Total

Apartments Row multiple housing Single-family housing residential

2268.8 1007.5 50659.8 53936.1

1.48

2.12

32.97 35.11

47.25

24 25 26 27

Railroads and yards Expressways and interchanges Hydro Other utilities

2539.6 3086.3 3158.2 2472.9

1.65 2.01 2.06 1.61

2.37 2.88 2.94 2.31

Total transportation and utilities

11257.0

7.33

10.50

28 Agricultural 29 Vacant Total undeveloped land

3398.1 43022. 1 46240.2

2.28 28.00 30.24

TOTALS

148983.3

Total open space 18 19 20

Industrial Industrial reserve Warehousing

.82

.23

.66

100

.33

.94

50.30

100

Hearle 1963; Clawson 1971) but only at this gross level of generalization. Excluding roads and streets, residential uses occupy about one half of the developed area, public and private 48

open space 20 per cent, industrial and transportation related uses each about 10 per cent, and commercial and institutional uses about five per cent each. The rationale of the subsequent analysis is to isolate the degree of covariance in the spatial expression of these uses and thus to reduce the underlying structure to a few basic components. The approach utilizes principal components analysis as a descriptive device, following on the standard procedures of factorial ecology (see Berry [1971]). Here, the technique has been applied to different modifications of the same basic data source: to raw and percentage land use; to the total urban area and the developed urban area; and to three sets of spatial grid units 1) census tracts (n-301), 2) zones of approximately equal population (n-62), and 3) to zones of approximately equal area (n=59). Most of the discussion in this paper relates to the analysis of percentage land use data for census tracts. At the end of the paper a brief comparison is made of the resulting factor structure for all three of the above units of analysis. Table 4.1 also illustrates the limitations of standard land use measurement and classification procedures. First is the obvious problem of mixed classification criteria, such as structural type (apartments, row-multiple and single-family residential), location of ownership (government, public open space) as well as strictly functional criteria (industrial, retail, etc.). There are also problems of measurement scales. Land use data is classified into nominal or mutually exclusive categories, setting this analysis apart from typical factorial ecologies. Further, all land uses are weighted equally, even though they clearly operate at different densities and reflect differing scales of locational decisions. Also different patterns result when land use is measured in absolute (raw) or relative terms (percentage); the former is largely a scalar (size) effect while the latter measures areal homogeneity. These problems should be taken as a note of caution in interpreting the following results. Empirical Results The first stage in the analysis is the derivation of a matrix of simple correlation coefficients (Table 4.2) between the proportions of each 300 tracts in each of 29 land use types. 4 The cor4 The area under study is the municipality of Metropolitan Toronto. One census tract, the Toronto Island, was dropped from the data set. Also the number of land use categories was reduced to 28 in the 1968 survey (combining industrial reserves with the general industrial category). The present analysis, however, maintains the 29 categories used in 1964.

49

TABLE 4.2 SIMPLE CORRELATION MATRIX FOR PER CENT LAND USE Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Gas stations Offices Retail Hotels Parking Private school Public school Separate school Universities Churches Hospitals Government Stadiums Parks Golf Cemeteries Drive-ins Industrial Industrial reserve Warehousing Apartments Row multiple Single-family Railroads Expressways Hydro Other utilities Agriculture Vacant

1

2

3

4

5

6

7

8

09 38 10 11 -03 10 12 -04 14 -11 02 02 -17 -13 -07 -10 15 06 04 -01 04 03 03 -11 10 -10 -05 -09

29 54 65 -Ol -12 -04 06 19 01 12 19 -07 -03 -03 -03 02 -03 -05 02 01 -23 -07 -07 -06 -02 -02 -09

14 33 -05 11 16 04 46 -07 13 12 -24 -14 -12 -21 00 -07 -05 -03 24 07 -06 -21 -11 -12 -07 -33

52 -04 03 05 00 20 00 -01 27 -05 -03 -04 01 02 -03 -05 00 23 -26 -04 05 -07 -05 -02 00

-03 -14 -01 10 22 04 18 38 -07 -04 -04 -06 06 -02 06 -03 12 -32 01 -07 -07 02 -02 -11

-05 -07 00 -04 -02 -01 -02 -02 -03 -04 07 -08 -02 -05 20 -03 09 -05 -05 -03 -03 -02 -07

18 08 12 -12 -06 -12 -12 -08 -05 -13 -15 01 -14 06 17 21 -07 -05 08 -15 -04 -11

-04 20 -05 01 -03 -05 -04 -04 -07 -04 -02 -05 -01 07 09 -05 -10 -01 -04 -03 -07

9

10 11 12

-05 00 -08 43 -01 03 16 17 00 01 -22 -06 -01-09 -02 00 -08 -01 -03 -07 -04 -04 -13 -07 -01 -10 -02 -03 -11 -03 -03 11 -07 -02 21 00 -12 15 22 -04 -15 -06 -04 -14 -05 -02 -15 -04 -02 -11 05 -01 -05 -01 -02 -21 -06

13

14

18 -06 -03 -02 -01 -01 -05 -01 -02 00 08 03 -05 -04 03 -03 02 03 26 14 -31 -06 06 06 11 -05 -07 -01 -04 -01 -02 03 -10

-06 -03 05 -09 03 -10 -02 -05 -22 -06 16 03 -04 17 00

15

16

17

-01 02 00 -08 -08 10 -02 -03 -01 -07 -07 -04 -01 -12 -13 -04 -03 02 -04 -02 -01 05 -04 00 -02 00 -04

-12 -02 -09 00 -05 -13 -10 07 02 07 04 25

18 19 20 21 22

05 27 00 -11 01 00 -03 -43 -10 35 01 -01 05 04 10 07 20 -02 00 04 -01

23

24 25 26 27 28

-10 01 06 -30 04 -32 44 -09 -08 -24 07 12 -01 -21 08 02 -04 -07 -12 -06 01 13 -06 -04 -25 05 -04 -03 -01 -03 02 -03 -04 -01 04 -02 05 -15 -10 -33 -09 10 18 -01 01

relations are almost universally low. These weak associations attest to the measurement problems noted above, but more important, they suggest a very high degree of independence among major land use distributions in the city. If this assertion of independence is accurate, what does it do to the underpinnings of our concepts of urban spatial structure? What of the logic and reliability of operational planning and transportation models, most of which are based on a priori assumptions concerning the interdependence of different spaceusing activities in the city? Clearly the absence of many strong interdependencies in Table 4.2 raises serious doubts about the applicability of these assumptions. In the resulting factor analysis (Table 4.3) 11 factors were able to account for only about 62 per cent of the spatial variance among the 29 land use types. This relatively low level of "explanation" is to be expected given the weak correlations in Table 4.2, and the standard methods of defining land use. Yet the results seem sufficiently reasonable to encourage proceeding. The first three factors identify the key underlying physical patterns of land use in Metropolitan Toronto. These may be summarized as: 1) a core area characterized by a functional mix of offices, hotels and parking; 2) a sectoral pattern of industry and warehousing adjoining the major rail lines radiating outward from the city centre; and 3) space extensive uses and vacant land typically dominant on the urban periphery. When mapped, the factor scores for the latter increase regularly with increasing distance from the centre of the city. The remaining factors identify functional nuclei and concentrations of single-purpose and usually space extensive uses. These indicate the mutually-exclusive status of some land use environments in all cities, and the particular ingredients of certain local environments within Toronto. These nuclei are the most difficult to conceptualize and are clearly the source of much of the unexplained variance in traditional studies of urban ecological structure. The first of these concentrations (factor four) is the (partial) network pattern of the expressway system. At the time of data collection (1964) there were few such expressways in the city, and the impact of those completed was only beginning to appear, which may account for their apparant independence of all land use types. The fifth factor isolates the extensive areas of predominantly institutional use in the city. In the Toronto example 51

TABLE 4.3 FACTOR LOADINGS FOR TOTAL URBAN AREA: PER CENT LAND USE DATA Factor variable -Jjo, no^_ Factor loading Variable name 1

2 4 5

. 80435 .75886 .83189

Per cent variance explained Each factor Cumulative

Offices Hotels Parking

9.52

9.52

-.70419 -.72969 -.76045

Industrial Warehousing Railroads and yards

7.38

16.90

20 24

17

-.52557

6.93

23.83

29

-.81511

Drive-ins, other private open space Vacant land

25

. 66485

Expressways and interchanges

5.86

29.69

9

.81357 .82196

Universities Government

5.41

35.10

12

7

.52447

5.31

40.41

22

.67822

Elementary and secondary public schools Row multiple housing

19

-.71852 -.62451

Industrial reserve Other utilities

5.00

45.41

27

8

1

.56751

Gas stations

4.83

50.24

9

14

-.50846

Parks

3.98

54.22

10

15

.52048

Golf courses

3.67

57.89

11

16

.73121

Cemeteries

3.55

61.44

2

3

4 5 6

7

18

NOTE: 29 variables, 300 census tracts Variable numbers refer to Table 4.1

this reflects the proximal location of the provincial government offices and the University of Toronto on the periphery of the central core. The sixth factor includes the only category of residential land use to emerge in the analysis. Row-multiple housing is combined with public schools, both of which are expressions of the older and relatively stable neighbourhoods in the east and west ends of the central city. The minor position held by residential uses in this factor structure is likely the result of employing proportional rather than raw measures. Proportions tend to differentiate between subareas of the city primarily on the basis of homogeneity of use, particularly those areas dominated by a single type of use. Residential areas, as neighbourhood 52

units, by definition contain a mixture of institutional, recreational and local retail activities and thus are ranked low in the factor structure relative to more homogeneous and often single-purpose land use environments. The five remaining factors identify several of these singlepurpose environments: planned industrial reserves and parks, service stations, parks, golf courses and cemeteries. These nuclei tend to appear in a whole variety of locations. Also, in this study they tend to be highly unstable, that is they appear irregularly in the factor structures when the units of analysis , urban boundaries, or type of measurement (raw or percentage data) are varied. In fact, in most instances they simply reproduce the categories in the original land use data source.5 Comparison of Raw and Percentage Analyses It is worthwhile at this point to briefly discuss differences which emerge when the raw data in Table 4. 1 are subjected to exactly the same procedures as applied to the proportional data (Table 4.3). The resulting factor structure is summarized in Table 4.4. As might be expected the effects of scale or size differences give considerable emphasis to suburban areas. Instead of eleven factors as in Table 4.3, only eight factors are obtained but they encompass a similar proportion (62 per cent) of the initial variance. In contrast to the analysis of proportional acreage, the first factor consists of the low-density and space- extensive uses typical of the suburban fringe. This factor alone accounts for nearly one-fourth of the cumulative variance explained. Further, the factor denoting high density uses of the core area, which ranked first in Table 4. 3 appears largely intact in Table 4.4, but is ranked fifth in importance. Also, industrial and warehousing uses split from railroads and merge with expressways, hotels and service stations to form a new dimension typifying the sprawling commercial and industrial sectors of the suburbs. Of considerable interest is the emergence in factor two of a set of raw land use measures which are descriptive of residential neighbourhoods. This factor, which did not emerge in the analysis of proportional data, groups single-family housing with those uses typically scattered through residential areas: retail, 5

It should be stressed that most existing land use classifications are based on mutuallyexclusive subdivisions of use; that is, a parcel of land is assigned to one and only one type of use.

53

TABLE 4.4 FACTOR LOADINGS FOR TOTAL URBAN AREA: RAW LAND USE DATA Factor variable no. no. Factor loading Variable name 1

14 17

.55211 .90356

Parks Drive-ins, other private open space

24 26

.78620 .78461

Railroads and yards Hydro

28 29

.91754 .88924

Agricultural Vacant

3 7

.52028 .83822

8

.58762

10 23 3

4

Per cent variance explained Each factor Cumulative 15.6

15.6

11.0

26.6

.61648 . 86463

Retail Elementary and secondary public schools Elementary and secondary separate schools

Churches Single-family housing

1 4 18 20 25

.60891 .67938 .82586 .72286 .71783

Gas stations Hotels Industrial Warehousing Expressways and interchanges

10.4

37.0

15 19 27

.60349 .83778 . 74463

Golf courses Industrial reserve Other utilities

6. 1

43.1

2

.80686 . 84275

Offices Parking

5.5

48.6

.76199 .71495

Universities Cemeteries

5.2

53.8

16

7

13

.65593

Stadiums, arenas and other

4.4

58.2

8

11

.67050

Hospitals

4.1

62.3

2

5

5

6

9

29 variables, 300 census tracts

elementary and secondary schools, and churches. Again it should be noted that this is based solely on variations in the mix of land uses across the city. Thus neighbourhoods appear not as social entities, but as areas of homogeneous physical attributes. The other major difference is the contrasting number and composition of factors previously described as functional nuclei. Since most of these are based on very marginal correlations, instability is expected and little significance should be attached to their detailed outline. Yet it is interesting that the same types of functions appear in these factors as in Table 4.3—institutional and open space uses in particular—all of which tend to be spaceextensive. 54

Land Use, Densities, Accessibility and Stock Quality None of the above analyses adequately reflects the complexity of the urban physical landscape. Land use typically says nothing about the density, quality, and age of buildings or activities. As a step in this direction, data on gross densities of employment distribution (by place of work), on general accessibility, and on housing stock quality are introduced, and the analysis repeated. The intended effect of combining employment with land use is to reduce the statistical weights attached to space-extensive and peripheral uses and to increase specialization within the aggregate factor structure. Employment densities rather than totals are utilized to avoid size effects. Net population densities are also added to provide a more refined method of differentiating between residential environments than is provided by the housing type measures in the land use inventory. Total population is subsequently introduced as the single criterion of size of spatial unit and extent of urban development, and one that would not emphasize the central business district. Four accessibility measures, distance to the centre (peak land value intersection) of the city, distance to the centre of gravity of metropolitan population, and travel times to both centres by automobile and public transit, are also added. These are considered as surrogates for the traditional concepts of centrality, interaction, and the operation of competitive processes in the urban land market. Auto and transit times, however, are highly collinear and the latter are subsequantly eliminated. Nine other variables are added to describe the attributes of the dwelling stock: age, rent, tenant status, length of occupancy, crowding, and deterioration. Table 4.5 summarizes the resulting factor structure. There are ten factors, accounting for nearly 60 per cent of the variance in the expanded set of 44 variables. The inclusion of employment densities further emphasizes the CBD as the dominant influence in the economic structure. All density variables, except construction and government employment, appear as coreoriented patterns loading on factor one, independent of each accessibility measure and the concentric pattern of population density. This latter pattern of population density is then added as the second factor combined with all four accessibility measures, the proportion of vacant land, age of housing and length of occupancy. It is apparent that inclusion of four such accessibility variables, with high intercorrelations between respective pairs, 55

TABLE 4.5 FACTOR LOADINGS PER CENT LAND USE DATA, ACCESSIBILITY, EMPLOYMENT DENSITY AND STOCK QUALITY Per cent variance each factor

Per cent explained cumulative

13.4

13.4

-.779 -.689 -.936 -.893

Offices Hotels Parking Primary industry employed density Manufacturing employed density Transportation and commercial employed density Wholesaling employed density Retail employed density Finance, insurance employed density Personal service employed density

2

-.569 -. 874 -.860 -.792 -. 646 .593 .591 .628 -.814

Vacant Distance to Queen/Yonge Distance to centre of population Time by car to Queen/Yonge Time by car to centre of population Population density Per cent occupied > 10 years Per cent constructed before 1920 Per cent constructed since 1945

10.9

24.3

3

.604 . 619 .535

Row multiple housing Per cent crowded dwellings Per cent constructed before 1920

5.8

30.1

4

-.775 -.651 . 454 -.708 -.525

Industrial Warehousing Single-family housing Railroads and yards Manufacturing employed density

5.4

35.5

5

-.785 -.582 -.772

Apartments Average contract rent Per cent occupied < 1 year

4.7

40.2

6

-.523 -.583

Parks Expressways and interchanges

4.3

44.5

7

-.854 -.727

Hospitals Government and community employed density

4.1

48.6

8

-.597 -.813 -.505

Universities Government Per cent dwellings in need of major repair

4.1

52.7

9

-.601 -.466 -.509

Drive-ins, other private open space Agricultural Vacant

3.5

56.2

10

-.608 -.631

Industrial reserve Hydro

3.4

59.6

Factor Factor no. loading 1

-.953 -.670 -.733 -.881 -.502 -.680

Variable name

has some effect in producing this separate component. Yet it must also be concluded that distance to either the commercial or 56

geographic centre does not offer nearly as much insight into the relative dimensions of urban land use as might be expected. Similar complexity emerges in other factors. Factor three combines variables denoting row-multiple housing, the census index of crowding and the per cent of dwellings in that tract constructed before 1920. These clearly pick out areas of older housing in the inner city. Other residential variables, such as average rent levels, the variable denoting high turnover rates (per cent occupied < 1 year) are shown by factor 5 to increase directly with the proportion of an area T s housing stock in apartment units. Two other housing stock variables, median value and proportion owner-occupied, did not appear in any of the factors. The fourth factor, as in Tables 4. 3 and 4.4, identifies the older industrial-warehousing sectors paralleling the main rail lines. Only two of the other variables show any significant association with these land uses: manufacturing employment density tends to increase within these zones, and the proportion of the housing stock in single-family units declines. Both relationships are reasonable descriptions of this type of land use. Subsequent factors differentiate specialized nuclei and large tracts in a single low-density use: parks and expressways, institutional uses, fringe open space uses and vacant land, peripheral planned reserves and public utilities. One of the two institutional factors (eight) again merges government and university concentrations as in the analysis of proportional land use distribution. The appearance of the deteriorated housing variable in association with this factor, although based on a very weak correlation does nevertheless raise some interesting questions concerning the importance of externalities from public location decisions in influencing the urban structure. The addition of these attribute characteristics to the data inventory improves the clarity of the results. The composite factor structure, incorporating land use, accessibility, employment, density, age and quality measures for the housing stock, may be summarized in terms of underlying dimensions as follows: 1) 2) 3) 4) 5) 6)

CBD—commercial core area Distance—density-age gradient (concentric) Older inner-city residential areas Industrial-railroad sectors Apartments—luxury residential—mobility Open space 57

7) 8) 9) 10)

Institutional—hospitals Universities—government—slums Fringe uses Utilities

Since mapping of all of these factors here is impossible Figure 4.1 attempts to display in general terms the major components of urban structure as a series of cumulative spatial patterns superimposed on a physical base. Stability of the Dimensions Testing the cross-sectional spatial stability of the preceding generalizations could be undertaken in a number of ways. In this study, the initial land use inventories were simply aggregated from census tracts into two alternative grid systems as previously described. One set approximates an equal area random sampling grid (n-59), the other aggregation combines census tracts into areas of similar total population and homogeneous social characteristics (n-62). 6 The factor analyses described above are then repeated in exactly the same format for both new grid systems. The results of one of these comparative analyses, summarized in Table 4. 6 show quite remarkable stability in the groupings of land use types. As expected, there is a shift in the order of importance and variance represented by different factors, and the total variance explained increases with the smaller sample size of the coarser grid systems. Note that this table refers to land use specifically (see Table 4.1), and does not include measures of density, accessibility or quality. Subtle shifts of course occur throughout and since the original loadings are not reproduced here, the reader will have to assume that the factor names are reasonably representative of the variables loading on that factor. As expected, not all of the factors identified, particularly the small specialized nuclei, emerge in all three analyses. The most difficult comparison and the most unstable of the factor solutions derived, although this may not be immediately apparent in Table 4. 6 is in the analysis based on equal size areas. Ideally this analysis should have been repeated for several different grid locations and the results averaged to 6 The selection of a roughly similar number of cells in both examples is an attempt to avoid the well known tendency for reductions in the total variation explained to be directly related to the number of observations or grid cells employed.

58

Figure 4.1 A schematic model of urban structure: a Toronto land use example 59

TABLE 4.6 COMPARISON OF LAND USE STRUCTURES FOR DIFFERENT SPATIAL GRIDS: PER CENT LAND USE

Factor

Description

1 2 3 4 5

CBD, core area Industrial, railroads Fringe area Expressways Universities, government Core area, residential Industrial reserves Ribbon commercial Open space Golf courses, utilities Cemeteries Institutional, schools Schools, apartments

7 8 9 10 11 12 13

TOTALS

Equal population

Equal area

N=300

N=62

N=59

Per cent Per cent Per cent variance Variables variance Variables variance Variables explained loading* explained loading* explained loading*

no.

6

Census tracts

13.2

5.1

5 3 7 1

5.3

4 3 7 2

2

5.4

2

9.7

4

5.3 5.0 4.8 4.0

2 2 1 1

6.9 5.8 5.2

5 2 2

4.2 4.2

3 1

3.7 3.6 -

1 1 -

4.3 5.0 -

1 1 -

7.1 5.8 5.8

3 1 2

9.5 7.4 6.9 5.9

3 3 2 1

5.4

61.4

19

15.1 9.2

12.0

74.0

29

5.7

13.7

74.4

29

For definitions of zones and for summaries of specific variables see text. * Number of variable loadings on varimax rotation > *.0.5.

obtain an average factor structure. Despite these shifts what does emerge clearly is the prominence of certain basic dimensions of urban physical structure in Metropolitan Toronto. Application of different spatial filters in the form of larger grid units tends to emphasize the importance of the core area, the industrial-transportation sectors and the fringe area vacant land patterns. The latter dimension, at least in the analysis based on zones of approximately equal area, further emphasizes the relative importance in both scale and homogeneity of the suburban fringe. The revealing implication is that the cross-sectional dimensions identified above are not unique to one specific level of spatial aggregation. On the other hand, there is not a set of ubiquitous factors for all levels. Marginal correlations among land use types encouraged frequent regrouping among the variables denoting smaller categories of use from one analysis to another. Also, land use zones tend to display degrees of internal homogeneity which varies over differing sizes of area. Thus one 60

texture of spatial grid may be appropriate in measuring the variance of one land use distribution, but may be quite inappropriate for another. Analyses of land use changes, and trends in land utilization, are frequently based on the assumption that one level of aggregation, categorically or spatially, is sufficient as a basis for extrapolation. It is not surprising that such results are suspect in their present form. The composite factor structure resulting from the entire series of analyses can be summarized as revealing the following independent land use dimensions: 1) a core area representing the CBD and the surrounding fringe; 2) sectors of industrialwarehousing and railroad uses; 3) space extensive uses and highway-oriented commercial uses; 4) medium density residential uses and schools; 5) the juxtaposition of governmental and university land uses; 6) fringe area uses such as golf courses and utilities; 7) apartments; and 8) vacant land. In all analyses only the first three dimensions remained the same: the CBD or core area, industrial-warehousing areas following the railroads, and a composite index of space-extensive and highway-oriented commercial uses. Finally, superimposed on this factor structure are concentrations of specialized functions and extensive areas in a single use. It may be postulated that these nuclei differ in number, location, and composition with, and in fact mirror, the unique character and historical development of each city. They may, as in Toronto, encompass large expanses of parks, hospitals, cemeteries, public service utilities, fringe uses, and other institutional kinds of space utilization. Obviously they are also in part a function of the size of areal unit and the particular scale of measurement employed. Yet these clusters of specialized uses may provide the most insight into the components of land use which underlie and give a distinctive character to the traditional models of urban spatial structure. The traditional models are inadequate to grasp the complexities of urban land use. This analysis, in addition to summarizing the dimensions of land use in Toronto, may also lay part of the groundwork for more comprehensive models of urban structure. The sequential application of factor analysis to various subsets of the data and to differing definitions of the urban area of interest reveal a markedly unstable and complex factor structure. Density gradients, sectors, and nuclei, for example, are adequate descriptions of only part of the metropolitan land use 61

surface. A distinctive core and periphery must be added, and a dimension of neighbourhood land use units must be superimposed on the aggregate structure.

References

BERRY, B. J. L. 1971. "Introduction: the logic and limitations of comparative factorial ecology," in Economic Geography Special Issue 47, No. 2: 209-19. BOURNE, L. S. 1970. "Dimensions of metropolitan land use: cross sectional structure and stability." Research Paper No. 31. Toronto: Centre for Urban and Community Studies. University of Toronto. CLAW SON, M. 1971. Suburban land conversion in the United States; an economic and governmental process. Baltimore: Johns Hopkins for Resources for the Future Inc. ECONOMIC GEOGRAPHY. 1971. "Comparative factorial ecology," Special Issue, B. J. L. Berry, ed. 47, No. 2. KING, L. J. 1969. Statistical methods in geography. Englewood Cliffs, N. J.: Prentice-Hall. MANVEL, A. D. 1968. "Land use in 116 large cities," in "Three land research studies." National Commission on Urban Problems Research Report No. 12. Washington, B.C.: GPO. MURDIE, R. A. 1969. "Factorial ecology of Metropolitan Toronto, 1951-1961." Research Paper No. 116. Chicago: Department of Geography. University of Chicago. NIEDERCORN, J. H. and HEARLE, E. F. R. 1963. "Recent land use trends in fortyeight large American cities." Memorandum RM-3664-1-F. Santa Monica, Calif.: Rand Corp. SIMMONS, J. W. 1964. "Descriptive patterns of urban land use." Canadian Geographer 9, 3: 170-4.

62

5

Application of the Lowry model of urban structure to Toronto* P. D. Harper

During the past decade urban research and planning has been transformed by the design and development of comprehensive activity allocation models. The allocation of different activities to subareas is an important aspect of urban modelling as it provides major inputs to land use and transportation planning, and in addition, it offers a means through which changes in the urban system can be better monitored and understood (Batty 1971). Apart from using these models for evaluating change in spatial systems, it is important to recognize that any urban model is a translation of a hyopthesis about the organization of the urban system into a form which can be manipulated and tested against observations in the real world. Formal models of urban systems thus are powerful tools for testing and improving various hypotheses of spatial structure. This paper examines one activity allocation procedure originally developed by Lowry (1964) and modified by Echenique et. al. , (1969) and applies it to Metropolitan Toronto. Emphasis is placed on the methodology of calibrating the allocation functions and on the sensitivity of the model to parameter variation. Model Overview The Lowry model is designed to replicate the actual structure of a city by distributing locally dependent employment and population to zones of the urban region after the distribution of basic or T site constrainedT employment is exogenously specified. The underlying theory of the model assumes that, given the distribution of employment in the basic sector, service employment! This paper is a revised and considerably abridged version of Harper (1972). 1

Although the terminology is confusing, it should be recognized that these two employment classifications actually bear no functional relationship to the distinction made in

63

and population can be derived; and that the spatial distributions of these activities are consistent with the distribution of basic employment. First, through the application of an activity rate, the population associated with the basic employment is determined. This population is then allocated to different zones in the system, thus initiating the first increment of service employment. Use of a population- serving ratio then determines the level of service employment associated with this initial distribution of population and this service employment can be allocated amongst the zones. This service employment leads to further population increments and in turn to further increases in service employment. The iteration continues until the population to be allocated is negligible. However, constraints on the amount of land available may require additional iterations of the model until such constraints are satisfied. Structural Equations of the Model In the Lowry model, the allocation process is carried out through a linked set of simple structural equations each defining a relationship in urban structure. The sequential process is shown in Figure 5.1. The first equation defines the area available for receiving activities. By multiplying the number of basic employees (BEMP) in each zone i by a scalar BSS, the average number of acres used per basic employee, and subtracting this from the total land available (TOTA) in each zone i, the land available for residential and service activities (SRASj) is obtained: that is, SRASi = TOTAi - [BEMPi • BSS]

[1]

Multiplying the number of employees in each zone by the labour force participation rate (LFPR) gives the total population in each of the j zones. POP

= BEMPj • LFPR

economic base studies. The classification made by Lowry (and retained in this study) is based on the locational characteristics of the employment rather than on its structure. Thus, service employment consists of those occupational groups whose location is determined by the location of its local market or its service area; these are considered "residence oriented" or population serving. The residual employment, the basic sector, consists of those occupations or industries which do not locate in response to local demands, buUto other structural or economic influences and are considered, at least in Lowry's terminology, as "site-oriented."

64

[2]

The population associated with places of employment (POPj) is then allocated over all zones using the operator Ay , resulting in a residential distribution RAj RPi = POPj •

[3]

Ajj

where 2 Ay = 1 for all j zones. i The operator AJJ is determined from a given journey-to-work distribution and is a function of the available space in the zone of destination (SRASi) and the cost of travel (Dy). These distribution functions are discussed in the next section.

Figure 5. 1 Generalized flow chart of the Lowry Model The distribution given by equation [3] is constrained by the amount of available land in the destination zone. The number of residents (RPi) is translated into residential acres (RACi) by multiplying RPi by the residential space standard scalar RSS 65

(residential acres per person). RaCi = RPi

• RSS

[4]

Since residential use in any zone cannot exceed SRASj, the residential overflow of each zone (RXi) in acres is calculated as RXi = RACi - SRASi

[5]

Fence RYi = RXi • RSS'1

[6]

where RYi = vector of population overflow. The product of the population in each zone (RPi) and the service employment to population ratio (SEPR) is the number of service employees (SERVO generated by the population allocated to zone i. SERVi = RP. • SEPR

[7]

This service employment is then distributed using the operator By which is derived from a given journey-to-shop distribution. The number of service employees in all zones j (SEMPj) are distributed from all zones i using the operator By. SEMPj = By

- SERVi

[8]

As with the residential distribution, the service employment distribution is constrained by the amount of space available. The number of service employees is translated into service land (SACj) by multiplying SEMPj by the service space standard SSS (service acres per service employee). SACj = SEMPj • SSS

[91

The space constraint is then expressed as RACj + SACj < SRASj

[10]

If the sum of the space required by both residential and service activities in zone j exceeds the space available, then any residential activities previously allocated are displaced by the service activities, resulting in an overflow of residents (RYSj) from zone j. Since RXSj = [SACj + RACj] - SRASj

66

[11]

then RYSj = RXSj - RSS-!

[12]

where RXSj = amount of residential land in "overflow.IT In each zone, two kinds of residential overflow (ROi) are then possible: one generated by the lack of available space in the original residential distribution (RYi in equation [6]) and the other by the displacement due to the service activity allocation (RYSi in equation [12]). Adding these together gives the total amount of displaced residential activity. ROi = RYi + RYSi

[13]

This displaced residential activity in each zone had previously generated service activity which must be subtracted. This is given by SSEj = By • (SEPR • ROi)

[14]

where SSEj - service employment in zone j generated by the residential overflow in zone i (ROi) BIJ = operator as expressed in equation [8], SSEj is then subtracted from the original calculation of service employment in each zone, SEMPj. The residential overflow in each zone (ROi) must be reallocated to those zones which have not yet reached capacity. This is accomplished by converting the population back to employees and then returning these employees to their zones of employment using the operator Ay. Explicitly:

EROj = Ay • (ROi • LFPR'1) [15] where EROj = employment redistributed to zone j from residential overflow of zone i (ROi) These employees (EROj) are then added to the initial service employment vector (SEMPj) in that zone to form the new employment vector for the second iteration.

[16] where the superscript k refers to the iteration number and BEMPj and SEMPj are as defined previously. 67

Thus, for the second and succeeding iterations this recalculated employment vector (BEMpk4"1) replaces the initial basic employment vector in equation [2], For the new vector of available land in each zone (SRAS^4"*) the land used for residential and service activities in the previous iteration are added and then subtracted from the previous vector, i.e. , [17]

where k again refers to the iteration number. k+1 This new vector of available land (SRASj ) is then introduced in equations [3], [5], [10] and [11] replacing the previous vector. The Distribution Functions Used in the Model All of the concepts introduced in the previous section deal with aggregates of activities and the multipliers 2 employed are analogous to those used in macroeconomics. The distinguishing characteristic of the Lowry-Echenique formulation is its concern for spatial distributions and the specific methodology for allocating the derived quantity of any activity to a particular zone. As noted previously, the formal structure of the model is completed by specifying a function of distance Ay or By for residential and service allocation respectively. The allocation functions take the general form: [18]

This is interpreted as follows: the probability of trips ending in zone i (PRO is directly proportional to the number of opportunities in the destination zone (Oj) and the distance between the origin and destination zone (Dy), and secondly, inversely proportional to intervening travel costs expressed as an exponential function of distance. With this formulation, the peak distribution of trip ends does not automatically occur at the origin (as with the gravity formulation used by Lowry), but is usually found at some distance from the origin. Explicitly: the labour force participation rate, LFPR (persons per all employees); the service employment to population ration, SEPR (service employees per persons); the basic space standard, BSS (basic acres per basic employee); the service space standard, SSS (service acres per service employee) and the residential space standard, RSS (residential acres per person).

68

The operator for the residential allocation (Ay in equation [3]), based on the journey to work distribution, distributes population from the employment zone (j) to the residential zone (i) in direct proportion to the available space in the destination zone (SRASi), to the distance from the origin zone and inversely proportional to transportation costs. Explicitly,

[19]

where the parameters a and b are determined from the journey to work data and are equal to 1. 0 and 0.505 respectively (r2 = 0.578). The operator By (equation [8]) allocates service employees to employment zones (i) in direct proportion to the opportunities* at the zone of destination (Ei) and their distance from zones of residence (j), Dy, and inversely proportional to distance costs. The terminal opportunities are a measure of attraction and are equal to the size of previous employment and the available space at the zone of destination. Explicitly, [20]

where EI = BEMPi + (SRASi • SSS l) and the parameters a* and b* are determined from the journey to shop data and are equal to 2. 0 and 0.425 respectively (r2 = 0. 874). BEMP, SRAS and SSS are as defined previously. The distribution of residential and service activities is thus dependent upon the number of terminal opportunities in each zone (either available space or number of potential employees) and varies with each iteration. The Data for the Model In this study, Metropolitan Toronto was divided into 16 zones representing aggregates of census tracts. This zonal system corresponds to the planning districts used by the Metropolitan 69

Toronto Planning Board (MTPB). The between zone work and shopping trip matrices (by number of persons) were prepared from the records of the Home Interview Survey completed by the MTPB in 1964. Auto-peak travel times between census tracts were used as an index of inter zone transportation costs. Census tracts as planning district centroids were used to specify the aggregated matrix. In addition, the population, land use and employment data necessary to the model were also available from MTPB publications. The land use and employment data were based on detailed surveys undertaken in 1963 and 1964 respectively and were aggregated from the census tract to the planning district level. The critical division of land use and employment into basic and service categories was reworked several times in order to overcome several definitional problems. For example, one of the nine categories used by the planning board is composed of all government and community service employees including all persons employed in educational and institutional facilities in addition to all civil servants. Obvious problems arise in attempting to allocate all employment in this category into either basic or service groups. For this reason, it is considered sufficient for this study to include all government and community service employees in those census tracts making up the CBD as basic, while those employees in this category in all other tracts were allocated to the service group. Essentially the same problem arises when the land use categories are subdivided into basic and service. The results reported here are based on the assumption that institutional land in the CBD is basic, while in all other zones it is service. 3 The employment and land use categories can then be summarized as follows: BASIC: employment land use SERVICE: employment land use primary industry industrial manufacturing construction transportation transportation and utilities wholesale business service government institutional (zone 1 only) (zone 1 only)

retail recreation personal service government (zones 2-16)

3 The spatial separation of this group, although introducing errors, is logical since the civil service in Toronto can be considered 'site oriented' and in fact major metropolitan

70

commercial institutional (zones 2-16)

These data are then used to calculate the multipliers used in the model. Initially it was felt that the average of the individual zone figures would be appropriate, but the standard deviations are exceptionally high, and more representative area-wide multipliers derived from the zone totals were subsequently used. Calibration of the Model The problem of model calibration is essentially one of assigning the appropriate values to the distribution function parameters (a, b, a*, and b* in equations [19] and [20]) which result in a calculated distribution of trips giving the best fit to the initial data. The procedure followed was to successively evaluate the functions with different values of the parameters and then to choose appropriate values such that: 1) the average generalized cost of travel (mean trip length) as predicted by the model, should be equal to the survey average 2) the correspondence between the predicted proportion of trips in each time interval and the observed proportion of trips should be as high as possible. A Tgolden section! search procedure^ is used to determine the successive values of the submodel parameters to be substituted (either separately or in combination) in the specific distribution functions. With each value (or combination of values) of the parameters the mean trip length statistic5 is calculated and compared with the mean trip length in the survey. When the actual and calculated mean trip lengths are equal, the search procedure is terminated and the goodness-of-fit statistics calculated. Thus, the MTL statistic is essentially the only measure used to determine the appropriate values of a and b in the distribution funcand provincial facilities are concentrated in that one planning district that comprises the CBD. The 'golden section' search procedure is described in detail in Meyer (1970) and Wilde and Beightler (1967). The mean trip length statistics for both the residential and service employment distribution functions are calculated as:

where

Ty = the number of trips (either actual or calculated between zones i and j , and DJJ = the generalized cost of travel (travel time) between zones i and j.

71

tions independently of the rest of the model. However, the use of the r 2 and the RMSE statistics confirm the fit of the population and employment distributions in assessing the overall performance of these submodels. In addition, after the appropriate parameter values were selected, a simple runs test performed on the residuals was successful at the 5 per cent significance level, thus ensuring that the residuals suggested a random distribution. The reason for selecting the mean trip length statistic as the best estimator is based on Hyman T s (1969) research which indicated that the MTL statistic was considerably more sensitive to parameter variation than other statistics. Furthermore, as Batty (1970) notes the use of the r 2 statistic and the V2 criteria do not yield as effective a representation of the data points as the MTL statistic with non-linear functions. Sensitivity of the Model to Parameter Change A comparison of the results of the various simulation runs permits an examination of the sensitivity of the model. Such tests are an important component of the design process since the overall performance of the model is related to the effect of each parameter on the allocation of activities. The parameters can be divided into two basic groups; those which result from initial definitional decisions and assumptions made by the planner; and secondly, those parameters which are determined from demographic and socio-economic conditions in the region. The first group includes the space standards and activity constraints, while the second group includes the employment to population ratios and the allocation function parameters. In order to determine the model sensitivity, several runs were made with different values of selected parameters, at the same time holding the other parameters constant. The derived distributions of population and employment are then compared. In most cases the total population or employment predicted by the model was about ten per cent below the actual levels but the percentage of the total allocated to each zone showed a high degree of correspondence with the actual data. For this reason, the following tables illustrating the results of the various runs give the percentage of the calculated total allocated to each zone rather than the calculated values themselves. Presenting the results in this manner permits a partial separation of the influence of the multipliers and the influence of the allocation function para72

meters on the resulting spatial distribution of activities. The results illustrate the efficacy of the operation of a simple allocation function at this high level of spatial aggregation to achieve a reasonable distribution of residential and service employment activities (Figure 5.2)

Figure 5. 2 Zonal system and actual distribution of population, basic employment, and service employment However, the use of a single area-wide multiplier does tend to make all zones more homogeneous; the only variation in their attractiveness being essentially due to the relative accessibility levels. There is a wide range in the values of the space standards and employment multipliers over the Metropolitan Toronto region and when an average multiplier is applied to those central zones for example where the actual space standards are low (indicating a high employment or residential density) the activities which should be allocated to these zones are displaced outwards. This displacement operates through a feedback process and partially distorts an effective separation of the results of the operation of the various parameters. These comments apply to all of the results and should be kept in mind when the particular runs are discussed. Tables 5.1 and 5.2 give the percentage of total population and service employment allocated to each zone as the space standards are changed for each run. Where the space standards used in the simulation are higher than the actual space standards 73

TABLE 5.1 ALTERNATIVE POPULATION DISTRIBUTIONS RESULTING FROM CHANGES IN THE SPACE STANDARDS (per cent of total in each zone) Calculated distributions Run 1 Run 2 Run 3

Zone no.

Actual distribution

1

7.15 14.04 13.22 11.99 3.49 13.42 3.60 7.41 2.05 3.73 5.91 0.43 9.03 2.88 1.03 1.07

0.36 13.47 12.59 11.63 4.13 13.57 3.45 8.96 2.62 4.38 7.17

4.732 1723078

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Standard deviation Total allocated

0.59 9.94 3.42 2.05 2.05

0.00 13.64 12.65 11.73 4.16 13.80 3.44 9.12 2.62 4.42 7.23 0.60 9.96 3.48 1.99 1.99

0.00 14.29 12.66 12.15 4.20 14.74 3.33 9.73 2.62 4.58 7.97 0.65 10.62 3.79 2.29 2.29

4.755 1603828

4.89 1583582

5.69 1506354

Space standards used in the simulation Run 1 Run 2 Run 3 BSS 0.02 0.03 0.04 SSS 0.01 0.05 0.10 RSS 0.03 0.05 0.07

for that zone, the constraint procedure precludes the "proper" allocation of activities. The displaced activites are allocated to zones less accessible to the location of employment, but the allocation is selective. Accessibility to employment or to the consumer market would appear to continue to dominate the allocation procedure since there is a wide variation in the amounts allocated among zones with large amounts of vacant land. The interaction between the two allocation processes is also evident. Although the total amount of service employment derived by the model is increasing as the space standards increase, the population totals derived in the model are decreasing. At the same time the zonal allocation of service employment is becoming more homogeneous while the "lumpy" nature of the residential allocation increases. Both of these results indicate the feedback process in the land allocation procedure and the outward dis74

TABLE 5.2 ALTERNATIVE SERVICE EMPLOYMENT DISTRIBUTIONS RESULTING FROM CHANGES IN THE SPACE STANDARDS (per cent of total in each zone) Zone no. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Standard deviation Total allocated

Calculated Run 1 28.36 11.32 11.22 11.79 2.37 9.01 2.56 4.76 1.92 4.02 3.55 0.54 6.17 1.15 0.60 0.65

Actual distribution 28.60 11.41 11.31 11.88 2.39 9.08 2.58 4.63 1.79 3.87 2.58 0.54 6.23 1.11 0.47 0.51

7.213 210579

7.124 191110

distributions Run 2 28.29 11.29 11.19 11.91 2.37 9.00 2.55 4.59 1.85 3.89 3.66 0.54 6.16 1.16 0.65 0.90

7.103 191581

Run 3 28.24 11.28 11.17 12.02 2.36 9.01 2.54 4.48 1.78 3.80 3.71 0.55 6.17 1.14 0.71 1.05 7.099 191805

placement of residential activities by the service activity allocation. 6 By holding the space standard parameters constant and by varying the values of the parameters in the distribution functions (b and b*)? the influence of the distribution functions on the allocation of activities may be determined. The results of changing the value of b in the residential allocation function on population and service employment distribution are shown in Tables 5. 3 and 5.4 respectively. The most notable feature about these results is that once the initial distribution of activities has been determined there is little change in the allocation of either population or employment as the exponent Similar comments apply to the results obtained when the labour force participation rate and the service employment to population ratio parameters take on different values. As the overall effect of changes in these parameter values is minor the results are not reported here. Since it is difficult at this stage to separate the effects of a and b on the distributions (a operates simply as a weight on the cost of travel) it was decided for the simulation to first change only the value of b in the residential allocation function, holding all other parameters constant, and second to change only the value of b* in the service employment allocation function. Changes in these parameters would result from changes in the perceived distance to work or to shop respectively and are in fact a prelude to disaggregating employment and income groups and the costs of travel. Tables showing the results of changes in the values of the parameter b* and the labour force participation rate are given in Harper (1972).

75

increases. This is somewhat disturbing since changes in the exponent obviously effect the frequency of trips. The reason for this discrepancy apparently lies in the choice of the zonal system for this study and the resulting small number of trips with time intervals of less than twenty minutes. Major changes in the frequency of trips as b varies occur in these short time intervals and as a result would not appear in the actual simulation runs. TABLE 5.3 ALTERNATIVE POPULATION DISTRIBUTIONS RESULTING FROM CHANGES IN THE VALUE OF b IN THE RESIDENTIAL ALLOCATION FUNCTION (per cent of total in each zone) Calculated distributions 0.5 b-0.001 1.0

no.

Actual distribution

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

7.15 14.04 13.22 11.99 3.49 13.42 3.60 7.41 2.05 3.73 5.91 0.43 9.03 2.88 1.03 1.07

0.36 13.47 12.59 11.63 4.13 13.57 3.45 3.95 2.63 4.39 7.17 0.59 9.94 3.42 1.66 2.03

-

13.46 12.58

8.96 2.62

1.67 2.05

-

2.06

4.732 1723078

4.756 1612370

4.755 1612340

4.757 1613130

Zone

Standard deviation Total allocated

_

-

-

-

13.56 3.44

NOTE:-indicates no change in value from previous run. No change in distribution as b exceeds 1.0.

Summary and Conclusions Testing the model with several values of each parameter demonstrates a number of points about the Lowry model. In evaluating alternative urban plans, one should be able to estimate the effect of each parameter on the activity allocation. However, alternative plans for urban development in a particular metropolitan area will involve changes only in selected parameters. Once the planner has determined the appropriate employment and land use 76

TABLE 5.4 ALTERNATIVE SERVICE EMPLOYMENT DISTRIBUTIONS RESULTING FROM CHANGES IN THE VALUE OF b IN THE RESIDENTIAL ALLOCATION FUNCTION (per cent of total in each zone) no.

Actual distribution

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

28.60 11.41 11.31 11.88 2.39 9.09 2.58 4.63 1.79 3.87 2.58 0.54 6.23 1.11 0.47 0.51

Standard deviation Total allocated

7.213 210579

Zone

Calculated b=0.01 28.56 11.41 11.30 11.89 2.39 9.08 2.58 4.63 1.83 3.87 3.58 0.54 6.22 1.11 0.47 0.51 7.208 189616

distributions3 0.5

1.0

28.36 11.32 11.22 11.79 2.38 9.01 2.56 4.75 1.92 4.03 3.55

28.35 11.30 11.21 11.52

-

-

8.96 -

2.01 -

3.50 -

6.17 1.15 0.60 0.64

1.17 0.62 0.69

7.125 191098

7.096 192300

NOTE:-indicates no change in value from previous run. No change in distribution as b exceeds 1.0.

groups parameter variation is essentially limited to the space standards for each activity. The various runs indicated that variations in the labour force participation rate, or in the parameters of the activity allocation functions influence the level and spatial distribution of activities only to a limited extent. The formulation of alternative urban development proposals will, however, involve specified changes in the exogeneously determined location of basic employment, in the activity space standards as land constraints and zoning conditions are applied, and in the travel times between zones as new transportation modes or routes are planned. At the level of spatial aggregation used in this study, it is difficult to evaluate the effects of small changes in the between zone travel times. However, allocation of residential and service activities conformed to the nature of the trip frequency curves as the parameter values changed. This indicates that with a high level of the travel time exponents, the respective activities are allocated as closely as possible to the employment and residence opportunities. These results reinforce the concept that the influence of transportation improvements on activity allocation depends to a great extent upon the value placed on time by the 77

trip maker, expressed in the model as the time-distance exponents b and b*. Often, the costs of municipal services are cited as one criterion for evaluating alternative urban land use plans. Although detailed budgetary considerations are difficult to formulate without fine-grained locational data, aggregate urban or semi-regional trend patterns are often determined through forecasts of population densities which are easily calculated from the model output. In Figure 5.3 population densities as predicted by the model are compared with the observed population densities.

Figure 5.3

Gross population densities

In this diagram the zones are ranked by time-distance from the CBD and the general decline in density as distance increases is evident both in the actual and calculated densities. Densities derived from the model are lower than those observed for the first six zones and greater than those observed for the outer ten zones. Only in the CBD zone is there a large discrepancy between 78

the calculated and observed figures. Use of space by basic and non-basic activities precludes the "proper" allocation of population in the central zones, but population densities in all zones conforms well with the observed data. Despite some minor modifications which may be made to the model formulation, in particular the spatial disaggregation of the multipliers (Harper 1972) the overall operation of the model as it stands is satisfactory. The model is able to replicate those general patterns and relationships in urban land use which contribute to an understanding of urban structure. Population densities and employment concentrations are adequately described by the model and the model structure is simple enough to permit parameter variation in order to determine alternative distributions. It is also significant that the model is most sensitive to those parameters to which planners attach the greatest importance.

References

BATTY, M. 1970. "Some problems of calibrating the Lowry model." Environment and Planning 2; 95-114. . 1971. "Design and construction of a subregional land use model." SocioEconomic Planning Science 5: 97-124. ECHENIQUE, M.; CROWTHER, D.; LINDSAY, W. and STIBBS, R. 1969. "A model of a town: Reading, " LUBFS-WP12. Cambridge: Centre for Land Use and Built Form Studies. HARPER, P. D. 1972. "The Lowry model of urban structure: a review and Toronto example." Unpublished M. A. Research Paper. Toronto: Department of Geography. University of Toronto. HYMAN, G. M. 1969. "The calibration of trip distribution models. " Environment and Planning 1: 105-12. LOWRY, I.A. 1964. "A model of metropolis." RM4035RC. Santa Monica, Calif.: Rand Corp. MEYER, R. 1970. "Theorietical and computational aspects of non-linear regression," in B. Rosen, O c L. Mangasarian and K. Ritter, eds. , Non-Linear Programming. New York: Academic Press pp. 465-86. WILDE, D. J. and BEIGHTLER, C. S. 1967. Foundations of optimization. Englewood Cliffs, N. J.: Prentice-Hall, Inc. pp. 242-45.

79

Ill Growth Characteristics

Editors' comments

Perhaps the most interesting topics in present-day urban research are associated with changes in the urban environment. Not only are past trends of interest, but the possibility exists of using these trends to describe the future shape of our urban areas. To date, relatively little research has been carried out on time series data, particularly relating to Canadian cities. The papers in this section take selected examples of outcomes of physical and social change, in order to illustrate ongoing processes determining urban form. Obviously there are many expressions of change. People move, new buildings are built and old ones demolished, organizations are reorganized, traffic and interaction channels are modified and redirected. Each has a slightly unique spatial pattern; each is a response to different processes; each raises different implications in both the long and short runs for theory and policy; but all are in some way linked together in the "city as a system." The topics represented by the following papers are four of the most important and widely discussed: land use, population densities, accessibility and household migration. Land use is traditionally the common denominator of change in urban areas. It is of course essentially a measure of physical change, reflecting our historical emphases on physical planning and on physical guidelines for measuring social progress. Despite these emphases, little systematic research has been carried out on the ordering of land use change within the city. In previous chapters, Gad (Paper 2) and Maher (Paper 3) have outlined some of the aggregate properties of land use change as they relate to city size. In the first paper in this section, Bourne and Doucet summarize the complexity of changes in land use in Metropolitan Toronto. Comparing the results of two cross-sectional surveys, 1964 and 1968, the authors document differing rates of change among types of uses and between subareas within the city. Through regionalization techniques, a set of common components 81

of physical growth are identified, based on similarities in the patterns of change of differing uses. These spatial components are more complex expressions of change processes than the simple dichotomy proposed above; growth clearly takes several forms, operating at different levels, each with different spatial geometries, and each reflecting a unique set of decisions and constraints. Models of intraurban growth distribution must therefore be disaggregated and then reconstructed around these kinds of intraurban spatial processes. In the second paper, Hill describes some aspects of the changing spatial patterns of population densities within Toronto. Initially, Hill reviews and evaluates the shortcomings of previous approaches to the study of density patterns and changes, particularly the fact that these approaches ignore the twodimensional nature of population distribution in the city. In response to these criticisms, he employs the technique of "trendsurface" analysis to study changes in population densities in Toronto from 1941 to 1966. MacKinnon and Lau describe changes in one of the fundamental transformation of physical space: the travel time accessibility patterns. The paper is descriptive in nature, but it attempts to give some rationale for the observed changes in accessibility in Metropolitan Toronto from 1964 to 1969 and briefly discusses some of the potential implications for such changes. Within the context of land use, density, and accessibility change, individual households are continuously migrating in order to adjust their needs to changing environmental conditions. In the final paper, Simmons describes regularities in the spatial and temporal patterns of net migration in Metropolitan Toronto. Intraurban migration is considered to be the critical link between household relocation (discussed in Paper 13) and social change. The aggregate patterns are complex, but relatively stable over time. It is clear that established migration streams exist within the city, as people of similar backgrounds, life cycle status, and location of origin follow similar spatial migration paths. The paper concludes with a broad range of implications for social change in the city.

82

6

Components of urban land use change and physical growth* L.S.Bourne and M.J.Doucet

Most researchers have assumed that land use change, and more generally physical growth of the city, takes the form of two processes: expansion of the periphery and renewal of the core. The thesis of this paper is that these concepts mask an underlying complexity of complementary and additive components of growth. A single aspect of the complex mosaic of urban change is examined: namely, shifts in aggregate land use structure and the intraurban components of land use change. Specifically, the paper provides a descriptive summary of the scale and diversity of land use change in Metropolitan Toronto between 1963 and 1968. The analysis is divided into two sections. The first, building on the results of previous studies of land use structure in 1963 (see Paper 4), documents trends and spatial patterns of net aggregate land use change; while the second attempts to identify the basic components of land use change through factor analytic techniques. * The latter are then used as a means of inferring the dominant processes altering the physical structure of the city. There are few parallel examples in the literature. Studies of land use change in U.S. cities, such as those of Niedercorn and Hearle (1963), Hoyt (1968) and Manvel (1968) generally have not had comprehensive time series data available, at least not in a sufficiently detailed breakdown by use type and by location within the city. Data and Units of Measurement The study utilizes two standardized sets of land use data for *This paper represents a merging of two earlier papers. See Doucet (1970), and Bourne (1970). iExcellent reviews of factor analytic techniques are provided in Harmon (1960) and King (1969)

83

census tracts in the Metropolitan Toronto area compiled by the Metropolitan Toronto Planning Board in 1964 and 1968. Initially the two sets were recoded to facilitate comparisons and to permit the calculation of change measures in each category of use. * Although both raw and percentage change measures were derived and subjected to examination, in the bulk of the analyses the following index of relative per cent change was utilized: Relative Per cent Change Index = Raw Chan*e 1963"68 X 100; Total Area 1963 where Total Area 1963 is the area of each individual spatial unit (census tract or zone). There are problems in utilizing percentage change measures, particularly if zero values appear in the above ratio or if large negative changes occur, 3 but these problems were judged to be less severe than those involved in utilizing raw change figures. The analysis was also repeated for different spatial units.4 For Metro Toronto the land use data were aggregated into three different sets of units: 1) the initial set of 301 census tracts; 2) 62 zones of approximately equal population and 3) 59 zones of approximately equal area. Although the results of all three are too bulky to reproduce here (see Bourne [1970]), major discrepancies between them are noted. If certain processes are operative over different spatial units then analyses based on these units should produce different patterns, each acting as a filter. RATES AND PATTERNS OF NET AGGREGATE CHANGE

Land use change, and rates of land utilization, will obviously vary between use types and among areas with a metropolitan region. Table 6.1 summarizes changes in the land use structure of Metropolitan Toronto between 1964 and 1968 in each of 29 major use categories (detailed descriptions on the land use structure in 1963 and 1968 are given in Paper 4). Figure 6.1 then maps the pattern of (net) aggregate change for census tracts. 2 The 1964 data were initially aggregated for the 301 Metro Toronto 1969 census tracts while the 1968 data were aggregated into 326 tracts according to the 1966 census. In this study the latter set was reduced to the same 301 tracts used previously.

^Since the denominator of the above expression can never be zero or negative no constraints are required in the calculation. 4

Detailed descriptions of the problems and methods involved in determining an appropriate set of recording units is given in Paper 4 of this volume. A more general discussion of spatial measurement problems is found in Johnston (1970).

84

TABLE 6.1 AGGREGATE LAND USE CHANGE: METROPOLITAN TORONTO, 1963 and 1968

Land use category

1968

Per cent Per cent Total of total of dev. area acres area

Per cent Per cent of dev. Total of total acres area , area

0.,44 0.,26 2.,16 0.,13 0.,19

0.63 0.37 3.10 0.19 0.27

726.6 421.5 3890.8 236.2 335.4

4887.5

3.18

4.56

157.5 2566.0 214.5 215.8 724.4 526.6 131.3 1337.2

0.,10 1.,67 0.,14 0.,14 0.,47 0.,34 0.,09 0.,87

0.15 2.39 0.20 0.20 0.68 0.49 0.12 1.25

Total institutional

5873.3

3.,82

14 Parks 15 Golf courses 16 Cemeteries 17 Other private open space

10650.0 2522.6 1264.4

6.,93 1,,64 0.,82

6487.1

4.,22

6.05

4934. 1

Total open space

20924. 1 13..61

19.51

21367.0 10812.5 1703.7

1 Gas stations 2 Offices 3 Retail 4 Hotels 5 Parking

677.9 398.5 3321.0 201.7 288.4

Total commercial 6 7 8 9 10 11 12 13

Private schools Public schools Separate schools Universities Churches Hospitals Government Other institutional

18 Industrial 19 Industrial reserve 20 Warehousing Total industrial

Change 1963-1968

1963

Per cent Total Per cent change in o acres change dev. area^

0.47 0.27 2.53 0.15 0.22

0.61 0.35 3.25 0.20 0.28

48.7 23.0 569.8 34.5 47.0

5610.5

3.64

4.69

723.0

14.8

1. 27

206.5 3327.9 319.4 996.7 812.3 559.0 294.6 1278.6

0.13 2.16 0.21 0.65 0.53 0.36 0.19 0.83

0.17 2.78 0.27 0.83 0.68 0.47 0.25 1.07

49.0 761.9 104.9 780.9 87.9 32.4 163.3 -58.6

31.1 29.7 48.9 361.9 12.1 6.3 124.4 -4.4

2. 68 2. 56 4. 22 31. 20 1. 04 54 10. 72 -. 38

5.48

7795.0

5.06

6.52

1921.7

32.7

2. 82

9.93 2.35 1.18

12060.6 3001.6 1370.7

7.84 1.95 0.89

10.07 2.51 1.14

1410.6 479.0 -106.3

13.2 19.0 8.4

1. 14 1. 64 72

3.21

4.12

-1553.0

-23.9

-2. 06

13.89

17.84

442.9

2.1

0. 18

7.03 1.11

9.03 1.42

2109.5 64.2

24.2 3.9

2. 09 34

2173.7

21.0

1. 81

8346.5 356.5 1639.5

5.,43 0,,23 1,,07

7.78 0.33 1.53

10342.6

6,,73

9.64

12516.2

8.14

10.45

2268.8

,

7.2 5.8 17.2 17.1 16.3

62 50 1. 48 1. 47 1. 41

21 Apartments 22 Row multiple housing 23 Single-family housing

1,,48

2.12

3211.2

2.09

2.68

942.4

41.5

3. 58

1007.5

0.,66

0.94

1526.1

0.99

1.27

518.6

51.5

4. 44

50659.8

32,,97

47.25

55486.1 36.08

46.32

4826.3

9.5

81

Total residential

53936.1 35,,11

50.30

60223.4

39.16

50.27

6287.3

11.6

1. 00

133.7 459.4 49.1 378.0

5.3 14.9 1.6 15.3

46 1. 28 14 1. 32

24

Railroads and yards 25 Expressways 26 Hydro 27 Other utilities

2539.6 3086.3 3158.2 2472.9

1,,65 2.,01 2..06 1.,61

2.37 2.88 2.95 2.31

2673.3 3545.7 3207.3 2850.9

1.74 2.31 2.09 1.85

2.23 2.96 2.68 2.38

Total transportation and utilities

11257.0

7.,33

10.51

12277.2

7.99

10.25

28 Agricultural 29 Vacant

3398.1 2.,21 43022. 1 28.,00

_ -

3070.5 30941. 8

2.00 20.12

Total undeveloped

46420.2

-

34012.3

22.12

TOTALS

1

30.,21

153640.8 100,,00

100.00

153801.6 100.00

1020.2

9.1

0. 78

-

-327.6 -12080.3

-9.6 -28.1

_ t 83 -2. 42

-

-12407.9

-26.7

-.2. 30

160.9

0.1

0. 01

_

100.00

* Metro area change is due to land reclaimed from Lake Ontario in Census Tracts 119, 134, 147, 148, and 171. For the period 1963-68 this figure was 11.6

2

The initial impression is one of rapid and complex change. In the short span of five years all but four types of uses and all but a few selected areas of the city underwent significant changes in land use. As expected, rates of raw change in Table 6.1 vary 85

widely between categories; ranging from a low of 23.0 acres (offices) to a high of 4, 826. 3 acres (single-family housing). Expressed in percentage terms, the increases ranged from 3.9 per cent (warehousing) to 361.9 per cent (universities). ^ The four declining uses were all extensive and largely non-urban activities. These declines ranged from 58.6 acres (other institutional) to 12,080.3 acres (vacant), or from -4.4 per cent to -28.1 per cent. It should however be noted that a number of problems are inherent in such data. In particular, small shifts in classification procedures between two periods can produce inflated figures on change. Urban land is frequently classified by ownership instead of by its present function or use. This problem is most pronounced when extensive land users, such as universities, governments and industries, are involved, particularly in holding large tracts of undeveloped land. This no doubt accounts for part of the large increases noted in Table 6.1. A preferable measure of change would be total floor area in a given use on a given parcel, combined with raw land acreage; but such data are not as yet available for Toronto. Despite these distortions the percentage increases in land use between 1963 and 1968 are considerable. They mirror some of the major structural trends in Canadian society: the shift in the urban economy toward service industries and employment, heavy immigration, the boom during the 1960Ts in governmental and educational expenditures, and shifts in the residential construction mix (Economic Council of Canada 1970). For example, large increases in apartments and other forms of multi-family housing reflect the trend toward higher density housing construction. Soaring land costs and a shortage of available serviced land have been instrumental in this shift, but these factors have had a differential effect on different uses. The rapid increase in government-held land in Toronto, paralleling trends in most other Canadian cities (Simmons and Huebert 1970), results primarily from the intensive institutional and office building program of the Ontario Government and the Metro School Boards. As governments at all levels increase their direct involvement in urban land, the physical imprint of the public sector on the 5 This excessive figure is due in large part to the fact that between 1964 and 1968 Toronto's second university (York) commenced operations on its new campus in northwest Metro All of the site was recorded as in university use although only a small portion was actually developed by 1968.

86

location and growth of other uses will certainly increase. Intra-urban Land Consumption Rates Of course, population growth is the principal determinant of land use change. The above figures can be converted to measure the amount of land utilized on a per capita basis. ^ Four such indices, described as land absorption or consumption coefficients, were calculated (Table 6.2). Indices 3 and 4 are most revealing. They are, for raw and percentage data, respectively, the marginal rates at which land is absorbed into each use category for each additional thousand of population growth. ^ Those categories with index values over ±1. 00 are increasing (or decreasing as the case may be) at a higher rate than the population as a whole. Those categories with indices between 0 and 1*1.00 are increasing (or decreasing) but at a slower rate than the population. The land uses with the highest values are normally associated with suburban growth. Single-family housing, for example, while declining in rate of growth, still represents the largest increment to Metro's land use inventory (18.4 acres/1000 population; Index 3). Yet as evidenced by the value of Index 4, this growth is not keeping pace with population increase. Land in high density housing, however, is increasing at 2 to 3 times that of population growth. The overall rate of land consumed for residential purposes has however remained relatively constant. Increases in land consumption are also noted in the institutional and retail categories. The growth of large suburban shopping plazas and industrial parks for instance, has off-set the increasing densities of activities in the core. Equally important, the total amount of land consumed per capita appears to be decreasing. In 1964 there were 66. 2 acres of developed land per capita compared to 63.7 in 1968. Between 1964 and 1968, about 12,408 acres were brought into urban use while Metro T s population grew by 263,000. 8 Combined these two Measurement problems and comparative statistics on land consumption are discussed by Gad in Paper 2. 7 Note that since the figures for raw land use change used in this index represent net changes in each category the full impact of land use conversion through redevelopment is not considered. 8 These trends are admittedly exaggerated by using the fixed municipal boundaries of Metro Toronto. However, incorporation of estimated change figures for fringe developments outside of Metro during this period do not drastically alter the conclusion (see Doucet 1970).

87

TABLE 6.2 INDICES OF LAND CONSUMPTION: METROPOLITAN TORONTO, 1964-1968

Land use category 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Change (Index 2 Index 1)

Index 3 Rates of change raw change/ population change

Index 4 1964-68 Per cent area change/per cent population change

0.3861 0.2240 2.0674 0. 1255 0.1782 0.1097 1.7683 0.1697 0.5296 4.4316 0.2970 0.1565 0.6794 6.4084 1.5949 0.7283

-0.0326 -0.0222 0.0161 0.0009 0.0001 0.0124 0.1833 0.0372 0.3963 -0.0158 -0.0282 0.0754 -0.1466 -0.1697 0.0368 -0.0527

0.1852 0.0875 2.1665 0.1312 0.1787 0.1863 2.8970 0.3989 2.9692 0.3342 0.1232 0.6209 -0.2228 5.3635 1.8213 0.4042

0.4444 0.3580 1.0617 1.0556 1.0062 1.9198 1.8333 3.0185 22.3395 0.7469 0.3889 7.6790 -0.2716 0.8148 1.1728 0.5185

2.6217

-1.3851

-5.9049

-1.4753

Index 1 Index 2 Land utilization (Acres/1000 population) 1964 1968

0.4187 Gas stations Offices 0.2461 Retail 2.0513 0.1246 Hotels 0.1781 Parking Private schools 0.0973 Public schools 1.5849 Separate schools 0.1325 Universities 0.1333 0.4474 Churches Hospitals 0.3253 Government 0.0811 0.8259 Other institutional Parks 6.5781 1.5581 Golf courses Cemeteries 0.7810 Other private open 4.0069 space Industrial and industrial reserve 5.3755 Warehousing 1.0127 Apartments 1.4014 Row multiple housing 0.6223 Single-family 31.2908 housing Railroads and yards 1.5686 1.9063 Expressways 1.9507 Hydro 1.5274 Other utilities Agricultural 2.0989 26.5732 Vacant

5.7452 0.9053 1.7063 0.8109

0.3696 -0.1074 0.3049 0.1886

8.0209 0. 2441 3.5833 1.9719

1.4938 0.2407 2.5617 3.1790

29.4825 1.4205 1.8840 1.7042 1.5148 1.6315 16.4409

-1.8083 -0.1482 -0.0223 -0.2465 -0.0126 -0.4674 -10.1323

18.3510 0.5084 1.7468 0.1867 1.4373 -1.2456 -45.9327

0.5864 0.3272 0.9198 0.0988 0.9444 -0.5926 -1.7346

94.8984

81.7224

TOTALS NOTE:

1) 2) 3) 4)

1961 population = 1,619 ,000 1966 population = 1,882,000 population change 1961-1966 = 263,000 per cent population change 1961-1966 = 16.2 per cent

figures yield an average (raw) land absorption coefficient of 47. 2 acres per 1000 population increase. Whether this trend will continue is an open but important issue. Spatial Variations by Census Tract Mapping of net aggregate change in land use reveals an extremely complex pattern. This complexity to some extent is attributable to the number, size and shape of the spatial units employed. Nevertheless, a definite order based on census tract data does emerge. In Figure 6.1 the tracts which show the highest change values 88

Figure 6.1 Index of net aggregate land use change : 1963 - 1968 Metropolitan Toronto by census tracts

(greater than 30 relative per cent change in the five-year period), are all located on the outer suburban margin. Clearly from this measure the suburbanization process is the dominant growth process affecting the urban landscape. In total, nearly 26 per cent of all tracts outside of the central city have net aggregate land use change index values greater than ten. These tracts tend to form clusters within the urban periphery, identifying nodes of major residential, commercial, institutional and industrial construction. Data for any other period would of course have identified different clusters of tracts with the highest rates of change. Nevertheless they would occupy the same peripheral position. In contrast, the suburban tracts which remained relatively stable during the period were predominantly the mature suburbs. Although many are of sufficient age, they have not for various reasons entered into the extensive renewal or redevelopment phase typical of the central city (Fisher 1967). The second major area of growth identified in Figure 6.1 reflects this redevelopment process. Note that several census tracts, particularly in the downtown core, recorded a rate of change equivalent to from 10 to 30 per cent of their total area in only five years. This rate equals that of most of the suburban fringe and it surpasses that common in most of the inner suburbs. Clearly there is, as some authors have suggested, a second ring of growth about the urban core, in addition to the suburban fringe, in which is expressed the renewal process (Blumenfeld 1949). But the spatial expression of this process is not adequately described as a ring. Rather it reflects the concentration of growth, or regrowth, at specific nodes in the built environment of the city, only one of which, but usually the largest, is the downtown core (Bourne 1967). Outside of the core area, patterns of change in land use are closely associated with the spatial structure of the transportation system. High indexes of change are in evidence at nodes along the north-south (Yonge) subway line and at scattered locations within portions of existing railroad and expressway areas. In the latter case the effect of the reclassification of land used as rights-of-way most certainly has inflated the change indices. Equally interesting are those areas which have not changed. Within the city of Toronto proper three such areas in Figure 6.1 stand out: 1) some high income inner-city residential areas such as Rosedale (tracts 92, 93 and 94), Wychwood Park (tract 35), North Toronto (tracts 79 and 82), and Deer Park (tract 51); 2) 90

older industrial zones such as those straddling the railroad lines in the west end of the city (tracts 8 and 16 for example); and 3) older working-class residential neighbourhoods located at a considerable distance from the core in both east and west ends of the city (tracts 120, 121, 6 and 25). Each of these three types of stable areas reflects the existence of different constraints on the processes of change. Briefly, the last two types suggest the importance of environmental limitations on the attractiveness of certain areas in terms of renewal for new and different uses. The latter are now densely occupied by recent immigrant groups and thus tend to be unattractive to developers, for either office or apartment construction. The first type, on the other hand, of which Rosedale is the most obvious example, illustrates the effectiveness of individual and community power amongst the higher income neighbourhoods in preventing or redirecting change through zoning decisions; changes which, given the attractions of the area, would no doubt have occurred in the absence of such power. Zoning restrictions, particularly on apartment development, have been considerably more rigid and effective in this area than in other residential areas within the city. INTERNAL COMPONENTS OF GROWTH PROCESSES Are there other dimensions of the growth process? There is no way that the casual observer can comprehend the complexity of land use change evidenced in 28 different categories. At least 28 maps would be needed. Conversely, net aggregate change indices reveal little of the components of urban growth. The following section turns to a more specific analysis of the various expressions of urban physical growth as measured by land use change through the medium of factor analysis. Here, changes in twenty-eight categories of land use were reduced to a set of common dimensions by combining the land use categories which displayed similar patterns of change. In so doing, inferences may be drawn regarding the processes underlying recent changes in the physical structure of urban areas. Factor Structure for Census Tracts Tables 6.2 and 6. 3 summarize the internal structure of urban physical growth processes. In these tables only general labels and brief descriptive notes are provided rather than the detailed factor loadings. Analyses are presented only for relative per 91

TABLE 6.3 DIMENSIONS OF URBAN PHYSICAL GROWTH: SUMMARY OF FACTOR STRUCTURE, RELATIVE PER CENT LAND USE CHANGE 1963-68*, BY CENSUS TRACT (N=300), TOTAL URBAN AREA, TORONTO

Factor

Per cent variance explained each factor cumulative

Description

Major growth processes 1

Core area growth—the renewal of the CBD fringe to office uses and expansion of low-density uses such as parking.

8.3

8.3

2

Suburban growth—conversion of vacant land to lowdensity residential, retail and industrial uses (also includes the temporary expansion of vacant land in developed areas due to demolition).

8.1

16.4

3

Conversion (or reclassification) of private open space to institutional open space uses.

7.1

23.5

Localized growth processes 4

Institutional and utilities growth and decline— expansion of universities.

5.7

29. 2

5

Growth of the urban expressway system.

5.6

34.8

6

Government and institutional growth—offices and private schools.

5.6

40.4

7

Railroad relocation (on agricultural land) and right-of-way reclassifications, and growth of utilities systems.

5.2

45.6

8

Residential renewal process—conversion of singlefamily areas to high-density apartment use.

4.9

50.5

9

Suburban high-density hotel and institutional growth.

4.8

55.3

Expansion of commercial recreational facilities, private open space reclassification and decline.

4.7

60.0

10

* Measured as change in per cent of total area.

cent land use change and for two spatial grid systems: census tracts (n=300), and zones of approximately equal population (n=62). As the descriptions in each table are quite elaborate, only a brief resume is necessary here. In each table, the factors or dimensions suggested by each set of factor loadings have been divided into two groups on the basis of scale of observation. These two are 1) the generally 92

continuous processes of suburban expansion, renewal, and land use reconversion; and 2) the growth or rearrangement of small specialized nuclei of activities and utilities. The latter are frequently distorted by the simple re classification of undeveloped land within the city. Consider the generalized factor structure in Table 6.3. For per cent land use change by census tracts, the analysis produced 10 factors of change types, summarized into two groups. Within the first group three dominant expressions of growth are manifest. The first two, accounting for a similar proportion of the total variance are similar to those noted in terms of net aggregate change: the growth or renewal of the core and the suburban fringe. The contrasting patterns of these factors are displayed in Figures 6.2 and 6.3. The renewal process involves high density office expansion particularly around the periphery of the core; however parking and low density public office land use have in fact declined in the core. This of course is different from what might be expected were changes to be measured in floor area rather than land area. Moreover, much of the parking component in this factor is a temporary state between demolition and new construction which would be apparent at the time of any survey. The third factor involves the widespread conversion and re-use of open space—land in most instances classed initially as commercial or institutional open space. Clearly a major portion of such changes may simply represent re classifications of land without an effective change in its use. The complexity of land use change is obvious. These three dominant patterns combined account for only 24 per cent of the total change in land use. Ten factors in turn account for only 60 per cent of total changes. The low degree of "explanation" in part is due to the large number of observation units; it is also a measure of the independence of land use types (see Paper 4). Six additional factors are grouped under localized growth processes. They reflect the expansion of smaller specialized nuclei of activities in the urban fabric. The contents of each are largely self-explanatory from the tables; and their spatial patterns are highly concentrated. Two examples of the degree of concentration are provided in Figure 6.4 (factor 5—the growth of the expressway-utilities systems) and Figure 6.5 (factor 10~open space conversion). These nuclei should, however, be interpreted with caution. Many are quirks of the data, the classification scheme, and of 93

Figure 6.2

Core area renewal

Figure 6.3

Suburbanization

Figure 6.4

Expressway growth

the city on which the analysis is based. The re classification problem as mentioned is also considerable. Vast tracts of undeveloped land, for example, zoned in industrial use and attached to a particular industry may be recorded as industrial rather than vacant. Similar examples are common around suburban institutional uses. Portions of these areas are often subsequently sold or leased and then developed for uses quite different from that of the original land holder. This is subsequently recorded in most such records as a change in use. Nevertheless, certain basic patterns of localized growth processes are evident. In most instances two or three categories of land use change group to reflect major trends in construction activity and land use conversion. Among the latter are: institutional growth, largely due to the expansion of Toronto's universities and colleges; public offices and service facilities; and highdensity apartment redevelopment. Two other types of change with different spatial geometries are the expansion and restructuring of the network of expressways and utilities within the metropolitan area (Figure 6.4). Combined, the six localized factors add 36 per cent to the cumulative variance of land use change explained by the factor structure. Comparison of Structures for Different Spatial Units How stable are these patterns? Specific factor loadings, and the inferences concerning land use change processes based on these factors, may vary with the level of aggregation employed. To test these effects the factor analyses were repeated using the 62 and 59 zonal grids. The most revealing of these is for the 62 (equal population) zones. The results improve immediately (Table 6.4). Comparing this factor structure with that in Table 6. 3, we note an increase in the variance explained to 72 per cent in 9 factors, and a smoothing out of the composition of each factor. Four major growth processes rather than three are suggested: with the additional factor representing a merging of the expressway and utility systems into what might be termed network infrastructure growth. Combined, these four factors account for nearly 45 per cent of the total variance and over 60 per cent of the variance in nine factors. But the localized growth factors are less stable. Of the five localized factors three are institutional uses, one reflects large scale shifts in railroad properties, and the other identifies concentrations of high density construction particularly 97

Figure 6.5

Open space conversion

TABLE 6.4 DIMENSIONS OF URBAN PHYSICAL GROWTH: SUMMARY OF FACTOR STRUCTURE, RELATIVE PER CENT LAND USE CHANGE 1963-68*, BY EQUAL POPULATION ZONES (N-62) TOTAL URBAN AREA, TORONTO

Factor

Per cent variance explained each factor cumulative

Description

Major growth processes 1

Core area growth—the renewal of the CBD fringe to office use and expansion of low density uses such as parking in the core area.

16. 7

16. 7

2

Suburban growth—conversion of vacant land to lowdensity residential, institutional and retail uses.

12.0

28.7

3

Conversion (or reclassification) of private open space to public open space, and the growth of suburban institutional facilities.

9.2

37.9

4

Growth of urban expressway and utility systems.

6. 8

44. 7

Localized growth processes 5

Expansion of new institutional services—hospitals.

6.1

50.8

6

Growth of high-density residential and suburban hotel construction.

5.8

56.6

7

Institutional growth—expansion of primary and secondary schools.

5.6

62.2

8

Railroad relocation (on agricultural land) and right-of-way reclassification.

5.0

67.2

9

Institutional growth—expansion of private schools.

4.9

72.1

* Measured as change in per cent of total area.

in the inner city and near the airport. Not surprisingly, the location of an international airport has had a significant impact on changes in urban spatial structure. Comparing the two sets of results leads to the conclusion that the major expressions of urban physical growth described above are in fact valid generalizations. They vary in strength, but are unique to one level of aggregation. Instability in the patterns of change appears in the minor and more localized factors. A comparison of the two sets of maps (although not included here) reveals similar stability in the dominant processes. 99

Summary To provide one summary of the preceding factor analyses is to lend an assertion of concreteness which does not as yet exist. The analyses are for one metropolitan area, and for one short time period. Yet it is worth attempting some generalizations more in the form of a set of hypotheses. Table 6.5 is an example. Here the importance of each component is represented by the per cent variance explained drawn largely from the two analyses described above. Despite the differences in these two grids the variance explained figures are sufficiently similar to add substance to the preceding generalizations. The results lead us to propose in Table 6.5 the following expressions of urban physical growth in Toronto: 1) growth of the suburban fringe; 2) expansion of the central core; 3) growth of various infrastructure networks; and 4) a set of nucleations in which land use change is concentrated. Note that these are independent expressions of physical growth processes; processes which can be summed. Note also that each dimension is a combination of several uses, each with a distinct spatial pattern. To create each of these four broad headings several of the above growth factors were combined and renamed. For example, under infrastructure change two major subcomponents are denoted— changes in the expressway system and changes in the multiple systems of public utilities. In some analyses these emerged as one factor, or combined in concert with other land use types. Similarly, under the suburban growth factor, three subcomponents are suggested, one based on the growth of neigh-, bourhood units as clusters of homogeneous land use (low-density housing, schools, churches, local retail), a second on changes in open space designation, and a third on concentrations of largescale institutional and industrial developments. This particular separation of suburban growth types reflects the recent imprint of trends in the building and real estate industries and the effects of mutually-exclusive zoning in suburban areas. Clearly the dominant process in land use change is suburbanization. Second in importance is the nucleation category. The number and composition of nucleations represented by land use change varies from one level of measurement to another. This is of course to be expected as our grid systems will identify certain patterns while obscuring others, depending on their size and location. From one city to another the nucleations will vary in composition, location, and size depending on local economic, 100

TABLE 6.5 SUMMARY OF COMPONENTS OF URBAN PHYSICAL GROWTH PROCESSES AS EXPRESSED IN FACTOR STRUCTURES OF LAND USE CHANGE BY CENSUS TRACTS AND EQUAL POPULATION ZONES

No.

Per cent variance explained* Single component Totals

Title and description

1

Suburban and fringe growth: i) neighbourhood units ii) open space iii) institutional - industrial

2

Core area growth: i) expansion of CBD fringe ii) renewal of core

3

Infrastructure network growth: i) expressway system ii) public utilities

5.6 (6.8) 5. 2 (5. 0)

4

Nucleations—growth and decline (and land reclassifications) i) institutions ii) government iii) recreation iv) hotels v) apartments

(4.9) ' (5.0) 4.7 4. 8 / 5 g, 4.9

8.1 (12.0) 7.1 ( 9.2) 5.7 ( 6.1) 8.3 (16. 7)

Totals

20.9 (27. 3)

8.3 (16.7)

10. 8 (11. 8)

20.0 (16.3)

60.0(72.1)

*The proportion of variance explained is included for illustrative purposes. Both figures relate to relative per cent land use change the first for the census tract analysis, and the second in parentheses from the 62 equal population zone analysis.

social and historical conditions.

The other two categories of physical growth, the core area and infrastructure networks, are of lesser importance in terms of the space occupied. The two subdivisions of core area growth recognized (which did not emerge separately in either of the two examples included here) comprise less than ten per cent of the variability in land use change. This figure is similar to that for the composite infrastructure factor. Yet, neither represents the true impact of these changes on the urban spatial structure. How do these patterns compare with the classical models of urban growth: the concentric zone, sector, and the multiple nuclei hypotheses (Ullman 1962) ? Although comparison is difficult between these studies and the present study, based as they are on different data and techniques, there are similarities. The first and second components of growth in Table 6.4 are but two of the concentric zones recognized in studies of most cities, areas which he also identified as registering the highest rates of transi101

tion. The third component in fact depicts residential sectors varying in status and income. The relationship between lowincome housing and proximity to railway-industrial areas suggests that this factor is also pointing to low-status residential sectors. The fourth component, nucleations, fits neatly in both title and content into the conceptual schema outlined by Ullman. Thus the traditional models are in one sense confirmed and in another extended and elaborated, in their application to urban land use change.

References

BOURNE, L. S. 1967. "Private redevelopment of the central city." Research Paper No. 112. Chicago: Department of Geography. University of Chicago. . 1970. "Dimensions of metropolitan land use: cross-sectional structure and stability." Research Paper No. 31. Toronto: Centre for Urban and Community Studies. University of Toronto. BLUMENFELD, H. 1949. "On the concentric circle theory of urban growth." Land Economics 25: 209-12. CLAWSON.M. 1971. Suburban land conversion in the United States. Washington, D. C.: Resources for the Future, Inc. DOUCET, M. J. 1970. "Trends in metropolitan land use and land consumption: Metropolitan Toronto, 1963-68." Research Paper No. 35. Toronto: Centre for Urban and Community Studies. University of Toronto. ECONOMIC COUNCIL OF CANADA. 1970. "Patterns of growth." Seventh annual review. Ottawa: Queen's Printer. FISHER, B. J. 1967. The renewal of urban land: process decisions and simulation. Chapel Hill, N. C.: Center for Urban and Regional Studies. HALL, P. 1969. "Land use--the spread of towns into the country," in M. Young, ed. Forecasting and the social sciences. London: Heinemann. HARMON, H. H. 1960. Modern factor analysis. Chicago: The University of Chicago Press. HOYT, H. 1968. Urban land use requirements 1968-2000. The land area required for the future growth of the urban population of the United States. Washington, D. C.: Homer Hoyt Assoc. JANELLE, D c G. 1970. "The mass movement of land use surfaces in London, Ontario: a conceptual approach for isolating basic changes in urban land use." Paper prepared for a conference on The Geography of the Future. London: University of Western Ontario. JOHNSTON, R. J. 1970. "Grouping and regionalization: some methodological and technical observations." Economic Geography 46, No. 2 (supplement): 293-305.

102

KING, L. J. 1969. Statistical analysis in geography. Englewood Cliffs, N.J.: Prentice-Hall, Inc. pp. 165-93. MANVEL, A. D. 1968. "Land use in 116 large cities," in "Three land research studies." National Commission on Urban Problems, Research Report No. 12. Washington, D. C.: GPO. MURDIE, R. A. 1969. "Factorial ecology of metropolitan Toronto 1951-1961." Research Paper No. 116. Chicago: Department of Geography. University of Chicago pp. 49-53. NIEDERCORN, J. H. and HEARLE, E. F. R. 1963. "Recent land use trends in fortyeight large American cities." Memorandum RM-3664-1-F. Santa Monica, Calif.: Rand Corp. RAPKIN, C. 1970. "Economic patterns of land use." Appraisal Journal 38; 227-39. SIMMONS, J. W. and HUEBERT, V. H. 1970. "The location of land for public use in urban areas." Canadian Geographer 14, No. 1: 45-56. ULLMAN, E. L. 1962. "The nature of cities reconsidered. " Papers and Proceedings of the Regional Science Association 9: 7-23.

7

Spatio - temporal trends in urban population density:A trend surface analysis* F.I.Hill

The contemporary city poses a number of problems to which partial solutions may be found through measurement and analysis of urban population densities. One such problem, facing researchers and managers alike, is the forecasting of trends in *The present paper is a revised and shortened version of an earlier research paper, F.I. Hill, "Spatio-Temporal Trends in Population Density: Toronto 1932-1966" Research Paper No. 34. Centre for Urban and Community Studies, University of Toronto 1970. The assistance of Alan Baker and James Simmons is gratefully acknowledged.

103

the location and intensity of development within large cities. Population density serves as a workable and sensitive indicator of these trends. The first part of this paper surveys traditional analyses of urban population density patterns and changes within the city. The second part reports a series of trend surface analyses of density patterns observed within Toronto from 1932 through 1966. A Survey of Traditional Approaches Following significant earlier studies of the spatial variation of population density within cities (Jefferson 1909; Clark 1951), contemporary researchers have provided explanations for the negative exponential decline of densities outwards from the city centre (Alonso 1960; Muth 1961; Casetti 1967). The latter two, however, simply push the explanation one step away: Muth r s model assumes that the price of housing declines in a negative exponential manner with distance from the market, and Casetti f s assumes exponential preference functions for centrality and for non-congested sites (Brown 1968). Moreover, alternative historical explanations suggest that density decline may follow largely from changing technological and social conditions, and physical aging of the housing stock, rather than from present conditions only (Adams 1970). A further criticism is that the simple exponential form, D(x) = Ae~^ x , does not fit observed data as well as other functions, particularly in central and peripheral areas of the city (Sherratt 1960; Newling 1969). Finally, density decay functions of whatever form, being unidimensional, assume that the city r s shape is not only symmetrical but also circular. And while shape distortions seem not to influence the values of gradients (Berry, Simmons, and Tennant 1963), the assumption of circularity is obviously questionable for many cities. Despite the limitations of the approaches noted above, and sometimes using other approaches, researchers have reported useful findings for various groups of cities. Stone (1967) notes that ever since the 1911-21 decade, growth in Canadian cities over 30, 000 has been exceeded by growth in the surrounding areas. Growth in Toronto city proper has been exceeded by growth in the surrounding census subdivisions in all decades since and including 1911-21. A similar trend elsewhere has been interpreted as evidence of decentralization (Hauser 1957), though it can be labelled simply peripheral growth or suburbanization. 104

The concern with proving the existence of decentralization also pervades much of the literature on population density gradients. Clark noted that TTin most (but not all) cities, as time goes on, density tends to fall in the most populous inner suburbs, and to rise in the outer suburbs, and the whole city tends to 'spread itself out'"(Clark [1951], p. 490). In terms of the negative exponential equation for population densities, D(x) - Ae~" x , this verbal statement is translated as a reduction in the value of b, while A, the extrapolated central density, remains the same or decreases. That b has decreased through time, however, may be as much a manifestation of urban growth as evidence of decentralization. Berry et. al. (1963) noted a significant relationship between density gradient and population size. Since it has been normal for cities to increase in population through time, b may therefore have decreased through time, simply as an expression of population growth. Mills (1970) offers evidence that density functions have flattened as a result of population growth and growth of income, rather than as a result of the passage of time itself. In fact if population and income remained constant, the density gradient would show a tendency to increase rather than decrease. Population growth, moreover, may have differential effects on both central density and density gradient according to the initial values of these parameters, and according to changes in the proportion of multi-family dwellings in the additions to the housing stock (Mercer 1968). Furthermore, the conclusion of Berry, Simmons, and Tennant that small cities are more "compact" than larger ones, on the basis of the relationship between b and population, may mean little more than that small cities are smaller in area (or radius from the CBD) than larger ones. Compactness is a concept which refers not only to the rate of decline in density with distance, but also to the central density and the size of the city. Changes in the pattern of urban population distribution with time have also been analyzed in terms of their relationship to the initial population density pattern, on the grounds that areas of intense urban use have a more limited potential for growth than undeveloped land. Newling (1966) has derived from this relationship the rule of allometric growth, i.e., the rate of growth of On the other hand, if transport costs relative to income cannot be reduced b remains constant or increases, while A increases. In western cities this condition has generally not been applicable, and the values of A and b have both decreased.

105

density is a positive exponential function of distance from the centre of the city: (1+r^) = (H-r0)e£ , where rd is the rate of growth at distance d, ro is the rate of growth at the centre of the city, and g is the intraurban growth gradient, with distance from the centre of the city. ^ A growth rate of 0 in the case of Pittsburgh from 1950 to 1960 corresponded to a density of 32,000 per square mile, which Newling designates at the "critical density, Tt above which negative growth (or decline) occurs. Positive growth above such a critical density would involve social costs and would occur only under exceptional conditions, such as heavy immigration without a commensurate expansion of housing. 3 Another concept used in the analysis of population density changes through time is that of the "wave of metropolitan expansion" (Blumenfeld 1954; Boyce 1966; Montreal City Planning Department 1964). In the expansion process, the concentric zones, from the centre outwards, go through subsequent phases of slow growth, fast growth, levelling off, and decrease in population. Although Blumenfeld T s method may be less elegant mathematically than quadratic or linear negative exponential equations, he explicitly relates change in population density, as measured by the forward motion of the wave crest, to changes in the total population of the city. Changes in population density in Metropolitan Toronto from 1941 to 1961 have been studied by Njau (1967) who calculated simple and quadratic exponential equations on the basis of censustract data grouped into one mile concentric circular distance bands. The simple exponential equations showed declines in both the A and b parameters. Quadratic exponentials, of the form Dd = D0ebd-cd2 yielded unexpected positive values for c in 1941 and 1951, although by 1961, c had become negative. The value of b in this case was negative in all three years but moved towards positive values through time. Only in 1961 was there a density crater in the CBD, and even then this was only of one mile radius. 4 ^Since both density and rate of growth are functions of distance from the centre of the city, rate of growth can be expressed as a function of density: (1 + rj)) = AD~k, where rj) is the rate of growth during a given period where the density at the beginning of the period is D, A is a constant and k is the ratio of intraurban growth gradient to the population density gradient. 3

It may be noted that growth did occur in some ward subdivisions of Toronto during the exceptional 'thirties and 'forties at population densities higher than the critical density which Newling found for Pittsburgh in the 'fifties. 4

The actual crater extended out to the fourth band, although the quadratic exponential equation did not show this.

106

Njau T s data showing changes in density by distance band, however, revealed that the concept of a wave of metropolitan expansion is applicable to Toronto, in terms of both a zone of rapidly increasing densities in the outer suburbs and a zone of declining densities in the central city. Population density gradients in Toronto have also been studied by Bussiere (1970) and by Latham and Yeates (1970) who calculated quadratic exponential equations of the Newling variety for 1951, 1956, 1961, and 1963, after converting gross densities by census tracts into gross densities based on a hexagonal pattern of areas. Through this time period, central density out to a distance of three to four miles declined, the curves became increasingly concave with respect to the origin, the value of b increased from -0. 366 to -0. 002, and the value of c declined from 0. 009 to -0. 009. By expressing the logarithm of central density and the values of b and c as a linear function of time, Latham and Yeates extrapolated the curves to 1966 and 1971. This extrapolation, however, demands the assumption that their change is linear with respect to time, when in fact changes in these parameters may be largely a reflection of the rate of growth of the city, which cannot be assumed to be linear with respect to time. Perhaps it is this problem which Latham and Yeates are referring to in their concluding remark that "future models will have to incorporate differentials in the rate of radial growth in order to provide a more realistic appreciation of urban growth." On the other hand, this may have meant that the quadratic exponential, like all unidimensional formulations of density gradients, takes insufficient account of symmetry. Either criticism is justifiable. This discussion of traditional approaches to population density structure and change points to the following conclusions. The determination of density gradients has been a one-dimensional approach to a two-dimensional phenomenon, and the variety of formulations of density-distance functions has not been accompanied by an equal variety of theoretical foundations which can account for these functions. Various mathematical formulations and the theories and processes associated with them may in fact be applicable to different subareas of the metropolis. Changes in the parameters for these functions have been too zealously interpreted as evidence of decentralization, when in fact changes are inherent in the growth process itself. Decentralization and compactness are both poorly defined terms, and existing mathe107

matical expressions for these concepts are confusing and misleading. Too little effort has been devoted to the separation of the internal readjustment of structure unrelated to growth, from changes implicit in the growth process. A Trend Surface Approach to Population Densities in Toronto The following section aims to demonstrate the uses and limitations of trend surface analysis of population density patterns, and to present some results for Toronto. The pitfalls noted above have been avoided to some extent, but not entirely. Trend surface analysis is an adaptation of multiple regression analysis to spatial data whereby the independent variables are spatial co-ordinates on a grid system, and the dependent variable, in this case population density, is predicted by the values of these co-ordinates. The mathematics of the technique is adequately explained elsewhere and will not be repeated here.5 Some of the limitations and assumptions, however, are discussed as they become relevant to the paper. Two sources of population data are used. The first is the Canadian Census for 1951, 1961, and 1966. The census tract bulletins for 1951 and 1961 also contain data for 1941 and 1956 respectively on the basis of the 1951 and 1961 census tract boundaries. Data are available for all of Metropolitan Toronto for all five years, but there are some boundary changes from the 1941-51 data to the 1956-61 data, and again to the 1966 data. Most of the changes involve tract subdivision in the peripheral areas, although a few minor changes are made in the central city as well. The significance of these changes is discussed later. The second data source is the annual reports of the assessment commissioner for the City of Toronto, which contain maps showing the population, areas, and density of the 51 ward subdivisions of the City of Toronto (Figure 7.1). The ward subdivisions have no boundary changes at all from 1932 to 1948. Using a modified version of Good T s (1964) program, the first-, second-, third-, fourth-, and fifth-order trend surfaces are computed for each of eleven data sets: census tract data for Metropolitan Toronto in the years 1941, 1951, 1956, 1961, and 1966; census tract data for the City of Toronto in 1941 and 1951; and ward subdivision data for the city of Toronto in 1932, 1936, 1941 and 1948. The 55 models produced by these analyses make ^An extensive description is contained in Krumbein (1956).

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Figure 7.1 City of Toronto. Wards and ward subdivisions: 1941 possible an examination of changes in the configuration of population densities through time, as well as the effects of considering only the central city as opposed to the metropolitan area, and the effects of a change in spatial filter from ward subdivisions to census tracts. Table 7.1 shows the explanatory power of the five orders of surfaces computed for census tract data from Metropolitan Toronto from 1941 through 1966. For each of the five orders of analysis, the models for the later years, especially 1966, tend to be less powerful than the corresponding models for the earlier years. In general, it can be concluded that as the metropolitan area grew, the spatial variation of population densities within it became less regular. Unpredictably or randomly spaced pockets of high or low densities become more common. As a city grows larger and larger, spatial segregation of land uses increases, and in the pattern of residential land use both large gaps and large concentrations—large enough to be detected by census tracts as a 109

TABLE 7.1 COEFFICIENTS OF DETERMINATION OF FIRST TO FIFTH ORDER TREND SURFACES OF POPULATION DENSITIES IN METRO TORONTO 1941-1966 Order of surface Year

1966 1961 1956 1951 1941

1

.180 .211 .245 .229 .228

2

3

4

5

.298 .334 .379 .351 .355

.373 .433 .486 .466 .486

.403 .469 .522 .516 .540

.462 .517 .567 .561 .597

spatial filter--emerge more frequently. The level of explanation is of course greater for higher order surfaces using the same data set, since the higher order equations can describe more complex surfaces which conform more closely to the observations. If the goal is simply to produce the highest level of explanation possible, then higher order models are to be preferred. But, depending on the computer or the programmers ingenuity, limits are imposed on the order of roundoff error, which becomes very high in multiple regression programs with large amounts of data and many independent variables (Draper and Smith 1966). Another sort of limitation on the use of high order models is that the more complex surfaces cannot readily be interpreted in terms of existing theory about urban population densities. A second-order surface allows a single inflection point and a regular density decline, as predicted by theoretical statements discussed above. Accordingly, Table 7.2 presents the results of the second-order analyses, and the following discussion focusses on these models. Compare first the five second-order analyses for Metropolitan Toronto using census tract data for 1941, 1951, 1956, 1961, and 1966. Contour maps for 1941 and 1966 are shown in Figures 7.2 and 7.3. Each surface is statistically significant at the . 01 level. Through time, the centre about which the contours are spaced imed steadily northwestward from far out in Lake Ontario in 1941 to approximately Toronto Island in 1966. This movement is the manifestation of the rapid growth of a lakeside city whose centre of population must move further inland as peripheral growth proceeds. On the basis of negative exponential formulations, one would 110

TABLE 7.2 COMPARISON OF SECOND ORDER TREND SURFACES

Year

Areal units of analysis

1932 1936 1941 1941 1941 1948 1951 1951 1956 1961 1966

Ward subdividions: City Ward subdivisions: City Ward subdivision: City Census tracts: City Census tracts: Metro Ward subdivisions: City Census tracts: City Census tracts: Metro Census tracts: Metro Census tracts: Metro Census tracts: Metro

Number of Coefficient of observations determination 51 51 51 135 257 51 135 257 301 301 324

.256 .255 .253 .129 .355 .276 .125 .351 .379 .334 .298

F-ratio 2.58* 2.57* 2.54* 3.18** 23.07** 2.86* 3.07** 22.61** 29.97** 24.70** 22.46**

* Significant at the . 05 level. ** Significant at the . 01 level.

expect population densities to decline less and less rapidly as one proceeds outwards from the centre. These second-order surfaces suggest, however, that this is not the case with Toronto. In fact, the contours have a tendency to become more widely spaced towards the centre. This tendency, however, arises from a basic difference between the two curve-fitting approaches. Distance-decay functions permit a discontinuity at the centre of the city, while trend surface analysis treats the centre as an inflection point. The density surface therefore has to flatten towards the peak value because of the restrictions imposed by the trend-surface technique. These second-order surfaces for the metropolitan area also display a directional asymmetry, with a somewhat ellipsoidal form, the major axis being parallel to the lakeshore. This elongation of the metropolitan radius in an east-west (or slightly northeastsouthwest) direction, may result from the concentration of transportation routes along the lakeshore throughout most of the city's history. Alternatively, it may be attributable to the limits on northward growth of the city imposed by the difficulty of extending water and sewage systems far northwards from Lake Ontario, upon which both systems depend. (The higher order surfaces also show ellipsoidal contours, centred around a point just northwest of the CBD (Figure 7.4). A comparison of the second-order surfaces suggests that the contours are becoming more nearly circular about the CBD as the inflection point or "centre" moves 111

Figure 7 . 2

Metro Toronto. Population density: 1941

Figure 7. 3

Metro Toronto. Population density: 1966

northwestward. Consider now the analyses using census tracts for 1941 and 1951, but only for the city proper, as shown in Figure 7.5 for 1941. In 1941, the city of Toronto contained 73 per cent of the metropolitan area's population, compared with 61 per cent in 112

Figure 7.4

Metro Toronto. Population density: 1966

Figure 7. 5 City of Toronto. Population density: 1941 113

Figure 7.6

City of Toronto.

Population density: 1941

1951 and 42 per cent in 1961, although the absolute population of Toronto city varied less than 2 per cent itself. In 1941, then, and even in 1951, Toronto city proper could be expected to show the concentric pattern which the metropolitan area showed, since it accounted for well over half the population. The second-order surfaces, for the city proper, however, instead show a ridge of high densities running northeast-southwest, passing northwest of the CBD. This pattern is attributable to the lakefront location of Toronto, since the lakefront has low gross densities, being an area of mainly non-residential land use, especially within the central city. Within the context of the metropolitan region, however, the belt of low densities along the waterfront becomes of minor importance, and a more circular second-order surface appears. The vast differences between corresponding surfaces for the city and the metropolitan area reveal the importance of the 114

Figure 7.7

City of Toronto. Population density: 1932

delimitation of the study area when trend surface analysis is used. This finding is consistent with Casetti f s (1967) conclusion that different density-decline functions may apply to different parts of the metropolitan area. Doubt is also cast upon the applicability of Muth T s (1961) findings for central cities to metropolitan areas. Finally, the four analyses using ward subdivisions are considered (Figures 7. 6 and 7.7). These analyses are significant only at the . 05 level, even though their level of explanation is greater than that of the census-tract, central-city analyses. The difference in significance levels is attributable to the difference in the number of observational units (51 as opposed to 135). The proportion of variance explained by the second-order surface is higher than in the case where census tracts are used for only the central city. Local pockets of very high or low density are erased when a coarser spatial filter is applied. Even though the level of explanation varies according to the degree of aggregation, the 115

same basic pattern with a ridge of high densities is shown in the second-order analyses of the central city. Several points of warning are necessary, however, when this interpretation of the series of analyses is assessed. Most of these points arise from the nature and assumptions of trend surface analysis. First, trend surface analysis assumes that the values of the residuals are statistically independent of each other. If the residuals are autocorrelated, the values of the F-ratios are artificially enlarged. Maps of the residuals show that adjacent residuals are frequently similar.6 That the residuals are normally distributed, however, was tested for a number of the analyses, and found to hold. The selection of data points also has a bearing on the results of a trend analysis (Norcliffe 1969). Although it is not necessary that data points be regularly spaced (i.e., on a lattice), it is undesirable that they be clustered, for the areas containing these clusters would be over-represented. In the case of the ward subdividions, it is quite obvious that the divisions do not vary greatly in size in any systematic spatial manner. The only area over-represented is the CBD. The census tracts within the central city also are of approximately the same size, but when the entire metropolitan area is considered, the census tracts in the periphery are much larger than the ones in the more developed area. The irregular shape of Toronto city proper may also influence the pattern. Trend surface analysis takes account only of the values at the data points, and agreement at the map edge may not be satisfactory (Krumbein 1956). TorontoTs irregular shape, especially the northern extension along Yonge Street, greatly increases the "edge" in relation to the area covered. Conclusion The usefulness of trend surface analysis in the study of urban population densities can be assessed from several points of view: 1) as a hyopthesis-testing technique; 2) as a smoothing technique, both for descriptive purposes and for the isolation of local variations from the regional trend; and 3) as a method for describing changes in urban population density surfaces over time. A s a test of the hypothesis that population densities decline outwards from the CBD in a regular fashion, trend surface analy6

Agterberg (1964) offers tests for the independence of residuals, but these have not been performed in the present analyses.

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sis suggests that the arrangement of densities in the metropolitan area is elliptical rather than circular, and that the lakefront plays as important a role as the CBD in the determination of the pattern of densities, at least in earlier time periods. The maps derived from trend surface analysis are also of use for merely descriptive purposes, since they represent generalized surfaces and patterns which the raw data, even in map form, may not reveal. If one is not concerned with the theoretical significance of such patterns, there is no reason why the higher-order surfaces should not be used, provided that serious roundoff errors are avoided. In fact, a relatively high-order surface is needed if a substantial portion of the variance is to be explained. It is disappointing to find, for example, that although a quadratic exponential function will explain about twothirds of the variance in population density in Toronto in 1951, 1956, or 1966, a second-order trend-surface model will explain scarcely more than one-third of the variance in these same years (Latham and Yeates 1970). Furthermore, there is danger in using trend surfaces as a smoothing technique because it may displace minimum and maximum values from their correct positions. A s a method of tracing population density patterns through time, trend surface analysis may be quite useful. The 1941 to 1966 analyses showed certain changes in the pattern of population densities, but perhaps not as much change as occurred when one considers that Metropolitan Toronto doubled its population. The analyses suggest that the growth in population took the form of a fairly regular peripheral growth, and that the concentric pattern of densities did not change a great deal but merely expanded. The fact that the highest density contours appearing on the later maps are lower than the highest ones for earlier years should not be construed as evidence of decentralization. Since the density crater in the CBD can not be described by a second-order surface, as this crater expands, the highest density contour moves outwards from the CBD and declines in value. The stability of the pattern, especially when the central city is analyzed alone, supports the earlier criticism that too many studies stress decentralization, when expansion may be a more accurate term. Certain changes, however, are inadequately described by a series of trend surface analyses. Small increases in density may not be very significant in terms of the overall pattern, but in areas already overcrowded, they have profound social effects. 117

A trend surface analysis of changes in density, rather than two analyses at the beginning and end of the period of interest, may be capable of detecting relatively small changes. It may also be possible to examine change in density by incorporating time directly as a fourth variable in a four-variable trend surface analysis (Harbaugh 1964). The main virtue of trend surface analysis is that it is twodimensional, and may be particularly useful in analyzing density patterns in asymmetrical cities. The problems of spatial autocorrelation, the spacing of data points, map-edge effects, and the misplacing of maxima and minima, however, need more attention before firm conclusions about population densities can be made on the basis of trend surface analysis. Profitable future research also lies in the direction of interurban comparisons of population density surfaces, especially among declining, stable, and rapidly growing cities so that changes due to the internal n metabolism!f of cities may be separated from changes which are a manifestation of growth processes and technological change.

References

ADAMS, J. S. 1970. "Residential structure of Midwestern cities." Annals of the Assoc. of American Geographers 60: 37-62. AGTERBERG, F. P. 1964. "Methods of trend surface analysis. " Colorado School of Mines Quarterly 59. No. 4; 111-30. ALONSO, W. 1960. "A theory of the urban land market." Papers and Proceedings of the Regional Science Association 7: 149-58. BERRY, B. J. L.; SIMMONS, J. W. and TENNANT, R. J. 1963. "Urban population densities: structure and change." Geographical Review 53: 389-405. BLUMENFELD, H. 1954. "The tidal wave of metropolitan expansion." Journal of the American Institute of Planners 20, No. 1: 3-14. BOYCE, R. R. 1966. "The edge of the metropolis: the wave theory analog approach." British Columbia Geographical Series 7: 31-40. BROWN, K. M. 1968. "A note on urban population density patterns: an alternate explanation." Canadian Geographer 12: 203-5. BUSSIERE, R. 1970. The spatial distribution of urban populations. Paris: International Federation for Housing and Planning, and Centre de Recherche d'Urbanisme.

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CASETTI, E. 1967. "Urban population density patterns: an alternate explanation. " Canadian Geographer 11: 96-100. CLARK, C. 1951. "Urban population densities." Journal of the Royal Statistical Society. Series A 114: 490-96. DRAPER, N. R. and SMITH, H. 1966. Applied regression analysis. New York: Wiley. GOOD, D. I. 1964. Fortran II trend-surface program for the IBM 1620. State Geological Survey, Special Distribution Publication No. 14. Lawrence: University of Kansas. HARBAUGH, J. W. 1964. "A computer method for four-variable trend analysis illustrated by a study of oil-gravity variations in southeastern Kansas." Bulletin 171. Lawrence: State Geological Survey. University of Kansas. HAUSER, P. M. 1957. "The changing population pattern of the modern city, " in P. K. Hall and A. J. Reiss, Jr., eds. Cities and society: the revised reader in urban sociology. Glencoe, 111.: The Free Press pp. 157-74. JEFFERSON, M. 1909. "The anthropography of some great cities." Bulletin of the American Geographical Society 41: 537-66. KRUMBEIN, W. C. 1956. "Regional and local components in facies maps." Bulletin of the American Association of Petroleum Geologists 40; 2163-194. LATHAM, R. F. and YEATES, M. H. 1970. "Population density growth in Metropolitan Toronto." Geographical Analysis 2; 177-85. MERCER, J. 1968. "Density decline surfaces in urban areas." Canadian Geographer 12: 158-75. MILLS, E. S. 1970. "Urban density functions." Urban Studies 7 (1); 5-20. MONTREAL CITY PLANNING DEPARTMENT. 1964. "The wave of metropolitan expansion: a study of changes in density in the Montreal region." Technical Bulletin No. 1. Montreal: City Planning Department. MUTH, R. F. 1961. "The spatial structure of the housing market." Papers and Proceedings of the Regional Science Association 7: 207-20. NEWLING, B. E. 1966. "Urban growth and spatial structure: mathematical models and empirical evidence." Geographical Review 56; 213-25. . 1969. "The spatial variation of urban population densities. " Geographical Review 59:242-52. NJAU, G. J. 1967. "The change in population distribution in Metropolitan Toronto: 1941-1961." Department of Geography, M. A. Research Paper. Toronto: University of Toronto. NORCLIFFE, G. B. 1969. "On the use and limitations of trend surface models." Canadian Geographer 13: 338-48. SHERRATT, G. G. 1960. "A model for general urban growth," in O. W. Churchman and M. Verhulst, eds. Management sciences; models and techniques. New York: Pergamon Press 2: 147-59. STONE, L. O. 1967. Urban development in Canada; an introduction to the demographic aspects. Ottawa: DBS.

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8

Measuring accessibility change R. D. MacKinnon and R. Lau

Urban theory and experience strongly suggest that transportation systems can have striking consequences on the spatial layout of the city and even urban growth rates. Historically, the introduction of such transportation innovations as the commuter train, the electric street railway, the automobile and the truck together with the concomitant route location decisions and technological improvements to existing modes over the years have played a key role in influencing the form of the present-day city. Relationships between transportation (or accessibility) and urban land use are studied in such works as Hansen (1959), Wingo (1961), Alonso (1964), Moses and Williamson (1967), Schneider (1968) and Stegman (1969). Of course, few people seriously maintain that these innovations would have been sufficient in themselves to determine growth patterns; it can be argued, however, that they or their equivalent have been necessary for these developments to have taken place. Accompanying conditions such as the underlying preference of many if not all people and institutions for more, rather than less space in which to conduct their activites, have made certain types of transportation innovations very attractive catalytic agents to help make possible more convenient spatial distributions of activity. Before outlining the purposes of this study, a cautionary comment related to the above discussion should be made. Our casual experience with urban areas suggests that the relationship between transportation and urban form is obvious to the point of being trivial and not worthy of study. Who has not looked at a map of built-up area and land-use densities in a region and informally "accounted for n most if not all of the pattern in terms of nearness to transportation facilities? Such forms as the starshaped urban area stretching out along the rail and expressway lines are well known. These casual observations may be mis120

leading, however, for at least two reasons. First these obvious forms represent the result of long term locational processes. They have taken decades to emerge. In forecasting future land uses we should not be solely interested in the long term form of the city at some distant horizon year; just as important is the process by which this pattern emerges. In this era. of rapid and accelerating change, horizon year forecasting and planning make less and less sense. We must seek to understand and delimit the possible paths to and beyond this horizon year. In this way, public and private institutions can be planned within the context of the dynamic environment in which they must operate. The second reason why casual observations, while intuitively satisfying, may be misleading is that, typically, one identifies high density zones and "accounts for" them by observing that their accessibilities are higher than other zones with lower densities. Unless all zones, their accessibilities and densities, are included in some statistical analysis, such an "explanation" is at best inconclusive. The point here, of course, is that while high accessibility may be a necessary condition for high density developments, it is never a sufficient condition. There may in fact be a low simple correlation between accessibility change and land use (or density) change in spite of the existence of both a simple theoretical rationale and a conviction based on casual empiricism that the correspondence should be high. •*• The purpose of this study is to analyze the changing accessibility patterns in Metropolitan Toronto on the grounds that access can be an important influencing factor on the distribution of population and land use. First, the concept of accessibility is defined both nominally and operationally. Second, the data available for the Metropolitan Toronto area are discussed. Third, selected accessibility maps for different modes and years are presented, discussed, and compared. Finally, the likely implications of transportation system modification bn land use and population distributions are briefly outlined. The Concept of Accessibility The primary purpose of an urban transportation system is to give people and goods the capability of moving from one part of the ISimple statistical analyses of accessibility change and land use and population change over the 1964-1969 period have been undertaken. The results, not surprisingly, are rather inconclusive. The time span is probably insufficient for statistically significant relationships to emerge.

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urban area to another. With the spatial distribution of opportunities fixed, a transportation improvement from a given person's point of view would result in a net reduction in transportation expenditures needed to visit the same set of destinations as previously (measured as a combination of dollars and hours, for example), or an increase in the number of destinations which may be visited with the same expenditures on transportation. The transportation improvement for the population as a whole then would consist of a (weighted) sum of improvements to each resident. Of course, the distribution of opportunities does not remain fixed. Over time, households and establishments relocate their bases of operations. Moreover, in most situations, new people, establishments and buildings are introduced into the urban system. Thus, a change in accessibility may be a result of a redistribution of potential destinations as well as a modification of the transport system (Hooper 1968). Even at the nominal level, accessibility may be defined in different ways. First, it may be used to mean the ease with which a person at a particular location can interact with relevant, spatially separate destinations in the urban area. Alternatively, it may be the number and variety of such destinations which can be reached with a given level of transportation expenditure; or the summation of relevant destinations, each of which is weighted by its functional nearness to the given location. A few comments should be made about the concept in light of these definitions. First, the degree of difficulty in overcoming spatial separation and the number of destinations is common to all definitions. Thus, accessibility is a function both of the spatial distribution and numbers of opportunities as well as the nature of the transportation services connecting each origin to the set of opportunities. Accessibility may increase as a result of changes in the distribution of opportunities and/or the performance characteristics of the transportation system. In addition, all definitions of accessibility are place-specific. That is, accessibility is a characteristic of places, and all places have the attribute. Accessibility then is a concept which describes the advantages and attractiveness of a location in terms of its functional proximity to opportunities at other locations. While there are many other factors which contribute to the total attractiveness of a location, factors such as environmental quality, cost, zoning regulations, etc., accessibility is nearly always 122

cited as an important location factor by both theoreticians and urban decision makers. It is thus appropriate to consider a) alternative ways in which the concept can be operationalized; b) descriptive comparisons of alternative measures and of different years and c) attempts to relate these comparisons to changes in certain attributes of the urban spatial structure. In operationalizing the concept of accessibility, two specifications must be made: first, the functional form of the expression, and second, the precise nature of the variables to be used in that expression. The simplest measure of accessibility involves the specification of one or rrore strategic locations in the urban area and the measurement of impedance which must be overcome to reach these locations. In this study travel times are used to index impedance. (Other indices of "spatial friction" such as transportation dollar costs, discomfort, etc., are ignored.) Travel times to the CBD, a major shopping centre, a recreational area, and employment centre are measures of this type. A related measure is the number of opportunities of a given type which can be reached by making trips of less than some given duration. For example, the number of retail establishments or the total number of jobs, or jobs of a given type, within 30 minutes travel time are such measures. Slightly less intuitive indices of accessibility consist of those classed as "potential" measures. In these cases the opportunities (people, stores, jobs) of all the urban area are included for each observational unit, but each opportunity is "discounted" (divided) by the travel time required to reach the opportunity from the specified origin. Thus the accessibility index for location i is

where Pj if the number of opportunites in location j and tij the travel time between i and j. Such measures, derived from analogies of classical physics have been shown to be useful descriptors of accessibility (see for example Hansen[l959]). A standardized version of this measure is used extensively in this study:

123

The proportion of the total potential is thus identified for each location. This measure avoids the problem of overall population (or job, etc.) increases swamping the pattern of relative change when comparing maps from different time periods. These then are some of the nominal and operational definitions of accessibility which will be used in this study. Although there is considerable redundancy within this set of measures, each looks at the relative location of places from a slightly different point of view. This study is essentially descriptive in nature. It presents and discusses some "accessibility surfaces" of Metropolitan Toronto during the 1960Ts. This topic is of interest for two reasons. Transportation or accessibility is a public service and it is of some interest to observe the spatial pattern of the provision of public services. ^ Secondly, as has already been stated, accessibility is a quality of places which can play a major role in influencing the location decisions of institutions, firms, and households. Of particular interest then are changes in the accessibility surfaces over time. The effects of any transportation improvement are virtually never distributed evenly throughout an area. Thus improvements in general alter the spatial configuration of accessibility surfaces and may, as a consequence, induce major shifts in the likely patterns of land development. It is these spatial changes in accessibility upon which this study will focus. The Metropolitan Toronto Data Two benchmark years are used in this study—1964 and 1969. These years are used because of the availability of travel time data. As a part of the Metropolitan Toronto and Area Regional Transportation Study (MTARTS), the Department of Highways of Ontario (DHO) estimated the travel time data using conventional methods of determining travel times between contiguous zones empirically and from these data deriving, using a shortest path algorithm, the travel times between every pair of zones in the study area. To these times estimated "terminal times" are added so that tij is an estimate of the average door-to-door travel time between zone i and zone j. This procedure has been undertaken for two modes at two times during a typical weekday. Four Dodson (1969), for example, uses accessibility measures along with other variables as indicators of effectiveness of the urban transportation system for evaluation purposes.

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travel time matrices are available for each year then: automobile during the morning peak; automobile during the off-peak hours; transit during the morning peak, and transit during the off-peak hours. The 1964 population and employment data are taken from the same MTARTS survey. The population and employment data for 1969 derive from a data set obtained from DHO* The original data derive from a survey and assessment roles. These data are probably not as accurate as the 1964 data, but it is hoped no serious systematic error is introduced. ACCESSIBILITY MAP COMPARISONS Changes in Transportation 1964-1969 The period 1964-1969, while short, is significant in the history of Metropolitan Toronto transportation. Three transportation developments would seem to dominate all others. First, in 1966, an east-west subway line was opened and later, in 1968, extended. Second, the Don Valley Parkway was extended from Eglinton to north of Highway 401 and from Bloor St. south to the Gardiner Expressway, giving residents in northeastern Metro direct freeway access to the core. Finally, the Government of Ontario (GO) commuter train service was introduced along the lakeshore as a large scale experiment to determine the viability and consequences of opening a comprehensive commuter service connecting outlying areas with the Toronto CBD. Although one would expect this latter development to have immediate consequences largely in areas beyond the Metro boundaries, there are five GO stations within the Metro area and thus some internal impacts may be apparent. In addition to these obvious developments many others have undoubtedly taken place. The simple,addition, removal, or improvement of service on a bus route may have significant consequences for the accessibility of an area. In addition to changes in the transportation system, of course, changes in the number and spatial distribution of people and opportunities may alter all of the measures of accessibility outlined in the previous section. In terms of attractiveness of a parcel of land vis-a-vis its relative location, it is immaterial whether the changing accessibility is a result of an altered transportation system or changing spatial patterns. Of course, the connection between these two terms in an accessibility expression is very close in that transportation developments may induce changes in land use patterns. One could argue therefore that it 125

would be useful to have a measure of the quality of transportation which could then be used to forecast land use changes. But land use changes are as much affected by the current and anticipated distribution of land uses as they are by current and anticipated performance characteristics of the transportation system. Because the demand for transportation is essentially a derived demand, one must evaluate changes in transportation system performance in terms of the increased ability to reach certain types of destinations. Maps of Access to Population The first set of maps to be considered are concerned with accessibility to population. For both commercial and social reasons, it is desirable for certain activites to be accessible to large numbers of people. Retail stores and other businesses need to be near their customers. Although any one household only interacts with limited numbers of persons, a highly accessible place is more likely to be near those with whom it wishes to visit than an alternative location with a lower access rating. ^ In addition, of course, the location of many activities in the urban area is highly associated with the distribution of population; thus population may be used as a surrogate for many other activities. In Figure 8.1, an indication of the pattern of access to population by automobile is given. Of course, the basic pattern is a concentric one and because of the eccentric location of the Toronto core, the peak of the distribution is four miles north of the CBD. Indeed the Toronto CBD constitutes a small but fairly deep depression in the population potential surface. This is partly because the areas contiguous to the CBD have very low residential populations; in addition, congestion levels in and around the CBD are very high. Thus both the P and t terms of the potential measure would result in low values for the CBD. Of course the CBD would have very high value if the daytime population distribution were used. There is some evidence that the population potential surface is strongly influenced by expressway location—the Don Valley Parkway and Highway 401 in particular. The influence of expressways are perhaps even more apparent in Figure 8.2. Using the 3

This study is quite aggregate in nature as it fails to consider nearness to specific socioeconomic and ethnic groups. Many activities are rather selective in their importance to particular groups within the urban area. More refined accessibility measures have been developed, but are not reported here.

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Figure 8.1 Metro Toronto population potential map 1969 Auto off-peak travel time.

Figure 8. 2 Difference in population in Metro Toronto between 1964 and 1969 within 30 min. auto off-peak travel time.

number of people within thirty minutes auto travel time as the accessibility measure, this map considers the change in access from 1964 to 1969. It is important to re-emphasize that two things can affect the accessibility of a place—changes in the performance characteristics of the transportation system and shifts in the population distribution. The spatial pattern of the map in 127

Figure 8.2 is quite interesting. The accessibility of all areas except the core of the CBD has increased.4 The largest increases are located first in an L-shaped ridge to the northwest, west, and south of the CBD and secondly, in a broad suburban band to the west, north, and northeast. To a large extent, this pattern reflects the rapid growth of the suburbs during the 1960 f s. In addition, the impact of the extension of the Don Valley Parkway is also demonstrated. Less easily interpreted is the band of high increases to the northwest, west and south of the CBD. This could reflect the urban redevelopment which has taken place in the form of high rise apartment developments and the opening of the Gardiner Expressway along the lakeshore to the west of the CBD. The deep depression (discontinuity) centred on the core is probably exaggerated. 5 The complementary nature of automobile and transit modes can be seen by comparing Figures 8.1 and 8. 3. Whereas congested traffic depresses the CBD area in terms of automobile access, the transit mode flourishes under these circumstances. The transit access map in Figure 8.3 evidences a concentric pattern with a very high gradient centred on Yonge and Bloor and extending out from there along the subway lines. Figure 8.4 strikingly demonstrates the accessibility impacts of the introduction of the Bloor-Danforth Subway line. Particularly at the east and west termini and at critical intermediate stations sharp in creases in accessibility are apparent. In addition, note the finger-like projections north and south of the dominant linear ridge; these are coincident with major connecting transit services (subways, streetcars and buses). It is thus seen that some locations are more able to take advantage of transportation improvements than others—that is, those areas which are themselves more accessible to the new transportation facility. Also of some interest in Figure 8.4 are the areas of decline, particularly the long strip south of Highway 401. This decline is remarkable since it has occurred in spite of an overall population increase of 340,0001 4

The total population change is about 340,000.

5 The decline in the CBD's accessibility is accentuated by DHO's terminal time convention. A seven minute terminal time has been imposed on core CBD zones, four minutes more than in 1964. This increase has resulted in a reduction of the 30 minute hinterland in the west and north—a reduction which more than offsets the gains made in the northeastern section.

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Figure 8.3 Population in Metro Toronto 1969 within 30 rnin. transit off-peak travel time.

Figure 8.4 Difference in population in Metro Toronto between 1964 and 1969 within 30 min. transit off-peak travel time . Access to Employment Opportunities The length of the journey-to-work is often cited as a major factor influencing the residential location decision making process. Many models and theories look solely at the trade-off between distance to job site and rent or land values as the two critical 129

factors in residential site selection. As in the previous section, we can index accessibility as either a "potential" measure or the number of jobs within a certain travel time from each zone. Figure 8.5 gives an indication of the pattern of access to employment opportunities by automobile in 1969.6 ^6 can observe a generally semi-circular pattern centred on the CBD but two important deviations from this pattern are evident. First there is a pronounced tendency for the contours of the surface to TT V TT up the Don Valley Parkway and to the east along Highway 401. The northeastern sector of the city is significantly more accessible to jobs than the northwestern sectors—this is true despite the fact that the northwestern sector itself has many more jobs. The Don Valley Parkway gives people the ability to get to jobs in the CBD and along Highway 401 whereas there is no equivalent limited access facility in the northwestern sector. This map and the map in Figure 8. 6 provide tangible evidence that if the Allen (nee Spadina) Expressway is not completed, the northwestern sector will not be as well served as the northeastern sector and growth rates will as a consequence be higher in the latter area. The spatial changes in standardized employment potential also demonstrate the large in creases in "In many ways, a more satisfactory measure of employment accessibility has been suggested by Arad (1969) and Koike (1970). To use this index, jobs and people are grouped into m income classes. Jj^j is the fraction of jobs in zone j of the kth class. P!^ is the fraction of population residing in zone i in the kth household income class. Mji is then defined as

If MJJ = 1, there is a perfect match between the type of people residing in zone i and the jobs in zone j. The mix of jobs in zone j is a perfect fit to the mix of potential employees in zone i. As MJJ approaches zero, the relevance of zone j to zone i's residents diminishes. The accessibility of zone i is then defined as where Nj is the set of zones which are within 30 minutes of zone i or, alternatively, a potential measure can be used:

Maps of 1964 Metropolitan Toronto have been prepared using this matching factor method. On balance, they appear to give a more satisfactory measure of accessibility patterns. They are not reproduced here, however, since the focus of this paper is on change and the appropriate household income data are not available for 1969.

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Figure 8.5 Employment opportunities in Metro Toronto 1969 within 30 minutes auto peak travel time.

Figure 8.6 Difference in employment potential between 1964 and 1969 auto peak travel time. accessibility in the northeast in particular and the suburbs in general at the expense of more central areas. Even to this generalization there are exceptions with slight increases in the core and declines in southern Scarborough, for example. Returning to Figure 8.5, the second significant deviation 131

Figure 8. 7 Metro Toronto employment potential map 1969 transit peak travel time.

Figure 8. 8 Difference in employment opportunities in Metro Torontc between 1964 and 1969 within 30 minutes transit peak travel time . from a simple concentric ring pattern is the cratering of the surface over the CBD. This is somewhat difficult to interpret in that the CBD has by far the highest job density of any area in the city. This again demonstrates the effects of the (perhaps excessive) terminal times imposed by the travel time survey. 132

The transit patterns (Figures 8.7 and 8.8), as we have come to expect, are quite different. The transit accessibility surface is much more highly peaked. There is a greater tendency for sharper ridges of accessibility to develop along important transit corridors. In addition, of course, the height of the transit access surface is everywhere lower than that of the automobile access surface; that is, at no location are more jobs accessible by transit than by automobile. Changes in access by transit are overwhelmingly dominated by the effects of the introduction of the Bloor-Danforth subway line. Large increases along the line itself and along important subway, bus, and streetcar lines connecting with it are apparent. A comparison of the change maps for transit arid auto is instructive. Developments in transit have had more intensive, more localized, and more central consequences. Changes in automobile access to jobs has had a strong but more diffuse and suburbanizing tendency. Accessibility to Critical Focal Points in the Urban Areas The final category of accessibility measure to be considered is an index of the difficulty in moving from any area to a specified location deemed to be of some real or potential importance. The location could be a major activity centre or a critical node in the transportation system for example. This measure has the advantage that it holds everything except the performance characteristics of the transportation system constant. It is a univariate measure of accessibility with a high degree of locational specificity. Values for origin-destination pairs are not aggregated and this measure is not directly affected by shifts in the locational distribution of land uses. The automobile accessibility maps, none of which are shown here, show a general increase in accessibility (5-10 minute travel time reduction) for circumferential trips. Trips to and within the core area, however, have experienced increases in travel time of about the same magnitude. This accessibility measure would thus indicate that suburban locations are more attractive locations than previously for most activities and opportunities in the urban area.^ 7 Changes in activity locations can of course affect trip generation and distribution rates and thus alter congestion levels and. travel times.

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Several maps of travel times to selected locations and differences in such travel times from 1964 to 1969 have been prepared. One of these maps, illustrating transit accessibility to Eglington and Yonge, again demonstrates the effect of the BloorDanforth subway. Particularly in the western suburbs, accessibility has increased dramatically. In the southeastern sector accessibility improvements may have resulted from the introduction of GO commuter rail service. In the areas not served by rapid transit to the north, northwest and northeast, however, accessibility by transit has generally declined. In 1973 the northern Yonge Street corridor of accessibility decline will be erased when the Yonge street subway is extended. Small pockets of accessibility increase are undoubtedly due to local improvements in bus service. Accessibility; A Multivariate Notion Only a few categories of accessibility have been looked at in the context of Metropolitan Toronto in the 1960Ts. Access to other types of activity sites could have also been considered. Within these categories, there are at least two types of measures— potential and number of opportunities within a specified travel time. Each particular operational form of accessibility offers a significantly different perspective on the effect of transportation improvements and distributional change on the ease of movement to relevant opportunities. No attempt has been made therefore to aggregate the various measures into a single composite accessibility index. Such an attempt would be presumptuous in that it would require the specification of weights on the importance of each accessibility indicator to households and firms. In addition, of course, no attempt has been made to include measures other than travel time. Costs, comfort levels, privacy, convenience, and other factors also influence the relative effectiveness of any mode of transportation. Map Comparison: A Difficult Problem One of the most difficult and important problems in quantitative geography is to provide objective methods to compare spatial patterns. No attempt has been made in this study to use formal statistical methods of map comparison. It would have been possible to use such methods as simple regression and correlation and trend surface analysis. Perhaps more interesting would be 134

the calculation of empirical spatial autocovariance functions in an attempt to generate a parsimonious description of spatial pattern, thus facilitating inter-map comparisons. Related to this are the promising new procedures developed by Curry (1972) which permit the identification of similarities at different spatial scales. It is anticipated that future studies of these data sets will use some of these more formal methods of map comparison. Summary of Findings and Their Potential Implications for Urban Structural Change In the concluding section, the empirical generalizations regarding the spatial patterns of accessibility and accessibility change are summarized and some brief comments are made regarding the implications of these findings for future changes in the form of the city. Some of the more important observations are enumerated: 1) The most striking changes in accessibility have followed as a consequence of the opening of the cross-town subway line. In terms of accessibility, this has made Toronto more of an eastwest than a north-south city. Currently, high transit accessibility zones extend farther to the east and west than they do to the north. 2) The large positive changes in accessibility deriving from subway development are not limited to an even band on either side of the subway. The changes extend north and south along major transit lines. 3) The changes in access by automobile are less striking but nevertheless significant. While large transit changes are relatively localized and central, auto changes are relatively diffuse and suburban. In particular the northeast sector of the city has gained markedly relative to the northwest sector. 4) By virtually every measure of travel time accessibility, the automobile dominates the transit mode. Only for trips originating and terminating within a restricted central area is the transit mode more effective in terms of travel time. Transit accessibility surfaces have very sharp peaks relative to those of the automobile. Suburban areas are by and large very poorly served by this mode. 5) Because of the eccentric location of the CBD, its high levels of congestion, the low night-time population, the CBD is by no means the most accessible place within the urban area. By transit, the peak lies on Bloor Street somewhere between Bathurst and Yonge whereas for the automobile, it lies near Yonge and 135

Eglinton for most measures. The implications of these and other observations for urban form obviously cannot be specified positively. We have only considered one factor of many which can affect the changing spatial structure of a city. Amenity levels, personal tastes, income levels, the motives and power of private and public planning agencies—these and other factors have been ignored. Urban development is a very complex process and not well understood. Given this cautionary note, however, it is believed that the foregoing discussion of maps of accessibility and accessibility change together with what theory and experience we have accumulated would lead us to the following ceteris paribus conclusions concerning urban form: 1) There will be strong pressure to bid up the price and therefore the intensity of land use along the Bloor-Danforth Subway line. Intensive redevelopment is already well under way in the High Park area as well as in western Scarborough. There is some evidence that development can be just as intense if not more so at the outer extremes of subway lines as in more accessible areas between the core and the suburbs. In many inner areas of the city, land is being used very intensively already—rooming houses, large, extended families living together, etc. Land parcels are small and difficult and costly to assemble for large redevelopment projects. The physical and social environment is often perceived as deteriorating. Finally, in their journey to work, long distance subway riders are better accommodated than shorter distance commuters. 8 Under such circumstances, very accessible "suburban" areas are ripe for redevelopment. 2) Road users will find the suburbs more attractive than previously. The largest increase in automobile accessibilities are in the suburbs, particularly in the northeast. Thus not only residential development but population serving activities and industrial developments will probably increase in these areas. In general, virtually any anticipated highway development will increase the accessibility of suburban land both absolutely and relative to more central areas of the city. Accessibility to 8 This will be no small consideration for the increased volumes of passengers who will use the already crowded Yonge Street subway line after it is extended in 1973.

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the core area from the suburbs by auto will not increase as the result of any foreseeable expressway developments, however. Indeed, it is very likely they will continue to decline. In contrast, while subway construction may increase the accessibility of suburban areas, it will tend to strongly focus development in relatively narrow corridors with some extensions along previously existing transit lines.

References

ALONSO, W. 1964. Location and land use. Cambridge: Harvard University Press. CURRY, L. 1972. "A bivariate spatial regression operator. " Canadian Geographer 16: 1-14. ^ DODSON, E. N. 1969. "Cost-effectiveness in urban transportation." Operations Research 17; 373-94. HANSEN, W. G. 1959. "How accessibility shapes land use." Journal of the American Institute of Planners 25: 73-6. HOOPER, W. L. 1968. "Transportation: burden or blessing on the urban environment. " Transportation Research 2. MOSES, L. and WILLIAMSON, H. W. 1967. "Location of economic activities in cities." American Economic Review 57; 211-22. SCHNEIDER, M. 1968. "Access and land development. " Special Report No. 97. Washington, D. C.: Highway Research Board pp. 164-77. STEGMAN, M. A. 1969. "Accessibility models and residential location. " Journal of the American Institute of Planners 35: 22-9. WINGO, L., Jr. 1961. Transportation and urban land. Washington, D. C.: Resources for the Future, Inc.

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9

Net migration patterns" J.W. Simmons

As the city grows and the land use patterns evolve, the distribution of the population adjusts as well. As we have seen in Paper 7, the population density pattern changes regularly over time, but more subtle changes in household characteristics are taking place, as well. Murdie (1969) has described the main resultant trends in the social changes of a decade in Metropolitan Toronto, and in Section IV, the actual household relocation process is described. This paper attempts to link these two approaches by giving some idea of the ways by which the constant turnover of aggregates of households is translated into observed social change. Most social change within a metropolitan area occurs by way of intraurban migration. Population growth or decline, changes in income or ethnic characteristics, and the changing demographic structure of a neighbourhood result from the replacement of one population by another rather than in situ modification of the original residents. * This paper describes spatial and temporal patterns of net migration for various age-sex groups in Metropolitan Toronto, using census tract data for four points in time: 1951, 1956, 1961, and 1966. The analytical procedures and problems are discussed first: followed by a description of the main features of the spatial distributions. DEFINITION AND MEASUREMENT Net migration is defined as "the difference between the total number of persons ever entering the area during the (time period) and *This paper is an abridged version of an earlier paper: Simmons (1971); and was largely supported by the Ontario Institute for Studies in Education. •"-Present states of knowledge about intra-urban population movements are surveyed in Simmons (1968), and variations in demographic structure within urban areas are discussed in Coulson (1968).

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the total number of persons ever leaving the area during the (time period)" (Siegal and Hamilton{i952]p. 481). It does not indicate the absolute numbers of in-movers or out-movers, but the minimum or net movement required to produce a certain population change. It does separate out-movement and in situ effects, making it possible to predict each one separately, but because it underestimates the total amount of movement, it is of little use in constructing behavioural models of the movement process. It should also be noted that a high rate of net migration can continue without altering the internal population structure, as happens, for instance, in the TT port-of entry" part of the city or at an army boot camp. A variety of operational procedures exists for deriving net migration, each with different data requirements and different degrees of bias in various situations. They have been discussed in a number of articles, and in two recent papers, Stone (1967a and 1967b) provides a synthesis plus an appraisal of the Canadian data sources. The survival ratio technique was used in this study primarily because of the nature of the data available, but also in the knowledge that it probably provides better estimates than other alternatives. The various sources of error will be discussed more fully below. In the survival ratio method the population subtypes are age groups whose duration coincides with the length of the census period. All members of a group m at the beginning of the time period are aged into the next group m+l at the end of the time period, or die. This simplifies the calculations considerably. The operational definitions: NetMigration = Pj,m,t - (pj ,m-l, t-1 " Deaths) (Mj, m, t-1) - pj, m, t ~ (pj, m-1, t-1 x survival ratio) [1]

where the survival ratio is the proportion of an initial population of this type which lives through to the end of a time period. From equation [1] estimates of net migration require data on populations at each point in time plus estimates of the survival ratios. The sources of data are the census tract books published by the Dominion Bureau of Statistics. 2 Unfortunately city growth, 2 See Dominion Bureau of Statistics (1953) Census of Canada. 1951, "Population and Housing Characteristics by Census Tracts: Toronto," Bulletin CT-6; Census of Canada.

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municipal boundary adjustments and land use changes lead to alterations in census tract boundaries, making comparisons from year to year difficult, particularly in some of the suburban areas. Wherever possible, these problems were avoided by shifting to a slightly higher level of aggregation, forming combinations of tracts. The later censuses use more tracts, so that between 1961 and 1966 about 300 tracts are used, while in 19511956 only about 250 are available. Further data adjustments are necessary in the age-sex measures themselves. Population totals by age and sex are not consistent at the four census intervals. Sex breakdowns by age are not available for 1951 and five-year age aggregates for census tracts are not published between the ages of 25 and 65 for any census. Provision is made within the computational program to provide estimates of these values, although the results are somewhat suspect. Estimates of missing age-sex groups required two major assumptions: a) that the sex breakdown for all age groups in 1951, and proportions of population in each half of the ten year age groups 25 to 64, are the same for each tract in Metro, and b) that these ratios can be estimated by means of the York County totals. Metro accounts for about 90 per cent of the York County population during the time period studied. York County data for 1951, 1956, 1961, and 1966 are used to estimate proportions in the first half of each age group in each census tract, with the population in the last half computed as residuals from the total. The next stage generates the sex breakdowns in 1951 for each census tract, again using the York County proportions. Finally a total population (all persons up to age 70) is created. Errors undoubtedly result from these assumptions. Sex ratios vary due to institutions such as hospitals, colleges, jails, etc.; and age groups will vary with growth rates. Rapidly growing areas will have disproportionately large populations in the younger half. The assumptions affect only a part of the matrix, however, and their validity can be better evaluated when these results are compared to those computed from more accurate data. 1956. "Population: Census of Canada. Toronto," Bulletin by Census Tracts:

Characteristics by Census Tracts: Toronto", Bulletin 4-7 (1958); 1961. "Population and Housing Characteristics by Census Tracts: CT-15 (1963); Census of Canada. 1966. "Population, Characteristics Toronto," Bulletin C-20 (1968).

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The calculation of net migration requires the use of a survival ratio. In this case the forward survival ratio is used, equivalent to the ratio of population P j , m , t surviving at the end of the time period to population Pj ,m-l, t-1 living at the beginning of the time period. These data are obtained from the Dominion Bureau of Statistics Life Tables, 3 and survival ratios were calculated for each age group, male, female and total for three five year intervals, 1951-1956, 1956-1961 and 1961-1966. Absolute net migrations are calculated from equation [1] and matrices of net migration rates are developed as well. [2]

Net Migration rate

The result is a large body of output for each tract, with extensive amounts of potential error, but capable of indicating broad trends when each datum is examined in conjunction with other tract results, and compared with results for other age groups in the same tract, for other time periods. The possibilities of error are sufficiently large to merit discussion elsewhere.^ For spatial units of this size and 5 year time intervals the main source of error lies in the population counts. Errors can amount to ten per cent of the sub-population and a large proportion (50 per cent or more) of the net migrants. The value is an outer boundary, however, and in this study is mitigated by a) the high rates of net migration obtained for almost all age groups and many of the tracts, and b) the spatial contiguity and association of tracts. Each tract represents a sample of a larger area and when samples are combined—averaged—on a map, the error of estimate becomes smaller. Considerable confidence therefore may be placed in the overall trends represented on the maps. THE PATTERNS OF NET MIGRATION The results of the analysis are extensive, with each tract generating several hundred measures. Given over 300 tracts, the information produced poses an overwhelming problem in generalization. 3 Dominion Bureau of Statistics (1960) Provincial and Regional Life Tables. 1950-1952 and 1955-1957 (Ottawa: Queen's Printer); and Dominion Bureau of Statistics (1964), Provincial and Regional Life Tables 1960-62 (Ottawa: Queen's Printer). 4

See Siegal and Hamilton (1952) 482, 488, and Simmons (1971).

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Some spatial patterns are observable by mapping, however. The basic set of output to be described here is the 1961-66 net migration by age group. After a discussion of these patterns the effects of differences by sex, and of the variations in different time periods, can be evaluated. These data represent net effects, the difference between inmovement and out-movement, and the patterns should not be interpreted in terms of human behaviour. When, for example, a net migration of 100 persons of a certain age group into a given area is observed, and speculations made on the reasons why, the tendency is to ignore the 400 people who left the area in favour of the 500 who came in. However discussions of structural change, which is what is being measured here, are inevitably couched in terms which imply movement. When, in the following discussion, the word 'suburbanization is used it refers to a spatial pattern of out-movement at the core and in-movement at the periphery. Actual patterns of movement may be very complex: core residents may be leaving the metropolitan area and peripheral residents in-migrating from abroad. Most in-migrants to the suburban areas probably come from other cities or nearby suburban locations. The initial impression from Figures 9.1-9.4 is of marked variations in the degree of movement through the life cycle (as is found in ,gross mobility rates).5 Most of the fluctuation results from movement into Metro, which is extremely high for the 1525 age group. Looking at individual maps, movements of the 0-4 (Figure 9.1) and 5-9 age groups are almost identical; with gradual outmovement from throughout the central city and older suburbs, and intense in-movement into the newest subdivisions. Age group 10-14 is a transition period—in-migration is less intense, and much more widely spread. Only in the oldest low income area is there any degree of out-movement. Subsequent age groups show variable patterns. The next age group, 15-19 (Figure 9.2) has the most spectacular pattern of in-migration. Massive in-movements take place throughout Metro, but particularly in the apartment areas of the central city, where 200 or 300 per cent increases are not uncommon. There is an imbalance however. That part of the urban core west of Parliament Street has a much higher in-movement rate than A full set of maps is contained in Simmons (1971).

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Figure 9.1 Net migration, 1961-1966.

Age group 0-4

Figure 9. 2 Net migration, 1961-1966. Age group 15-19 the east end of the city. The following five year group has already begun to disperse. In-migration is increasing in the suburban areas, out-migration begins to take place in parts of the central city. The pattern continues for age group 25-29, although the rate of movement slows down. Age group 30-34 (Figure 9. 3) shows a definite pattern of suburbanization. Out-migration is universal in the city's older areas (built before 1950). A zone 143

Figure 9.3 Net migration, 1961-1966. Age group 30-34 of little change surrounds them, and in-migration is sharply defined in half a dozen recently developed locations. The pattern is even more sharply defined for the age group 35-39, and continues for all age groups beyond that age. After age 55 migration rates slow down rapidly but conform to the general patterns of suburbanization, although the over 60 T s also respond to high rise areas in the city centre. Males and females migrate in much the same pattern in time and space. In the early stages of movement toward the core, however, females tend to lead males by five years. Most of the female in-movement to the city centre takes place at 2024, while males peak at 25-29. Similarly women begin outmovement in an earlier age period. After 30, however, the patterns become more similar, until the older age groups are reached. Past the age of fifty, the higher survival rates of women gives them numerical superiority and increasingly they dominate the total movement pattern. In order to evaluate further the spatial pattern, the effect of size of spatial unit must be eliminated and rates of net migration examined as a proportion of the original population in that agesex group. Three overall generalizations can be made. First, rates of net-migration vary by age group in proportion to the mobility of the population. Overall net migration rates will be higher for persons aged 15-34. Secondly, net migration rates are proportional to the total net migration for Metro Toronto. 144

Figure 9. 4 Net migration, 1961-1966. Age group 45-49 Age groups with a very high overall net in-migration will have proportionally higher net migration rates for various points in the city. Third, a broad ring of new development at the perimeter of the built-up area creates very high rates of in-migration in all age groups, because of the high total growth rate in those areas. Aside from these general patterns of variation, spatial patterns for particular age groups show considerable divergence. If five age groups are examined, the distribution of net migration rates identifies significant nodes of change, and broadly stable areas. Small children (0-4) are rapidly leaving the core area, and much of the older suburbs, particularly the areas of recent European immigration on the west side. The older Anglo-Saxon low income area to the east remains quite stable, however. By and large the high income sector of the city is attracting this age group at the expense of the low income areas. The 15-19 age group (Figure 9.5) is moving into virtually all parts of the city. Only in some of the older suburbs—particularly Etobicoke—is there stability. In-movement is particularly high in areas where apartment construction is taking place, and in the central core of the city. With age 20-24 the migration pattern changes. In-movement to the downtown area becomes less intense and in-movement to the suburbs increases. Many parts of the central city show net out-movement and only the newer apartment areas attract net in145

Figure 9.5 Net migration rate, 1961-1966. Age group 15-19 migrants. Ten years later all growth is concentrated in the areas of new development. Slight out-migration is universal for all other residential areas, but nowhere is the decline precipitate. This pattern continues through age 55-59; decline in the older parts of the city and growth at the periphery. The broad patterns of variation in net migration are stable through time. Dot maps of net migrants for 1951-1956, 19561961 and 1961-1966 are quite similar although the growth and development of the city tends to expand the ring of suburban development outward, as well as to enlarge the inner core of redevelopment in the city centre. IMPLICATIONS In summary, the following major implications for understanding social change in the city can be suggested: 1)

High rates of net in- or out-migration (greater than 20 per cent in five years) characterize all parts of the metropolitan area and virtually all age-sex groups. In terms of demographic structure there is little stability anywhere within a rapidly growing urban area. Part of this movement reflects the steady state functioning of a metropolitan area. Part of it is a product of the growth process which adds a steady 146

2)

3)

4)

5)

6)

stream of young people, and to shifts in the housing market toward specialized age group facilities—such as high-rise apartments and townhouses. The degree of net migration reflects in part the allocation of net migrants to the largest metropolitan area within any given urban system. Rapidly growing cities will have a large number of young in-migrants (15-34) to redistribute within the city. The analysis provides a link between the growth of the metropolitan area and the growth and change of specific areas. Internal population redistribution also occurs by age-sex group, but measures of intra-city net migration are modified by the distribution of the city growth and the consistency in direction of movement. Spatially, the patterns show the specialization of life cycle roles in various parts of the city. The central city absorbs large quantities of young people from outside the metropolitan area and from the periphery. The suburbs absorb great numbers of older people in a continuous exchange with the central city. The metropolitan area functions as an entity with different roles (and public services required) in the inner and outer areas. The impact of these massive population exchanges is softened by their continuity. The same processes have been going on for at least fifteen years and various areas have built up facilities appropriate for their specific roles—housing, commercial activities, public services etc. It should be possible, however, to evaluate the spatial shifts of importing and exporting regions for the various agegroups over time. At what point does a specific area begin to lose people of one age and gain another?

References COULSON, M. R. C. 1968. "The distribution of population age structures in Kansas City." Annals of the Association of American Geographers 58:- 155-76. ELDRIDGE, H. T. 1965. Net intercensal migration for States and geographic divisions of the United States. 1940-1960. Philadelphia: Population Studies Center, University of Pennsylvania.

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GEORGE, M. V. 1970. "Internal migration in Canada: demographic analyses." 1961 Census Monograph. Ottawa: DBS. HAMILTON, C. H. 1966. "Effect of census errors in the measurement of net migration. Demography 3, No. 2: 393-415. MURDIE, R. A. 1969. "The factorial ecology of Metropolitan Toronto, 1951-1961." Research Paper No. 116. Chicago: Department of Geography. University of Chicago. PRICE, D. O. 1955. "Examination of two sources of error in the estimation of net internal migration." Journal of the American Statistical Association 51: 689-700. SIEGAL, J. S. and HAMILTON, C. H. 1952. "Some considerations of the use of the residual method of estimating net migration." Journal of the American Statistical Association 47; 481. SIMMONS, J. W. 1968. "Changing residence in the city: a review of intraurban mobility." Geographical Review 58; 622-51. . 1971. "Net migration within Metropolitan Toronto." Research Paper No. 44. Toronto: Centre for Urban and Community Studies. University of Toronto. STONE, L. O. 1967a. "Evaluating the relative accuracy and significance of net migration estimates." Demography 4; 310-30. . 1967b. "A comparison of biases in the principal estimates of net intercensal migration." Unpbulished paper. Ottawa. TARVER, J. D. 1962. "Evaluation of census survival rates in estimating intercensal state net migration." Journal of the American Statistical Association 57; 841-62. ZACHARIAH, K. C. 1962. "A note on the census survival ratio method of estimating net migration." Journal of the American Statistical Association 57: 175-83.

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IV

Social Interaction and Residential Relocation

Editors' comments Throughout the last decade, it has become apparent that the study and analysis of urban form and structure—the land use maps, the designation of urban neighbourhoods, the vast literature of factorial ecology—must be complemented by research on linkages among urban locations, communities and activites. Spatial propinquity does not necessarily lead to frequent contact or interaction. Measurements and evaluation of urban linkages must be obtained in addition to maps of spatial structure. Studies of intra-urban contacts and linkages yield a variety of insights. In addition to their intrinsic interest as types of urban process and indications of functional and social communities in the city, they contain profound implications for future spatial change. Wellman!s study, building on data from an extensive study of the borough of East York in Toronto, describes the complexity of contacts undertaken by urbanites in their daily existence. The results require a re-evaluation of notions of community, and of the personal support relationships on which urban households depend. The daily contact patterns of the city define the access requirements of urban activities, be they households, firms or institutions, and the day-to-day contact requirements constrain future locations of those activities. Moreover, the daily contact patterns describe zones of awareness, of knowledge, of opportunities, and define paths of diffusion and ultimate social change. Gad, Peddie, and Punter link day-to-day contact patterns with more permanent household relocation. The study beings with the selection of households in an area adjacent to the Spadina Expressway ditch in Toronto slated for high-rise redevelopment. The reason for moving is established, and the conditions behind the move are basically the same. The authors then show how knowledge and daily activity patterns affect the search space for a new home, and how this search is modified by ethnic differences in the same population. The third paper, by Michelson is also concerned with the 150

household relocation process but from a different point-of view. Using an intensive survey of attitudes and household time-budgets undertaken in Metropolitan Toronto, this paper describes and interprets the context of motivations and expectations regarding household activities within which residential movement decisions are made. In particular, the activities of wives are emphasized and compared with those of husbands, in both single-family and high-rise accommodation. In the final paper, Simmons and Baker evaluate the overall pattern of relocation of households in Toronto, and discuss the implications for social change. Despite the obvious importance of the varied linkage patterns in the understanding of urban process, no comprehensive evaluation of these matrices has ever been undertaken for a major city. The work reported in this section represents a first step in this direction. That even this much has been accomplished is due to the development of three important sources of data in Toronto— the Metropolitan Toronto and Region Transportation Study, the Clarke Instituted Yorklea Study, and MichelsonTs Housing Environment Study.

151

10

Community ties and support systems: From intimacy to support * B.Wellman P.Craven, M.Whitaker, H.Stevens, A. Shorter, S. Du Toit and H. Bakker

Old myths die hard, but they do sometimes die. Extensive research in the past two decades has conclusively demonstrated the general inaccuracy of portrayals of urbanites as lonely (Stein 1960; Nisbet 1962). Most city-dwellers have important informal interpersonal ties that are often utilized as channels of informal support in times of everyday and emergency stress. The ties with intimates are often important components of urbanites1 "personal communities" of support and sociability. •*• 'These personal communities go beyond the neighbourhood because contemporary communication and transportation facilities can maintain ties at great distances (Wellman 1972). Indeed there is a wide geographic dispersion of intimates, with only 13 per cent reported as living in the same neighbourhood as the respondent (Wellman; Hewson and Coates 1969).2 The basic task of this paper is the examination of conditions under which perceived Tt supportiveness TT of intimates outside the household is associated. In what ways are a) the social characteristics of individuals and their intimates, b) qualities of the *Many of the ideas in this paper were developed in informal interaction with Charles Tilly, Leslie Howard, Jack Wayne, Harrison White, William Michelson, Donald Coates, Norman Shulman, Deborah Tannebaum, and a number of staff and students at the Department of Sociology, University of Toronto. This study is being assisted under Province of Ontario Health Research Grant No. P.R. 196, the Laidlaw Foundation, Bell Canada, and Urban Affairs Canada. This paper is a revised and condensed version of Research Paper No. 47 published by the Centre for Urban and Community Studies (see Wellman et. al., 1971). A more extensive bibliographic citation is presented in the earlier paper. "Intimates" are those to whom one feels especially "close" in primary interpersonal relationships, be they kinfold or unrelated. Defining "neighbourhoods" as communities a priori delimits arbitrarily the scope of studying such ties. Our focus on "personal communities" enables us to inquire into the extent to which urbanites' primary ties are in fact contained within neighbourhood boundaries.

152

ties between them, and, c) structural properties of the network of ties with intimates, associated with the availability of assistance from intimates in emergency and every day matters? It will be shown that the likelihood that individuals will receive support from intimates is related to the basis of the relationship (parentchild, friends), the residential and work proximity of intimates, and the frequency and mode of contact (face-to-face, telephone). Social characteristics of urbanites and their intimates influence the availability of support only by determining indirectly the character of intimate relationships. It is the qualitative and quantitative aspects of the interaction process itself rather than the social characteristics of the individuals involved which directly affect the mobilization of intimate support. Two-person ties are typically located within the larger social situtation of structured networks of relationships between intimates linked directly and indirectly to each other. ^ Some network characteristics are associated with the availability of support. "Dense" networks, for example, provide widespread support from heavily interconnected network members. The Data The data analyzed are derived from a 1968 survey of 845 adult respondents (18 years and older) in the Borough of East York, an "inner suburb" of Metropolitan Toronto. ^ East York is primarily a lower-middle-class and working-class area, heavily Anglo-Saxon Protestant in ethnic and religious composition, whose residents live primarily in single-family homes and newlyconstructed high-rise buildings. It is well-integrated into the metropolitan highway, public transportation and telephone systems. The lack of religious and ethnic diversity and the absence of extremes in socio-economic status (SES), permits analyses of social relationships while restricting analyses based on social characteristics. The demographic characteristics of the respondents are quite similar to that of the East York population at large. Respondents were asked to tell "about the people outside Because all intimates are directly linked to the respondent by definition, they must all be at least indirectly linked with one another through the respondent. ^The survey was conducted by the Community Studies Section of the Clarke Institute of Psychiatry, Toronto, as part of the "Yorklea Study" of mental health in the community. Dr. D.B. Coates, was director of that study, and we are grateful to Dr. Coates and the Institute for making the material available to us.

153

your home that you feel closest to; these could be friends, neighbours, or relatives.!T The respondents provided extensive data on the nature of their relationship with their intimates. Data on support was derived by asking which of the intimates the respondents "rely on for help in an emergency, TT and "rely on for help in everyday matters. TT Respondents reported on the network of relationships by telling which of the intimates they had named were also "close to one another." The use of the diffuse indicator of "closeness" unfortunately precludes data on "closeness" in specific situations. The data also do not permit the study of the relationships between support within the household and support provided by intimates. Measuring Availability of Support Measures were constructed to indicate the extent to which support is available from intimates. Two variables, "Everyday Support" and "Emergency Support" measure the total amount of support available. They are simple counts, ranging from zero to six, of the number of intimates who are everyday or emergency "supporters"—perceived by the respondent as available to give such support.5 The analysis, often considers the fundamental dichotomy of whether the individual has no or any support available; a majority have at least some Everyday Support (60 per cent) and Emergency Support (81 per cent) available from intimates (T^ble 10.1) There is another useful way to measure support: the likelihood than an imtimate is a supporter. 22. 6 per cent of all intimates are perceived as being available to provide Everyday Support and 30.1 per cent as being available to provide Emergency Support. Thus, although most people have some support available to them, less than half of their intimates are, in fact, supporters. The general availability of support is associated with the number of intimates, but not necessarily in a linear function. If a person has only a few intimates, each may be quite likely to give support. Two variables, "Standardized Everyday Support" and "Standardized Emergency Support" measure the likelihood of a person's getting support, without respect to number of his "Emergency support would be of the kind supplied in a major illness, emotional upset or financial crisis. "Everyday support" would be the kind supplied in minor illness, babysitting, assistance in household moving, or the loan of a small sum of money. Many intimates provide both kinds of support, of course.

154

TABLE 10.1 PERCENTAGE OF EGOS HAVING SUPPORT AVAILABLE FROM INTIMATES BY AMOUNT AND TYPE OF SUPPORT

Type of support Amount of support

Unstandardized support

Standardized support

None

1

2

3 or more

0

1-25

26-50

51-100

22.6 per cent

39.8 per cent

36.8

14.0

9.4

39.8

26.9

24.3

9.1

Emergency (845) 30.1 per cent

18.7 per cent

50.2

18.1

13.0

18.7

38.2

28.9

14.2

Everyday (845)

intimates. The standardized measures are constructed by dividing the unstandardized support measures by the respondent's total number of intimates. A respondent with six intimates, two of whom are supporters, would receive a standardized score of .33; respondent with three intimates, two of whom are supporters, would receive a standardized score of .67. Those with no supporters (including those with no intimates) would receive scores of 0. 26.9 per cent of all respondents perceive Everyday Support as being available from between one-quarter and one-half (26-50) of their intimates. Social Characteristics and the Availability of Support from Intimates The social characteristics of individuals, such as their sex and ethnicity, are often associated with variations in the character of their interpersonal relationships. The analysis considers how variations in key social characteristics might affect the propensity of different categories of individuals a) to be involved in situations in which they might need support from outside the household, b) to establish interpersonal relationships which provide informal support, and c) to have differing access to other kinds of support such as money, influential contacts and formal agencies. For example, people living by themselves or single parents may need more help from outside the household. Italians may have (Fried and Gleicher 1961; Gans 1962) a high level of supportive contact with their intimates. The well-to-do have economic resources, prestige, useful contacts, and familiarity with the ways of formal organization, and may need less informal support from intimates. A major finding of the study, though, is that such social characteristics have very little effect on the likelihood that a 155

person will receive support from intimates in everyday and emergency situations. The majority of people have a modicum of such informal support available, regardless of their social characteristics. The analysis, examines the likelihood of receiving support with respect to socio-economic status (occupation), ethnicity, stage in the life-cycle (including marital status), sex, and type of home. In general, only small variations in the amount of support are found among the major categories of a given social characteristic. It should be noted, though, that the variation in these categories in the fairly homogeneous Borough of East York is not wide. The negative findings shift the analysis to an examination of the ways in which respondents and intimates are linked. Thus, suppositions that working-class adults have comparatively more intimate support available may be based on inferences about the qualities of their social relationships. Variations in social characteristics may well be associated with differences in the nature of respondent-intimate relationships. The IntimatesT Relationship to the Respondent The analysis now turns to the examination of the effect of "closeness," "relatedness, l? residential and work proximity, and frequency of contact on the probability of intimates being supporters. Even among intimates, there can be variation in how "close" they are perceived to be (see Table 10.2). The closer an intimate is ranked by the respondent, the more likely he is to be also perceived as an everyday and emergency supporter. ^ This is so for intimates of both sexes. For example, 56.1 per cent of the "closest" intimates are emergency supporters, while only 15.9 per cent of those ranked sixth are. The relationship is not a linear one: an exceptionally high proportion of "closest" intimates are supporters. These findings support the notion of "supportiveness" as a relationship closely associated with "intimacy." Despite the transformation of extended family relationships, sociologists have continued to find that many close kin provide support for each other (see Adams [1968]; Litwak [1960a;1960b] ^The respondents were asked to report on their intimates in their order of closeness. This ordering, from 1 (closest) to a maximum of six, is reported as the intimates' rank. Many respondents reported less than six (median = 5.3). We realize that two individuals may have quite different feelings of closeness to the intimates they each rank as fourth closest.

156

TABLE 10.2 PERCENTAGE OF INTIMATES PROVIDING SUPPORT BY RANK OF CLOSENESS Everyday support 3 4 5 (740) (626) (509)

Rank

1 (820)

2 (798)

Total 22.6 per cent (879)

40.7

25.8

17.3

14.7

13.9

12.2

.38

Male 24.6 per cent

41.4

27.6

18.6

18.5

16.8

16.9

.31

Female 21.1 per cent

40.3

24.5

16.3

11.6

11.6

8.0

.45

2

Rank

Emergency support 3 4

6 (395)*

5

Gamma

Gamma

Total 30.1 per cent (1169)

56.1

32.3

24.5

18.8

17.5

15.9

.43

Male 35.7 per cent

60.9

38.8

32.5

25.6

21.6

21.3

.39

Female 25 . 6 per cent

52.5

27.5

18.2

13.3

14.1

11.3

.49

a Frequencies in this and other tables refer to total number of intimates and not just those providing support.

for example). Within the immediate family, women have often been relied upon more than males for support, with mothers, for example, being called upon to provide affective support and instrumental aid (see Bott [1957]). General confirmation of the importance of kinship is found in the East York data (Table 10. 3). Mothers and children (of both sexes) are most likely to be relied upon for everyday support (and not immediate kin are reported as intimates). The same situation exists for emergency support although fathers are as likely as mothers to be perceived as available. For other relationships, however—friends, neighbours, more distant kin—male intimates are more likely to be perceived as everyday and emergency supporters. An association between relationship, sex of intimate and support exists. 157

TABLE 10.3 PERCENTAGE OF INTIMATES PROVIDING SUPPORT BY INTIMATE'S RELATION TO THE RESPONDENT AND SEX

Relationship

Everyday support per cent female male

Parent (338) Child(226) Sibling (585) Other Relative (771) Neighbour (240) Friend (1673)

24.6 38.9 20.0 26.7 21.1 23.1

Gamma

36.8 33.9 20.3 16.2 20.7 18.3

- 28* 11 01 31** 01 15*

Emergency support per cent female male

49.2 56.5 38.4 31.4 40.0 31.1

50.5 44.1 26.1 24.0 29.3 17.1

Gamma

-.03 .25 .28** .18* .23 .37***

Chi-square is significant at the .05 level. Chi-square is significant at the . 01 level. ' Chi-Square is significant at the .001 level.

mate, the presence of kinship ties does not alter the likelihood of receiving support. For less close intimates, the relationship becomes an important factor. Parents and children are very likely to be perceived as supporters, no matter how low their intimacy rank. There is only partial evidence for expectations that the socio-economic status (SES) of the intimate would modify the association between relatedness and support (Table 10.4). The probability that fathers will be everyday supporters increases directly with increasing SES: 25. 8 per cent of White Collar Fathers are perceived as everyday supporters as compared with only 14. 3 per cent of Unskilled Fathers. But there is no similar relationship for fathers in the case of emergency support, nor for mothers, who give similar high levels of everyday and emergency support at all SES levels. The SES-support relationships of other kin follow no consistent pattern. Expectations that high SES friends and neighbours would be most likely to be supporters are almost completely refuted (unlike Axelrod [1956]). Indeed, it is the Unskilled male friends and neighbours who are the most supportive in everyday matters. The sheer availability of nearby intimates should increase the likelihood of their being supporters, even though the advent of the telephone and high-speed transportation has probably lessened the importance of proximity in interpersonal relations (see Webber [1964]; and Wellman [1972]). In East York there are only weak associations between residential proximity and support (Table 10.5) even when sex and SES are controlled. There is no indication of the disproportionate dependence of women and working-class adults on proximate intimates for sup158

Figure 10. 1 Intimates providing percentage of support by relationship to respondent port that has been indicated elsewhere (Young and Willmott 1957; Gans 1962). Unrelated intimates are somewhat more likely to be supporters if they live nearby (see also Keller [1968]). Immediate family members, on the other hand, are about as likely to provide support if they are near or far. ^ Most informal support comes from those intimates who do not live in the respondents neighbourhood . ^The association between residential proximity and the likelihood of receiving everyday support from intimate friends is more than twice as strong (gamma = . 17) as the association when not controlled for relatedness.

159

TABLE 10.4 PERCENTAGE OF INTIMATES PROVIDING SUPPORT BY SOCIAL CLASS AND RELATION TO THE RESPONDENT

•Occupation Relation to the respondent Father (87) Mother (115) Male neighbour (73) Female neighbour (85) Male friend (676) Female friend (663)

White collar

Clerical

Skilled

Unskilled

Gamma

Everyday support

25.8 per 38.6 per 24.1 per 27.0 per 25.7 per 20.9 per

cent cent cent cent cent cent

0.0

42.1 0.0 4.8 26.0

15.8

.16 -.01 -.01 .15 .11* .08

14.3 36.4 20.0 25.0 38.7 9.5

20.0 41.5 25.0 21.7 16.8 19.7

Emergency support Father (87) Mother (115) Male neighbour (73) Female neighbour (85) Male friend (676) Female friend (663)

51.6 per 54.5 per 48.3 per 32.4 per 33. 1 per 17 . 8 per

cent cent cent cent cent cent

0.0 63.2 28.6 14.3 22.0 16.7

-.09 .04 .07 -.10 .08 .03

57.1 27.3 40.0 50.0 35.5 9.5

53.3 65.9 43.8 39.1 28.0 17.8

* Chi-square is significant at the .05 level. TABLE 10.5 PERCENTAGE OF INTIMATES PROVIDING SUPPORT BY RESIDENTIAL PROXIMITY

Same building (70)

Same block (145)

Same neighbourhood (288)

East York (475)

Everyday support (25.8 per cent)

32.9

29.0

23.3

29.3

26.8

22.9

.07*

Emergency support (32.8 per cent)

41.4

38.6

34.0

37.3

30.0

31.6

.07*

Residential proximity

City of Toronto (970)

Elsewhere (983)

Gamma

* Chi-square is significant at the . 05 level. Discrepancies in percentage totals are due to the substantial number of missing cases (961) in this table.

everyday support (37.2 per cent). Given the basis of their relationship, it is not surprising that they are disproportionately low sources of emergency support (22.2 per cent). Men who work together are more likely to either give or receive support than are women co-workers. 160

TABLE 10.6 PERCENTAGE OF INTIMATES PROVIDING SUPPORT BY FREQUENCY OF CONTACT

Frequency of contact Visits Everyday support (22.6 per cent) Emergency support (30.0 per cent) Phone/letter contact Everyday support (22.7 per cent) Emergency support (30.1 per cent) "Maximum contact" Everyday support (22.6 per cent) Emergency support (30.1 per cent) "Maximum contact" males Everyday support (24.6 per cent) Emergency support (35.7 per cent) "Maximum contact" females Everyday support (21.1 per cent) Emergency support (25.7 per cent)

5 times per week

2-4 times weekly 1-2 times 2-11 times one time never per week per month per year per year or less

Gamma

(577)

(869)

(966)

(655)

(312)

(39)

36. 6 per cent

33.6

28.0

19.8

9.8

5.4

0.0

.40*

39. 5 per cent

40.0

37.7

25.5

21.7

10.9

2.6

.30 *

(661)

(876)

(865)

(505)

(116)

(317)

41 .2 per cent

34.0

22.6

16.2

9.1

4.3

15.8

.39

43 .3 per cent

43.4

31.1

22.2

18.0

21.6

21.8

.30

(814)

(940)

(819)

(407)

(61)

(13)

38,, 1 per cent

30.8

19.5

13.9

3.7

0.0

0.0

.46 *

41,, 0 per cent

38.8

29.5

19.0

17.0

18.8

0.0

.31*

(355)

(416)

(383)

(191)

(33)

(5)

42,.6 per cent

31.8

21.4

19.3

3.7

0.0

0.0

.44*

24,.9 per cent

43.1

38.0

27.4

24.6

27.3

0.0

.22 *

(459)

(524)

(436)

(216)

(28)

(8)

35,.3 per cent

30.1

17.9

9.2

3.7

0.0

0.0

.49 *

39,.8 per cent

35.5

22.7

11.7

10.2

7.1

0.0

.43*

(456)

(540)

(827)

(317)

(510)

* Chi- square is significant at the . 01 level.

Co-workers provide another type of relationship. One-third (33.0 per cent) of the 524 working respondents had a co-worker as an intimate, most had only one. They comprise 6. 3 per cent (252) of all intimates; only four (of 252) co-worker intimates are related to the respondents. Co-workers are as likely as close kin and more likely than neighbours to be reported as sources of The more frequently an intimate is in contact, the more likely he is to be regarded as a supporter, 8 though this is slightly less true for emergency support. Although some intimate supporters maintained contact only by telephone or letter, for the most part the provision of support usually requires some face-to-face contact between an individual and his intimates. The relationship with the intimate affects the association between frequency of contact and the provision of support. Neighbouring intimates are potentially available for support in many everyday matters no matter how frequently seen. On the other hand, intimate parents and siblings are likely to be supporters only when frequent contact is maintained. 9 The likelihood of female intimates being supporters is more closely associated with their frequency of contact with the respondent than it is for male intimates. Women are more likely to provide the kinds of socio-emotional support that are manifested through frequent contact. The ways in which individuals are related to intimates are more meaningfully associated with the likelihood of support than are their social characteristics. Interesting associations exist for all of the qualities of relationship analyzed: TTclosenessn rank, !!relatedness,!T residential and work proximity, and frequency of contact. In some instances, however, a social characteristic modifies the association between relationship quality and support, as in the case of sex of intimates and their frequency of contact. In general, though, it is clear that the study of interpersonal dynamics and not social statics offers more analytic power. Network Characteristics To this point the analysis has focused on first-order, egocentric 8

Frequency of contact was principally analyzed by means of a variable (Maximum Contact) which is based on the highest frequency of contact, be it face-to-face or telephone/letter. See Whitaker (1971). 9

The parents, gamma equals .54 (everyday) and .28 (emergency). For neighbours, it equals . 18 (everyday); this is not a statistically significant association.

162

networks. -^ They are egocentric because they describe those people to whom one particular individual feels close, and they look at the network from his vantage point. They are first-order networks insofar as they consider only his intimates, and not the intimates of the respondent's intimates (except when the latter are also intimates of the individual (see Craven [1971; 1972] for a more extended discussion) . The characteristics of the networks can be useful in understanding intimate support beyond what is learned in the study of the specific dyadic relationships occurring within them (see, for example, Bott [1957]; Mitchell [1969]). There are associations between the range of a network — the number in it (up to six) — and the provision of everyday and emergency support, since the range of the network is equivalent to the number of intimates who are potentially available to give informal support. -^ Over 65 per cent of the respondents naming six intimates report the availability of support in everyday matters, while only 35. 0 per cent of those with only one intimate report the availability of everyday support (Table 10. 7). Having just a few intimates is as likely to mean having support available as having many, though. A majority with two intimates (or more) have everyday support available, and a majority of those with at least one (or more) intimates have emergency support available. The denser the intimate networks in which an individual is located, the more likely he is to have support available from intimates (Table 10. 8). There is a direct association between the density of the network — the ratio of actual to possible firstorder links in the network — and standardized measures of everyday and emergency support. *2 The relationship is somewhat l^These are analytic networks constructed from information provided by the respondent about which of his intimates are close with each other. Some intimates may not agree with their placement in such an analytic network, although Shulman's (1972) interviewing of a subsample of intimates provides a good deal of substantiation of perceptions of intimacy. Tepperman (personal communication) has cautioned against the use of statistics which assume the independence of each Ego's network. In the ultimate case, he states, all of the East York respondents and their friends may be merely part of "one huge loving network." Although such a contention cannot be disproved, it becomes less tenable with large populations and strict membership criteria. Shulman's (1972) investigation gives some support to our assumption here that each network can be statistically treated as independent of networks of other respondents. •'•-'•'Respondents were asked to list a total of six non-household people to whom they felt close. Less than 3 per cent were unable to name any such intimate, while almost 50 per cent named six (see Wellman, Hewson and Coates 1969). -^standardization by range is indicated because the larger the number of intimates, the larger the number of ties required to achieve a given level of density in the network.

163

TABLE 10.7 PERCENTAGE OF RESPONDENTS HAVING SUPPORT AVAILABLE BY NUMBER OF INTIMATES Number of intimates (Range)

0 (21)

1 (20)

2

3

4

5

6

(56)

(114)

(116)

(117)

(401)

Gamma

Everyday support

0.0 per cent

35.0

55.4

58.8

55.2

67.5

65.1

.22*

Emergency support

0.0 per cent

65.0

71.4

78.1

82.8

88.9

86.0

.36*

* Chi-square is significant at the . 01 level.

TABLE 10.8 STANDARDIZED AVAILABILITY OF SUPPORT BY NETWORK DENSITY N etwork density

51-75 per cent (241)

76-100 per cent (149)

0-25 per cent (25)

26-50 per cent (430)

88.0

38.4 34.2 19.5

32.8 25.3 34.4

47.0 12.8 24.2

2.3 5.6

2.9 4.6

14.8

15.8 47.9 26.0

15.4 34.9 35.7

20.1 22.1 30.2

3.5 6.7

6.6 7.5

23.5

Standardized everyday support None 1-25 per cent 26-50 per cent 51-75 per cent 76-100 per cent

0.0 8.0 0.0 4.0

1.3

Gamma: . 14* Standardized emergency support None 1-25 per cent 26-50 per cent 51-75 per cent 76-100 per cent

92.0 0.0 4.0 0.0 4.0

4.0

* Chi-square is significant at the . 01 level.

stronger in the case of emergency support than in the case of everyday support. The more likely one f s intimates are to be close to one another, the more likely it is that support will be available from intimates. Range and density are highly negatively associated (gamma = . 34; significant at < . 001) in our data, for example. Without such standardization, there is hardly any association between density and support.

164

Density = Number of Actual Links x 100 Number of Possible Links = lOOa/n (n-1), where a is the number of actual links in the net, and n is the total number of people (including Respondent) in the network.

Figure 10. 2 Hypothetical variations in density

Figure 10.3

Model of interpersonal ties and informal support

Implications of Findings The analysis indicates the importance of intimate networks— their quality and quantity—in understanding the provision of support. The structural properties of intimate networks and information flows through the network are not well understood, especially where the informational content is a request for support and adequate feedback is the provision of that support. The 165

composition of the networks, such as the extent to which they are socio-economically homogeneous, may also be of much analytic importance (see Wayne [1971]). Finally, a consideration of various contextual factors, such as neighbourhood characteristics, may inform the study of the interpersonal relationships in which they are situated. A conceptual framework, setting forth some of the suggested analytic relationships, is presented in Figure 10.2. The study of urban phenomena of intimate closeness and support can be profitably related to other areas of urban analysis. There are other informal sources of sociability and support availability in the neighbourhood (see Stevens and Wellman [1972]; Keller [1968]), and there are important supportive relationships that are less intense than intimacy. The study of ties of intimacy is closely related to the consideration of just how urbanites are integrated into urban societies by interpersonal and collective means. Furthermore, a consideration of informal sources of support leads directly to a consideration of how such informal support is articulated with more formal sources of aid (e.g. , physicians, welfare agencies, churches). The ties and networks analyzed here are an important aspect of human behaviour in the urban setting, but they must ultimately be considered in conjunction with other social processes.

References

ADAMS, B. N.

1968.

Kinship in an urban setting. Chicago: Markham.

AXELROD, M. 1956. "Urban structure and social participation. " American Sociological Review 21: 14-8. BOTT, E. 1957. Family and social network. London: Tavistock. COATES, D. B. et al. 1970. "Yorklea social environment survey research report." Toronto: Clarke Institute of Psychiatry, Community Studies Section.Mimeo. CRAVEN, P. 1971. "The use of egocentric network properties as predictor variables." Working Paper No. 1. Toronto: Centre for Urban and Community Studies. University of Toronto. --. N.d. "Notes on the structural description of egocentric networks." Working Paper. Toronto: Centre for Urban and Community Studies. University of Toronto. Forthcoming.

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FRIED, M. and GLEICHER, P. 1961. "Some sources of residential satisfaction in an urban slum." Journal of the American Institute of Planners 27: 305-15. CANS, H. 1962. The urban villagers. New York: Free Press. GILLIES, M. and WELLMAN, B. 1968. "East York: a profile." Toronto: Clarke Institute of Psychiatry, Community Studies Section. Mimeo. JACOBS, J. 1961. The death and life of great American cities. New York: Random House. KELLER, S. 1968. The urban neighborhood. New York: Random House. LAUMANN, E. O. 1968. "Interlocking and radial friendship networks: a crosssectional analysis." Working Paper No. 5. Detroit Area Study. Ann Arbor: Department of Sociology. University of Michigan. LITWAK, E. 1960a. "Occupational mobility and extended family cohesion." American Sociological Review 25: 9-21. . 1960b. "Geographical mobility and extended family cohesion." American Sociological Review 25: 385-94. MITCHELL, J., ed. 1969. Social networks in urban situations. Manchester: University of Manchester Press. NISBET, R. 1962. Community and power. New York: Oxford University Press. PARSONS, T. 1960. "Pattern variables revisited." American Sociological Review 25: 467-83. SHULMAN, N. 1972. "Urban social networks." Unpublished Ph.D. dissertation. Toronto: University of Toronto. STEIN, M. 1960. The eclipse of community. Princeton, N. J.: Princeton University Press. STEVENS, H. and WELLMAN, B. 1972. "The social determinants of neighbouring." Paper presented at the Annual Meeting of the Canadian Sociology and Anthropology Association, Montreal. TILLY, C. 1970. "Community: city: urbanization." Ann Arbor: Department of Sociology. University of Michigan. Revised version. WAYNE, J. 1971. "Networks of informal participation in a suburban context." Unpublished Ph.D. dissertation. Toronto: University of Toronto. WEBBER, M. 1964. "The urban place and the nonplace urban realm. " In Melvin Webber et. al., Exploration into urban structure. Philadelphia: University of Pennsylvania Press. WELLMAN, B. 1972. "Who needs neighborhoods?" In Alan Powell, ed. The city: attacking modern myths. Toronto: McClelland and Stewart. --. , HEWSON, M. and COATES, D. B. 1969. "Primary relationships in the city: some preliminary observations." Paper presented at theAnnual Meeting of the Canadian Sociology and Anthropology Association. . andWHITAKER, M. , eds. 1972. Community-network-communication; an annotated bibliography. Monticello, 111.: Council of Planning Librarians. ., et. al. 1971. "The uses of community." Research Paper No. 47. Toronto: Centre for Urban and Community Studies. University of Toronto. WHITAKER, M. 1971. "The users and uses of different modes of communication." Working Paper No. 2. Toronto: Centre for Urban and Community Studies. University of Toronto.

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YOUNG, M. andWILLMOTT, P. 1957. Family and kinship in East London. London: Routledge and Kegan Paul.

11

Ethnic differences in the residential search process G. Gad, R.Peddle and J. Punter

Research on intraurban migration is now well developed and investigators have already looked at a number of questions (Simmons 1968), but it is only recently that more complex heuristic frameworks have been proposed. One of these, summarized by Brown and Moore (1970), relies on notions which have their origins in economics (the concept of the utility function) and psychology (migration as a process of adaptation) which have been integrated by Wolpert (1965). Wolpert emphasizes that migration is a process of adaptation which can be studied by identifying the nature and sequence of stimuli received and decisions taken by the household, and by specifying the main characteristics of the household which influence each decision within a spatial context. Background This study looks at the migration of a small group of households in Metro Toronto within the above framework. Broadly, it tries to integrate household characteristics, preferences, and the household^ knowledge or awareness of the city with the actual process of looking for a new home. Of particular interest are the relationships between the household's daily patterns of interaction (the activity space), the parts of the city known to the household through its activities and from other sources (the 168

awareness space), and the areas of actual or intended search (the search space). It can be postulated that activity and awareness space are closely related to such household characteristics as stage in the life cycle, socio-economic status, ethnic origin and life style. Because the study is basically concerned with gaining insight into the substantive elements of the intraurban migration process, some form of intensive communication with the households as decision-makers was required. A fairly comprehensive questionnaire containing both open-ended and close4 questions was used. The major guideline for designing the questionnaire was the Brown-Moore framework, but questions posed in other migration studies were also incorporated. 1 The content of the questionnaire was divided into four sections—household characteristics, household preferences, household activity and awareness spaces, and household search procedure—but this conceptual order was modified to stimulate easy flowing discussion during the interview, and to provide checks for the consistency of answers. Of a sample of fifty households, thirty-nine detailed interviews were successfully completed. As the sample population is dominated by two distinctive ethnic groups (Italians and Jews), this study focusses attention on the role of ethnicity in relation to activity, awareness and search space in the context of the household relocation decision. The characteristics of the study area and the sample households are discussed and the household's preference structure is summarized. The relationships between the activity, awareness and search spaces of the Jews and Italians are examined and the resultant search spaces compared and contrasted in the light of ethnic differences in the preference structure. The Study Area While this study was being formulated, an article in the local press noted the purchase of options on single family homes by apartment developers along the excavated, but unopened section of the Spadina Expressway, about four miles north-west of downtown Toronto. This circumstance seemed ideal for the purpose of studying the decision-making process involved in the choice of a new home, before or while that decision was actually being Sample questions were taken from Simmons (1968), Rossi (1955), and Lansing, Mueller and Earth (1964).

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made. ^ The households approached by developers are located in part of an immediate postwar subdivision comparable to many found in the inner suburbs of Canadian cities. It is composed largely of detached, single storey, brick houses, most of which are generally thought to be in excellent or good condition. 3 Due partly to economic circumstances at the time of construction, many houses are rather small, a notable cause of complaint among residents who moved there in the 1950Ts and who now have children. Census data shows that until the early 1960Ts the population had been predominantly Jewish, and the area lay on the edge of the path of Jewish migration north along the Bathurst Street axis. Two factors seem to have been at work in the late 1950fs and the early 1960Ts to alter the social composition of the area. One was the attraction of newer areas of Jewish settlement to the north and north-east. The other was the movement of Italians north from the original reception areas in the western part of the inner city.^ Murdie's (1969) analysis of 1961 census data shows that, while the demographic structure of the area is typical of an inner suburb, its peculiar ethnic mix gives it a certain distinctiveness, and means it cannot be considered representative of other areas in Toronto. The Sample Table 11.1 summarizes the demographic, socio-economic and ethnic characteristics of the sample of 39 households. Most households consist of "nuclear" families; there are no single persons apart from children and virtually no lodgers. Forty per cent of the households consist of four persons (parents and two children), and twenty-five per cent consist of couples with no children at home. A surprisingly high percentage of male heads of households are self-employed (19 per cent). The majority (67 per cent) have blue collar jobs, while professional, clerical and 2

It has been argued by some researchers that the decision-making process cannot be studied successfully after the decision has been taken, because of the tendency of respondents to rationalize and defend their decisions. 3

The housing stock of the area is described in a major planning report on the Spadina corridor. See Borough of North York (1968).

^Interviews reveal that Jewish families have lived in the area for an average of sixteen years; while the Italians, who now occupy many formerly Jewish dwellings have lived in the area for an average of seven years.

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TABLE 11.1 SUMMARY OF HOUSEHOLD CHARACTERISTICS Number of households

Household characteristics A. 1 2 3 4 5

Life cycle stage Young/middle aged couples (20-49). No children. Young couples (20-29). Children pre-school age. Middle aged couples (30-49). Children school/ pre-school age. Middle aged/older couples (40-59). Children school/post-school age. Older couples (over 50). No children.

Total responses B.

Socio-economic status

1 2

Low- in come ($4,000-6,000). Elementary school Middle- in come ($6,000-10,000). Elementary school High- in come (over $10,000). Elementary school High- in come (over $10,000). High school and/or college

3 4

Total responses C.

Ethnic origin*

1 2 3 4 5

Italian Jewish East European British Other

Total responses

Percentage

3 4

7.9 10.5

16

42.1

8 7

21.0 18.4

38

100.0

8

21.1

17 4

44.7 10.5

9

23.7

38

100.0

15 12 4 3 3

40.5 32.4 10.8 8.1 8.1

37

100.0

* Based on country of origin of parents, country of birth, mother tongue, mother tongue of parents and religion.

technical occupations only account for a small proportion (14 per cent) of households. The general level of education (in terms of years of schooling) is quite low, and household incomes tend to be on the lower side of the middle-income range. However, there is a significant proportion of households, largely Jewish, with both a high level of education and high incomes. As expected the sample is dominated by Italian or Jewish "ethnic" origin groups; 70 per cent of the householders are first generation immigrants . Most important for the objectives of this paper are life-style characteristics. Interviews show that the households have moved only once or twice since their formation and that for the majority of respondents the present home is the first they have ever owned. The majority of respondents make weekly visits to church or 171

synagogue, and have at least three groups of friends with whom visits are exchanged on at least a monthly basis. Most households show a strong orientation toward family, home and garden; only a few younger couples are in any way "outdoor" oriented. Conspicuous consumption, travel, cottages and boats, the use of downtown facilities and emphasis on careers are not characteristics of the study group. Household Preference Structure In this study "preference structure" refers to those characteristics of dwelling site and location which a household considers when searching for a new home. Dwelling and site are of interest since they have an implicit influence on location decisions. In general the preferences expressed by the respondents are very similar to the findings of other researchers studying households with the same characteristics. There are, however, some minor differences between the two major ethnic groups in this particular sample. The present dwelling seems to influence future design preferences: Bungalows are the most desired dwelling type except for those Jewish households occupying two-storey homes who prefer a new house of a similar type. All of the households preferring single-family homes want to increase the number of bedrooms by at least one, and every household wants larger rooms and more bathrooms. All respondents have definite ideas about dwelling type, design, size, and a firm desire for large lots and garages. Other features of house and site introduced into the discussion by prompted questions or spontaneous responses seem to be of minor importance in most cases. Most people feel that a basically sound, substantial building on a large lot is of primary importance, and they would only talk about "frills" once they have actually seen a particular dwelling. In terms of the quality of the neighbourhood a high standard of upkeep, cleanliness and spaciousness are most frequently mentioned as desirable. The "harmony with nature" syndrome— quietness, landscaping, trees and grass, fresh air—is extremely important. Peterson (1967) for example notes similar preferences. There is a marked rejection of "older, more crowded" inner city residential areas by all respondents. Although the money available from the sale of their present homes would have given them some freedom to move into "better" districts, most respondents clearly reject the idea of having "wealthier" neighbours, 172

and prefer neighbourhoods with people of comparable income and occupational status. Distance to place of work seems unimportant. Only 6 out of 27 respondents identify it as an important variable. Comparing search areas with place of work it is found that only two families would move closer to their workplace, while, on the average, households would move four miles further away. Accessibility to other facilities, particularly schools, shopping, and public transportation seem to be quite important. The local school is of greater concern to Italian than Jewish families since most of the latter are prepared to drive their children to private schools anyway. Accessibility to relatives and friends is also of some importance particularly for the Jewish respondents. The certainty and emphasis of response to certain questions indicates that the movers had very definite, and often quite inflexible, ideas about some features of the dwelling, neighbourhood and location. The number of bedrooms and the approximate cost of the dwelling are clearly defined. There are very clear images about the physical and, implicitly, the social characteristics of the neighbourhood; and there is a general dislike of the inner city (or "downtown"). All other features, whether related to dwelling, neighbourhood or location, are clearly perceived to be of secondary importance and their evaluation differs considerably according to life-cycle, socio-economic and ethnic characteristics of the households. Activity, Awareness, and Search Space The questionnaire results, although limited, provide a basis on which to generalize patterns of behaviour in residential choice. The determination of activity space (Figures 11.1, 11. 3 and 11. 6) is based on a number of questions related to the households' routine movements. These include location of work, schools, friends, recreational and cultural facilities and the frequency with which they are visited. These frequencies are standardized for time and then plotted; the maps for all households of a particular group are then overlaid and the average number of visits is computed for 1/4 square mile grid cells. Boundaries are drawn around cells of equal "contact" density with exceptions smoothed out to ensure contiguous activity spaces. The determination of awareness space is equally crude. A sample of 14 "neighbourhood" or district names is drawn from a large scale map of Metropolitan Toronto and respondents are 173

Figure 11.1 Activity points in the immediate area for each ethnic group asked a) which of the districts they have heard of and b) which they have visited, and then c) to describe the location of these districts, and d) to locate them on a base map. 5 Positive responses to questions a) and b) and correct responses to questions c) and d) score a point for each particular district. The points for each district are then expressed as a percentage of the sum of points for all districts. The maps (Figures 11.4 and 11.7) show aggregates for the two major ethnic groups in the study population. ^This elaborate test proved very taxing for most people and only eighteen useful responses are obtained.

174

175

Search space is determined by simply asking the respondents where they have looked or where they are considering looking for a new home. Initial responses vary from identifying street inter176

sections to naming entire boroughs. This led to some prompting by the interviewers to obtain more precise descriptions. Unfortunately these do not necessarily define actual search spaces. The areas shown on the maps (Figures 11.2 and 11.5) are very tentative and indicate only general locations. The Italian and Jewish activity spaces are markedly different. This is clearly illustrated by comparing the activity points (shops, friends, churches, synagogues, schools) of both groups in the immediate neighbourhood (Figure 11.1). The Italians show a pattern of contacts within, and to the west of, the study area and use the Eglinton/Dufferin and Lawrence/Dufferin area for shopping and religious activities. The Jews are almost entirely oriented towards Bathurst Street on which their synagogues and schools are located and where many of their friends live. Almost all Jewish respondents shop at one particular supermarket on Bathurst Street. This local pattern is projected into the metropolitan area as a whole with the Italians showing numerous contacts to the west and north-west, and the Jews being particularly concentrated on the Bathurst Street axis north of Eglinton Avenue. The Italians have stronger contacts downtown, but this is a reflection of the workplace of females rather than a basic difference in spatial orientation of all activities. Contacts east of Yonge Street are virtually non-existent for both groups. These basic orientations are broadly repeated in the patterns of awareness space. The Italians are more familiar with areas to the west and north-west while the Jews seem most aware of areas directly north. The maps of awareness space clearly illustrate the general ignorance of areas in Scarborough or Etobicoke, and greater knowledge of areas north of Highway 401. Despite these basic differences in orientation there are some similarities in the search spaces of the two ethnic groups (Figure 11.2 and 11.5). These are found first, in a small number of households searching the local area; secondly, in a general orientation west of the Yonge Street axis; and thirdly, in a general rejection of locations further toward the city centre. The immediate neighbourhood of the study area turns out to be particularly well searched. Other studies (Simmons 1968) have found that most moves take place within a short distance and this is taken to be a reflection of familiarity and satisfaction with the immediate neighbourhood. This seems to apply here. About one-half of the respondents stated they would prefer to remain in 177

the general area of their present home. The reasons for satisfaction seem to be the accessibility to transportation routes, and particularly the network of friends and relatives in the area. The Jews are the most anxious to remain. The intensity of social, religious and shopping contacts, especially along the Bathurst Street axis (Figure 11.1), undoubtedly goes far to explain this. Whereas the Jews want to move on the average four miles away from the present home, the Italians intend to move seven miles. Life cycle stage also has a strong effect on the distance of the move for people of all ethnic groups. Most elderly persons are looking for apartments and hope to find them in close proximity to their present homes. Social ties, immobility in the search process through the lack of a car, and a fear of not being able to cope with the search for new sets of contacts and families seem to be major factors in determining their search space. Apart from these similarities the Italian and Jewish search spaces are quite different. That of the Italians is broad in the north-western and western sectors of Metropolitan Toronto with the majority of households searching north of Highway 401. This reflects the Italian preference for residential locations on the edge of the built-up area where they hope to find relatively cheap housing, preferably bungalows, in the newer, more spacious, subdivisions. The directional bias of the Italian search space corresponds quite well to their widespread activity space in those same areas. There is no correspondence with activity patterns to the south of the study area because of the expressed aversion to the inner-city as a place to live. The Jewish search space is fairly restricted to the local area and northward along Bathurst Street, with the exception of a detached cluster in the Bayview district north of Highway 401. Again the search space corresponds very closely to the activity space with the same proviso that the southern part of the activity space, towards the downtown area, is rejected. Proximity to religious/cultural institutions, and to friends or relatives in the area is the dominant factor in determining the Jewish search space. The explanation for the secondary area of search in the Bayview/Highway 401 district can be traced to a small group of affluent, mobile Jews with young families who are mutual friends. They all have friends in the area and are impressed with the quality of the housing, the spaciousness of both house and lot, the "soundness of the investment," and the "prestige" location. 178

Implications The sample area occupies a unique place within the social geography of Toronto. Lying at the overlapping edge of the prominent Italian and Jewish sectors along the Spadina corridor, this area presents an excellent example of ethnic differences in the residential migration process. Jews and Italians seem to have migrated northward in close proximity in the last six or seven decades. Today, as a comparison of the 1951 and 1961 Census figures show (see Murdie 1969), these overlapping sectors seem to separate in the outer suburbs. The Italians move towards the northwest and the Jews towards the north-east. The findings of this study are in full agreement with these trends and the maps of search spaces give a very clear illustration of the stability of this movement pattern. The discussion of activity and awareness space shows the influence of these on the determination of the search space. The perpetuation and extension of these two "ethnic" sectors are a well-known phenomenon in Toronto, to which this detailed study provides further empirical evidence. The Jews, more than the Italians, form a close-knit community with strong social, cultural and economic ties, and are motivated to maintain these ties. They are also aware of the location of social contacts and Jewish institutions and take these into consideration in a relocation decision. Many Italian households, despite stated preferences to move away from predominantly Italian areas, follow the general pattern of migration towards the north-western corner of Metro Toronto. The activity and awareness space of the Italians is moulded by the spatial pattern of their community and tends to perpetuate spatial cohesion of the Italians whether they like it or not.

References

BOROUGH OF NORTH YORK, DEPARTMENT OF PLANNING AND DEVELOPMENT. 1968. Spadina corridor redevelopment study. North York: DPD. BROWN, L. A. and MOORE, E. G. 1970. "The intra-urban migration process: a perspective." Geografiska Annaler Ser. B, Human Geography, 52(B) 1: 1-13. LANSING, J. B.; MUELLER, E. and EARTH, N. 1964. Residential location and urban 179

mobility. Ann Arbor: University of Michigan. Survey Research Center, Institute for Social Research. MURDIE, R. A. 1969. "Factorial ecology of Metropolitan Toronto 1951-1961." Research Paper No. 1J6. Chicago: University of Chicago, Dept. of Geography. PETERSON, G. L. 1967. "A model of preference: quantitative analysis of the perception of the visual appearance of residential neighbourhoods." Journal of Regional Science 7: 19-31. ROSSI, P. 1955. Why families move; a study in the social psychology of urban residential mobility. Glencoe, 111.: The Free Press. SIMMONS, J. W. 1968. "Changing residence in the city: a review of intra-urban mobility." The Geographical Review 58, 4: 622-51. WOLPERT, J. 1965. "Behavioural aspects of the decision to migrate." Papers and Proceedings of the Regional Science Association 15: 159-68.

12

Discretionary and nondiscretionary aspects of activity and social contact in residential selection* W. Michel son

In many parts of North America, one must understand more than land values, rental costs, location of work place, size of family, and administrative procedures in order to know why *An earlier draft of this paper was presented to the meeting of the Research Group in Time-Budgets and Social Activities, European Coordination Centre for Research and Documentation in Social Sciences, May 23-26, 1972, Brussels, Belgium. The research was conducted under contract to the Central Mortgage and Housing Corporation and under a grant from the Canada Council. Considerable research assistance for this paper was contributed by David Belgue, John Stewart and Anna-Rose Spina. Computer programming was done by Chris Cotterell and Les Cseh with the support of the Ministry of State for Urban Affairs. Secretarial services arranged by Eleanor Little and Muriel French are appreciated.

180

families change residence, what they demand, and why certain developmental patterns are popular while others are not. Some recent writers suggest that people rationally assess their own characteristics and preferred activities; they then choose a place to live which is consistent with satisfaction of these perceptions (Bell 1968; Gans 1967). They call this phenomenon self-select ion because people in effect select themselves for their future residential settings. It is a new version of the Tt birds of a feather flock together" ecological phenomenon, differing from the old in terms of an emphasis on rational, voluntary, social criteria for selection, rather than on economic determinism. This study seeks at first to assess the amount of selfselection involved in residential choice, as well as the substantive basis for self-selection in certain environmental contexts. We are interested particularly in analyzing the differences in the preferred activities between those choosing to live in single family houses and high rise apartments and between those in downtown locations and far suburban locations. Furthermore, the study of self-selection partially reported here is the first part of a larger research context. We are conducting a longitudinal study of the social aspects of housing to discover the extent that people's expectations are realized under real life conditions. We are exploring the extent to which social implications accompany differential housing types and locations, how many of these are explained by self-selective processes, and what behavioural changes, on the other hand, are situation specific. Methodology Since this study is designed for specific substantive purposes, it differs from time-budget studies which seek to describe the time usage of a population. Hence, some mention of methodology is not only desirable but essential. We drew the closest we could to a 100 per cent sample of people (meeting social criteria discussed shortly) who were about to move to the four cross-classificatory categories whose various implications we wished to study: downtown high rise apartments, downtown single family homes, suburban high rise apartments, and suburban single family homes. 1 We received respondent names from those in the housing industry who finalize Locational definitions were relatively precise in terms of distance and travel times, leaving unstudied an ambiguous "middle" belt of territory separating downtown from suburbs.

181

sales and leases—a large network of brokers, rental agents, and developers. While all the new residences are in the Greater Toronto area, a fruitful one for study because of the variety of housing types at various locations, as well as an absence of major social problems stigmatizing any general type of location (e.g. , downtown), the respondents could and did live anywhere before their moves. Since this was intended as a natural experiment with differential environmental stimuli, we set criteria to make the sample as homogeneous as possible. Since there is great concern for family housing in Canada, we concentrated on married couples within the childbearing years, excluding those sharing their first home. So as not to exclude a variable central to residential settings, we sampled within each environmental category a varying percentage of families with and without children. However, since self-selection implies some choice, we sought a stratum of society more likely to exhibit freedom in their choice of housing and location; we therefore selected only families moving to dwelling units with reasonably high (although not exorbitant) monthly payments—apartments renting at about $200 a month or more and houses selling for $35,000 and over (but in no case more than $99, 000). The first interview with each family was to be held immediately after a respondent family signed an agreement to buy or a lease to rent a dwelling unit, but in any case before they moved to their new home. Within a family, interviews were to be held with the wife, the husband, and one child, if present, between the age of 10 and 17. Of the 989 eligible families found and approached, 77 per cent consented to interviews, with the remainder consisting of those who refused and those where language problems existed. The final sample contained 761 families, with the wife as our principal respondent (necessary for retention of the family) and with about 70 per cent of husbands and 80 per cent of eligible children. The families were divided into categories as follows: downtown high rise (14.3 per cent), downtown single family (12.4 per cent), suburban high rise (37. 6 per cent), and suburban single family (35. 8 per cent). Briefly, we collected four types of data during the first interviews:2 2

Interviewing and coding were sub-contracted to York University Survey Research Centre.

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1) We gathered traditional descriptive information about the family, including some aspects of residential history. 2) We explored various direct questions as to why people wished to move away from their existing homes and how and why they chose their new homes. We also asked what they expected in these new surroundings. 3) Time budgets were used to assess aspects of the life styles of respondents, to indicate such phenomena as might be relevant to the self-selection process such as differential use of both work and leisure time, the social context of daily life, and locational considerations. These three phenomena were to be viewed individually and in concert, with time as a quantitative metric. We took "yesterday" interviews for weekday activity, using fifteen minutes as the minimum base; that is, we recorded all activities which either lasted fifteen minutes or more or else occupied the majority portion of a fifteen minute period. We further collected time-budget data for the previous Sunday, but with one hour as the basic unit. This type of datum is the main one analyzed in this particular paper. 4) Since the study had a particular focus, we asked supplementary questions on some of our major emphases also covered less thoroughly by time-budget information. These included information on interactive patterns, preferred pastimes, and organizational behaviour. This offered the opportunity to externally validate the time-budget data, as well as go beyond it, Hypotheses We expected variation in activity by environmental setting along three lines: 1) active versus passive; 2) in-house versus out-ofhouse; and, 3) solitary versus social. 1) Active versus passive Since high rise apartments more characteristically introduce constraints on their residents due to noise transmission by neighbours, lack of space and lack of specialized intra-unit recreation facilities, and regulation (e.g. , only the management may make structural alterations), we would expect more active in-house behaviour to characterize those moving to houses and more passive forms of behaviour to characterize those moving to apartments. 2) In-house versus out-of-house Given the above, we should expect that those moving to apartments will spend more time out of their dwelling units. This would reflect the known constraints

183

of the physical structure. Furthermore, we should expect that those moving to downtown locations are more concerned with the concentration of leisure attractions in that setting and hence spend more time away from home. Therefore, we should expect high rise as a housing type and downtown as a location type to be related positively to time spent out-of-house, in additive fashion. 3) Solitary versus social Literature and mythology both suggest that high rise dwellers are isolated from their neighbours (excepting young single persons), even though in physical proximity to many of them. Furthermore, suburbs are said to be the centre of social participation (formal and informal), with various studies providing evidence to this effect (Tomeh, ed. 1969; Fava 1956). Therefore, we should expect that a greater devotion of time to activities in the company of others should be positively related to choice of single house and suburban location, in additive fashion. These expectations represent an expansion in detail over some advanced by Wendell Bell. Bell (1968) put forward n familismlr (an emphasis on childraising and family life) as a major reason for choice of suburban locations. Tt Careerism TT (an emphasis on one's job) and TTconsumerism!T (an emphasis on consumption) were thought more characteristic of those choosing centre-city locations. What Bell did not indicate was the place of housing types, if any, in his formulation and the related differentiation between the implications of housing type and location with respect to self-selection. This does not, however, disqualify Bell T s ideas from further examination. Analytic Operations We coded our time-budgets using the activity codes developed in the Multi-National Time-Budgets Study, although some revisions were made in coding childrenTs activities—not reported here (see American Behavioural Scientist [1966]). 3 Each activity was represented by a field on a punched card containing time commenced, the activity code, a persons present code, a location code, and a simultaneous activity code. Hence, duration of activities was calculated with reference to the time when the next activity commenced. This was satisfactory in every case except nightime sleep, which had no termination time; in calculation, an arbitrary 6 a.m. was used as the end of this activity. Since, however, the day reported began either at 6 a.m. or whatever later time the respondent woke up, usually the latter, the days reported are unfortunately not a uniform 24 hours. Furthermore, if a person was outside the Greater Toronto area for part of the day, only that time inside the area was counted, further shortening the length of the day. While this procedure is satisfactory for our theoretical investigations, it obviously leaves something to be desired for those seeking a full statement of time usage from our data.

184

Tables were run in the form of activity category by home environmental category, with the dependent variable being average number of minutes devoted to the given activity by those in the environmental category. In addition, given the lack of a uniform length of day, some standardization was added by recalculating each average time value as a percentage of the daily minutes reported by people in the environmental category. The particular perspective of our study added specific analytic demands, however. We are studying time differences of groups within a given culture. Even more than between cultures, differences in time usage are more a matter of emphasis than of kind. Furthermore, the kinds of activity which we must investigate for the hypothesized types of variation are those representing relatively small amounts of time (and even smaller percentages of daily time); variation in these figures by environmental category should be small indeed. Nonetheless, variations in life style by environment should be demonstrated by small but regular differences in averages of expenditure of marginal uses of time by housing type or location and/or the differential distribution of individuals showing extreme patterns in uses of marginal time. While none of these residential settings contains or attracts people so widely different in distribution of their daily time as to create statistical profiles that can be said to be confidently unmistakable, differences in emphasis are nonetheless important, given the constraints upon activity. Furthermore, small quantitative differences in behaviour may be accompanied by large differences in with whom and where it takes place by categories of environment. Hence, it was desirable to have a measure which had a standard range of values, of relatively equal size. This would overcome some of the analytic problems encountered with activities taking varying amounts of time, some of them representing extremely small percentages of the daily total. In this regard, we generated an analytic measure of TTactivity emphasis." This presents the percentage of persons in an analytic cell who are above one standard deviation of the mean time devoted to an activity by the whole sample (i. e. , the row, across all cells). fci other words, this illustrates the extent that persons who are relatively high in use of any category of time (i.e., about the top 16 per cent or less) make up a disproportionately high percentage of the respondents representing one or another category of environmental choice. Theoretically, values of our 185

measure of activity emphasis can range from 0-100, although the range shown is 0-50, with the typical range 0-20—independent of the amount of time involved. That this measures the distribution of heavy users of time for particular activities remedies a second problem. Given that activity categories are the least bit sensitive, there will be some persons who will be performing the activity and others who will not. Similarly, there will be variation in the amount of time devoted to the activity. What this implies is that mean amounts of time will conceal greater variations within the sample. Two people, one of whom is unemployed and another who is fully employed, will create a mean impression of half-time employment. 4 This point is underlined by the fact that for most activity categories, our mean time figure was lower than the standard deviation. Hence, a measure that shows the proportion of a subcategory containing the extreme cases from the population helps escape from a definite predicament. This paper is based on a series of tables on activity which utilizes both mean time and "activity emphasis^ measures. In operation, the two measures are extremely consistent. However, the measure of "activity emphasis ?? shows most findings both more clearly and more dramatically. Further, since the analytic perspective is on self-selection with respect to housing and location, with the possibility of additive and interactive relations between the two, three sets of tables were run, using three different categorizations of environment: 1) destination environment (housing type and location; 2) change in housing type; and, 3) change in location type. In addition to analyses of activities, similar procedures were followed using time budget data for analyses of persons seen and locations utilized for daily activity. Findings Since the approach is one which utilizes differences in emphasis rather than kind, and since, moreover, the data to answer the questions at hand are spread over three sets of tables, the presentation of findings is interpretive in character. Let me first describe the characteristic differences in the weekdays and Sundays of wives and husbands with respect to ^The opposite approach, considering averages which only include those who performed the activity, are completely inappropriate for measuring group activity emphasis.

186

TABLE 12.1 WEEKDAY TIME BUDGET OF WIVES FOR EACH DESTINATION ENVIRONMENT BY SELECTED ACTIVITIES

Work

Activity At home Watching Reading Teleeating television books phoning

Housework Children

N

Apartment average downtown minimum 266(19.5)* 8.3 AEQ**

28(2.1) 3.7

8(0.6) 0.9

65(4.8) 5.6

44(3.3) 3.7

13(1.0) 1.9

10(0.8) 0

108

average minimum AEQ

87 ( 6.5) 2.3

61(4.,5) 6,.4

34(2.5) 4.3

94(7., 0 ) 17., 0

31(2.3) 2.1

17(1.3) 2.1

38(2.8) 7.5

94

Apartment average minimum 146(10,7) suburbs AEQ 3 9

69(5, .0) 11,.6

30(2.2) 4.2

81(5. ,9) 10., 9

66(4.8) 9.2

5(0.4) 0

20(1.5) 4.9

284

House downtown

House suburbs

average minimum AEQ

81( 6.0) 2.9

81(6.0) 13,.5

24(1.8) 3.4

85(6.,3) 13,.0

60(4.4) 4.8

5(0.4) 0.5

27(2.0) 6.3

208

House resale suburbs

average minimum AEQ

66(4.9) 0

65(4,.8) 6,.5

47(3.5) 6.5

87(6,•4) 3,.2

51(3.8) 8.1

9(0.7) 0

33(2.5) 6.5

62

* figures in parentheses in this table and tables to follow represent per cent to total daily time. ** percentage of respondents in each destination environment cell who are one standard deviation or more higher than the mean for the whole sample with respect to a specific activity. The Activity Emphasis Quotient will hereafter be abbreviated to AEQ.

physical environment. Then, the general issues must be examined in light of the individual findings. 1) Weekdays and Sundays a) Wife Weekday As Table 12.1 indicates, amount of time at work varies positively with both apartment residence and downtown location in an additive fashion. Those in or going to downtown high rises are doubly high in work time. Housework and child care are low only among those moving to downtown high rise. There is thus a strong negative correlation between these functions which is related to the environment chosen. Time spent eating meals varies in the same fashion with housework and childcare. As might be expected, these findings are clearly explained with reference to family structure and work status. The relevant tables have been analyzed in the draft of the overall study, Phase I, but are not reported here. In our sample, there is a strong association between choice of downtown high rise and the family having a working wife with no children. In contrast, there are no differences in the daily time schedule in shopping, personal hygiene, educational and organizational activity (of which there is little), social interaction, and active recreation. 187

With respect to passive leisure, there are differences in emphasis in TV watching (suburban movers high), reading books (downtown high, consistent with professed interests), and telephoning (home owners high). These are not related to familywork status. One should note carefully the beginning of a pattern of differences in "obligatory time" centering faround downtown high rise and the family-work status as distinct from differences in use of discretionary time involving choice of future housing and location more generally (i.e., not the "downtown high rise package") but not involving differences in family-work status for the wife. In terms of persons present, as Table 12.2 suggests, the future high rise dweller is alone more, and this is related to neither family-work status nor location. Consistent with familywork status, women moving to the downtown high rise are high in time spent only with spouse and with work colleagues, while they are lower in time spent with children and with friends other than work colleagues on the weekday. With respect to location, wives moving to downtown high rise apartments understandably spend less time at home and more at work. Consistent with our hypotheses, people moving to high rise spend more time meeting people in the other person T s home, and those moving downtown spend more time at public establishments. Table 12.3 presents these findings. While not exclusively determinative of the match between activity and home environment, the wife T s work—family status, which is so strongly associated with residential choice, is tied to great differences in what kinds of people are met during the day and where they are met. b) Wife Sunday Sundays demonstrate fewer influences of work, although the influence of family cycle remains. Table 12.4 presents the relevant data. There is less difference in terms of work or housework than on weekdays. The latter item requires some passing mention. It is clear that an equal total amount of housework is not devoted by wives in all categories. Those working and without children do less, but what they do not do on weekdays is transferred to Sundays, leading to an amount almost equal to that reported on that day by the female heads of larger families in larger quarters. Wash in particular is postponed to Sundays. The necessity of considering the week as an entity is important in other contexts as well. 188

TABLE 12.3 AVERAGE MINUTES, PER CENT OF TOTAL TIME FOR WIVES AND ACTIVITY EMPHASIS QUOTIENT REGARDING LOCATION OF DAILY ACTIVITIES BY DESTINATION ENVIRONMENT (weekday)

Location

House Apartment Apartment downtown in suburbs downtown average per cent average per cent average per cent time AEQ minimum time AEQ minimum time AEQ minimum N=284 N=94 NrlOS 779 5 10 314 26 88 5 88 8

58.9

1323

100.2

At home Around the home In neighbourhood Place of work Another's home Business or public places Streets, parks In transit Other Total

0.4 0.8

23.7

2.0 6.7 0.4 6.7 0.6

5.6 0 1.9 3.7 0.9

13.9 0

11.1 1.9

1007

75.9

18.1

20 18 110 29 70 10 56 7

1.5 1.4 8.3 2.2 5.3 0.8 4.2 0.5

1.1 5.3 2.1 2.1

1327

100.1

10.6

1.1 6.4 1.1

House in suburbs average per cent minimum time AEQ N=268

Resale house!3 in suburbs average per cent minimum time AEQ N=62

969 27 15 178 39 49 7 62 7

71.6

20.1

1051

78.4

24. 0

1024

78.6

2.0 1.1

4.2 3.2 0.7 5.6 3.9 1.1 8.8 0.7

28 7 98 17 67 6 60 7

2.1 0.5 7.3 1.3 5.0 0.4 4.5 0.5

3.4 1.0 2.4 0.5 8.2 0.5 7.2 0.5

30 15 68 34 61 16 48 7

2.3 1.2 5.2 2.6 4.7 1.2 3.5 0.5

1353

100.0

1341

100.0

1303

99.8

13.2

2.9 3.6 0.5 4.6 0.5

27.4 3.2 3.2 3.2 3.2 8.1 1.6 6.5 1.6

TABLE 12.2 AVERAGE MINUTES, PER CENT OF TOTAL TIME FOR WIVES AND ACTIVITY EMPHASIS QUOTIENT REGARDING PERSONS PRESENT DURING DAILY ACTIVITIES (weekday) DESTINATION ENVIRONMENT

Persons present All alone Alone in a crowd With spouse only With spouse and children With children only With other household adults With non-household relatives With friends or neighbours With work associates Total

Apartment House downtown downtown avera ge per cent average per cent minimum time AEQ minim um time AEQ N=94 N= 108 352 7 424 8 40 5 43 37 173

32.3 0.6 38.9 0.7 3.7 0.5 3.9 3.4 15.9

1089

100.0

13.0 0.9 30.6 0 2.8 0 3.7 2.8 14.8

352 7 264 58 207 6 27 68 64

33.4 0.7 25.1 5.5 19.7 0.6 2.6 6.5 6.1

1053

100.0

11.7 3.2 17.0 6.4 16.0 2.1 1.1 5.3 6.4

Apartment in suburbs average per cenl AEQ minimum time N = 284 377 2 291 70 171 4 31 52 93

34.6 0.2 26.7 6.4 15.7 0.4 2.8 4.8 8.5

1091

100.0

16.9 0.7 18.7 6.3 12.3 0.4 2.8 5.3 7.0

House in suburbs average per cent AEQ minimum time N=208 296 1 246 87 206 3 41 45 48

30.4 0. 1 25.3 8.9 21.2 0.3 4.2 4.6 4/9

973

100. 0

8. 7 0 13.9 7.2 16.4 0.5 1.9 3.9 3.9

Resa le house;3 in suburbs average per cent AEQ minimum time IM=62 338 3 282 109 234 1 29 100 31

1127

30.0 0.3 25.0 9.7

20.8 0. 1 2.6 8.9

8.9

100.0

17.7 0

12.9 12.9 17.7

0 1.6

12.9

1.6

TABLE 12.4 SUNDAY TIME BUDGET OF WIVES FOR EACH DESTINATION ENVIRONMENT BY SELECTED ACTIVITIES

Apartment average downtown minimum AEQ

House downtown

average minimum AEQ

Apartment average minimum suburbs AEQ

House suburbs

average minimum AEQ

House resale suburbs

average minimum AEQ

Housework

Childcare babies

26(2. 2)

13(1.0)

1. 9

2.8

28(2. 2)

25(2.0)

3. 3

42(3.3) 4. 0

4.4

23(1.8) 3.3

Childcare older Personal Social Book children hygiene Church visiting Excurs'^ns reading

2(0..2)

26(2.,0) 5.,5

28(2,.1)

14(1.1)

29(2,.2)

3. 5

1.5

2.0

35(2..7)

39(3.0)

36(2,.8)

3.2

9.4

7(0.6) 41(3.3) 0.9

1.9

37(3.0) 5.7

5(0.4)

25(2.0)

11(0.8)

2.2

0

57(4.4) 11(0.8) 66(5.2)

30(2.4)

5.1

1.8

4.7

66(5.2) 24(1.9) 67(5.3) 9.5

40(3.1)

7..9

1.6

2.5

5.0

10(0.8) 55(4.3) 0

3.2

2.2

3(0.2)

91

275

6.9

26(2.0)

2(0.2)

1.0

3.5

35(2.7)

7(0.5)

4.8

106

4.7

52(4.1) 19(1.5) 71(5.6) 3.3 3.3 7. 7

4..0

39(3..0)

4. 8

81(6.6)

0.,9

N

201

63

3.2

Childcare is higher in the groups other than those moving to downtown high rises for obvious reasons. Time on personal hygiene, however, is much greater for this latter group. Those moving only from one home to another--i.e. , who are not in an apartment or are going to one—devote time to church, although this is related to family cycle. Social visiting is lower for those moving to downtown apartments than for all the other groups, but this can not be pinned on family cycle status. Two aspects of leisure are related to the current housing type: those in apartments now, particularly those with children, are high in taking excursions away from home (for an average of 41.4 minutes per day compared with 23.2 minutes for other groups) — consistent with our expectations—while those living in houses, regardless of family size, are more likely to spend time reading, 13.4 minutes versus 9.0 for other groups. 5 In terms of the persons present (as indicated in Table 12.5), Sundays are like weekdays in terms of who is with spouse only (downtown apartment movers), and with spouse and children (not downtown apartment movers). Suburban apartment movers spend larger amounts of time with relatives, while people moving to downtown apartments make up the time not used in visiting on weekdays on seeing their friends on Sunday. c) Husband Weekday HusbandsT weekdays do not follow the relaThis data is available in our draft document of Phase I of the overall study but the relevant tables are not provided here.

190

TABLE 12.5 AVERAGE MINUTES, PER CENT OF TOTAL TIME FOR WIVES BY DESTINATION ENVIRONMENT (Sunday) DESTINATION ENVIRONMENT

Persons present All alone Alone in a crowd With spouse only With spouse and children With children only With other household adults With non-household relatives With friends or neighbours With work associates Total

Apartment House downtown downtown average per cent average per cent minimum time AEQ minimum time AEQ N-108 N-94 154 2 637 54 22 4 53 100 6

14.9 0.2 61.7 5.2 2.1 0.4 5.1 9.7 0.6

1032

99.8

8.5 0.9 32.1 1.9 0.9 0 1.9 8.5 0.9

152 1 422 262 101 6 56 106 4 1110

13.7 0.1 28.0 23.6 9.1 0.5 5.0 9.5 0.4 99.9

5.5 0 15.4 8.8 6.6 0 3.3 6.6 0

Apartment in suburbs average per cent minimum " time AEQ Nz284 160 1 430 308 65 6 78 79 7

14.1 0.1 37.9 27.2 5.7 0.5 6.9 7.0 0.6

1134

100.0

.10.9 0 16.0 8.7 4.7 0.7 4.4 4.4 0.7

Houses in suburbs average per cent minimum time AEQ N-208 121 0 303 395 75 8 53 71 6

11.7 0 29.4 38.3 7.3 0.8 5.1 6.9 0.6

1032

100.1

6.0 0 5.0 12.9 5.0 0.5 3.5 3.5 0.5

Resale houses in suburbs average per cent minimum time AEQ N=62 338 3 282 109 234 1 29 100 31

30.0 0.3 25.0 9.7 20.8 o.l 2.6 8.9 2.8

1127

100.2

12.7 0 7.9 17.5 4.8 0 4.8 4.8 1.6

TABLE 12.6 WEEKDAY TIME BUDGET OF HUSBANDS FOR EACH DESTINATION ENVIRONMENT BY SELECTED ACTIVITIES

Work Apartment average downtown minimum 321(23. 7) AEQ

House downtown

3., 6

average minimum 361(26.,0) AEQ

10.,2

Apartment average minimum 376(27.2) suburbs AEQ

House suburbs

8(0.6) 2.4

2(0.1) 0

6(0.4)

10.,3

1.0

average minimum 391(28. 1)

2(0.1)

7., 8

0

average minimum 380(27. 6)

1(0.1)

AEQ

House resale suburbs

Activity Personal Social Eating Watching Book Housework hygiene visiting at home television reading

AEQ

7., 0

0

43(3.2) 22(1.6) 55(4.0)

65(4.,8)

2.4

7. 2

51(3.7) 31(2.3) 89(6.4)

4.8

4.8

9(0.7)

41(3.0)

7(0.5)

5. 1

3.4

46(3.3) 15(1.1) 64(4.7)

72(5.,2)

7(0.5)

4.6

9., 2

1.5

43(3. 1) 11(0.8) 62(4.5)

57(4..1)

1(0.1)

6.,3

0

59(4..3)

3(0.2)

4.1

3.5

5.1

1.0

0.7

5.6

47(3.4) 17(1.2) 69(5.0) 2.3

0

7.0

83

1.2

8.5

6.8

N

4., 7

59

195

142

43

0

tively simple explanatory pattern of their wives1. Contrary to what one would expect from Bell (1968) on ?T careerism ? t? men moving downtown spend less time on job activities than do the suburbanites, but this may be compensated for by more time spent at home in preparation. Indeed, downtown location may be preferred by those emphasizing work because of less spatial (and possibly behavioural) differentiation between home and work. Table 12.6 illustrates these weekday differences. There is little difference in the amount of time spent on housework (including gardening) but it clearly follows the demands imposed by current housing: those in houses spend an average of 11. 7 minutes per day compared with 6. 7 minutes for those in other housing types. 6 Those moving downtown but not necessarily to high rise apartments spend more time visiting with others at the home of one or the other. Men in houses now spend more time eating (75.4 versus 60. 8 minutes) while those in apartments watch more television (66.7 versus 51.0 minutes). In terms of whom men see during the day, they vary as do their wives by the downtown high rise package in terms of time 6 Again, this data is taken from the overall Phase I report and the tables are not included here.

192

TABLE 12.7 AVERAGE MINUTES, PER CENT OF TOTAL TIME FOR HUSBANDS BY DESTINATION ENVIRONMENT (Weekday) DESTINATION ENVIRONMENT

Persons present All alone Alone in a crowd With spouse only With spouse and children With children only With other household adults With non-household relatives With friends or neighbours With work associates Total

Apartment House Apartment downtown downtown in suburbs average per cent average per cent average per cent minimum time AEQ minimum time AEQ minimum time AEQ N=108 N=94 N=284 327 13 406 9 5 0 23 40 213

31.6 1.3 39.2 0.9 0.5 0 2.2 3.9 20.6

1036

100.0

13.3 2.4 32.5 0 0 0 3.6 4.8 16.9

397 9 301 50 56 0 4 49 213 1079

36.8 0.8 27.9 4.6 5.2 0 0.4 4.5 19.7 100.0

25.4 0 22.0 3.4 8.5 0 0 6.8 13.6

301 0 291 87 22 1 16 43 216 977

30.8 12.8 0 0 29.8 15.9 8.9 7.7 2.3 1.5 0 , 1 0 1.6 1.0 4.4 5.6 22.1 18.5 100.0

Houses in Resale houses suburbs in suburbs average per cent average per cent minimum time AEQ minimum time AEQ N=208 N=62 230 0 240 87 20 1 9 39 178 804

28.6 10.6 0 0 29.9 13.4 10.8 7.8 2.5 1.4 0 . 1 0 1.1 0 4.9 3.5 22.1 12.0 100.0

269 16 225 110 59 1 8 16 42 226

33.5 2.0 28.0 13.7 7.3 2.2 2.0 5.2 28.1

804

100.0

11.6 2.3 7.0 11.6 9.3 2.3 2.3 4.7 18.6

with spouse or with children. Suburbanites spend more time alone, probably driving, while apartment dwellers are with work colleagues more. These data are presented in Table 12. 7. There are no clear patterns of place. This in any case leaves no strong evidence that apartment dwelling men are out of their dwelling more frequently than are home dwellers. d) Husband Sunday On Sundays, those moving to downtown high rises are low in time spent at work as Table 12. 8 suggests. The others may have to work to pay off houses or to compensate for a wife less likely to be working. On the other hand, it may be a function of their different occupations; those moving downtown are more likely to be professionals, as opposed to managers—the difference being in educational level, not salary. Housework and homework follows the daily pattern as does time spent eating. Current apartment dwellers spend more time on personal hygiene (66.8 versus 58. 0 minutes), while their house counterparts go to church more (11.4 versus 8.6 minutes). 7 While downtown movers spend more time visiting on weekdays (perhaps in lieu of commuting), their suburban counterparts spend more time in that activity on Sundays, again showing the need for taking the perspective of the full week as a system. With respect to leisure, men in and particularly moving to high rises from some other type of housing are higher both in sports participation and television watching, unlike what they reported in describing their pastimes. There are. again, no consistent patterns about place or combinations of person and place. This apparent miscellany of facts (or discrete interpretations'.) conveys a number of messages when put back into context. At least eight points may be observed in the foregoing. 1) Where a family or its members spend their time is highly dependent on the family structure and (in our case) consequent work status of the wife. If family-work status is related to environmental choice, so will the 6places where people in particular environments spend their time. ^Data from overall Phase I study. 8 Data other than the time budgets showed that the choice of downtown high rise housing is very much related to lowering the duration of the work trip for the husband and particularly, the wife when they do not have children. When they do have children, selection of location is made much mor? on the basis of appropriate neighbourhood quality for the children, even if the wife _«cs work and is thereby inconvenienced.

194

TABLE 12.8 SUNDAY TIME BUDGET OF HUSBANDS FOR EACH DESTINATION ENVIRONMENT BY SELECTED ACTIVITIES

Work Apartment average downtown minimum AEQ

House downtown

average minimum AEQ

Apartment average minimum suburbs AEQ

House suburbs

average minimum AEQ

House resale suburbs

average minimum AEQ

Activity Personal Social Eating Watching Housework hygiene Church visiting at home Sports television

N

5(0.4) 46(3. 8) 113( 9. 3) 13(1.1) 0 0 10. 0 0

102(8.4) 12.5

80

61(4.8) 12(0.9) 38(3. 0) 130(10. 2) 36(2. 8) 3. 5 1,8 7.0 1.8 12. 3

76(6.0)

57

10(0.8)

6(0.5)

70(5.8)

1.3

2.5

3.8

14(1.1)

6(0.5)

0

1.8

31(2.5)

8(0.6)

60(4.8)

2.1

1.1

6.4

15(1.2)

12(0.9)

1.5

1.5

34(2.7)

3(0.2)

2.3

0

5.3

5(0.4) 69(5. 5) 123( 9.•8) 30(2.4) 103(8.2) 4. 3 2.7 0 12. 2 9.0

188

64(5.1) 17(1.3) 65(5. 2) 127(10. 0) 15(1.2) 6.6 5. 2 0.7 12. 2 2.2

89(7.1)

136

43(3.4)

9(0.7)

89(7.1)

0

4.7

4.7

3(0.2) 75(6. 0) 118( 9. 4) 18. 6 2. 3 0

7.4

43

2) What people do (i.e., activity) is not as uniformly affected by family-work status as is where people spend time. A number of important activities taking large amounts of time seem to be, but choice of activity toward the discretionary end of the continuum is not so affected. 3) There is a complex of variables centering on choice of downtown high rise apartments which includes no children, a working wife, being out of the dwelling unit, seeing noncolleagues on weekends only, and housework done to a lesser extent, particularly on weekdays. In terms of social contact, it is different in kind but not amount. 4) Differences in amount of discretionary time are related not to such a package but to discrete choices of either housing type or location, but self-selection is involved as in number 3.^ 5) There are not widespread differences in terms of active Q

This is supported by other data collected on the professed pastimes of the respondents. Some pastimes vary by housing type chosen, in definite self-selective patterns. Those moving to houses, unlike those moving to high rises (who had no pronounced interests) are more avid church-goers, card players, gardeners, sewers and sports participants. Other pastimes vary by location, with those moving downtown higher in attending plays and movies, while suburban movers (additively to home dwellers) like gardening. The point is that these discretionary activities are not related to an interactive package of environmental choice. Furthermore, with the exception of church going, they are not in any way related to family-work status. The two sets of data are mutually supportive.

195

versus passive activity among house and high rise male residents, as hypothesized by previous writers. There are some differences as a function of current residence in terms of house repair and gardening; in fact, men moving from houses to apartments show disproportionately high activity emphasis values on these activities. But the pattern is not more general than this. There is no variation by housing type in the category "nothing" and the categories of active and passive leisure vary only with respect to higher television viewing in apartments, which is as expected. 6) The relation of housing type of solitary versus social existence is knotty, as other data on neighbour traits and interaction indicate. 1® Wives moving to high rises in both locations are alone somewhat more. Those moving to downtown high rises also spend more time with just their spouses because they are less likely to have children; demographically, this situation provides for less interaction than one where children are present. 7) The locus of activity as in-house or away from the dwelling unit has partly to do with whether or not the wife works but not exclusively, both for present and future housing. SundaysT locations and husbands1 locations for activity are not so tightly bound to the wife T s job but nonetheless, vary by housing type in the direction hypothesized. 8) The week must be seen as a total system to evaluate activity at any one point during the week. For example, the rhythm of housework varies according to environment; differences observed either on weekdays or Sundays alone would be misleading. Some forms of activity such as visiting, which may be encouraged or discouraged during the week by commuting associated with location, may vary in intensity at different times according to location. In sum, the time budgets present clear evidence of selfselection involving activities, including considerations of people and places. While some of it is tied closely to the wife r s workfamily status, in connection with downtown high rise, other aspects based on more discretionary activities are related more selectively to varying combinations of housing type and location. Our other data make clear that while high rise residents make friends through other contexts, usually communities of interest (e.g., school, work, mutual friends), rather than in their local neighbourhoods (not only with but by means of children), adding up to as intense a social life as all other respondents, the high rise residents may well associate their residences with non-interaction and hence solitude in their own minds. This is certainly the perception they report to us when describing their neighbours.

196

The types of persons with whom our respondents participate in activity are more highly related to family-work structures than is the substance and amount of activity. Anticipated Changes in Time Usage and Life Style The above analysis follows the reasoning inherent in the theory of self-selection that people exhibit through their current behaviour those emphases which they seek to maximize in their postmove environment. We should then expect even greater differences in time usage in later phases of this study, reflecting people's greater ability to satisfy these demands. However, some differences in model activity emphases for different prospective time settings may be blocked by the current setting. If they are fully realized in the current setting, there would be no need for the future setting. Therefore, we asked respondents open ended questions about what changes they expect in their daily and Sunday time schedules. At another point during-the interviews, we asked how they expect their lives to change (in general). These questions are answered quite similarly. Wives reply to the effect that they expect more housework, unless they are moving to downtown high rise, but particularly in single houses and in suburbia. Those moving to apartments anticipate more participation in sports. Those moving downtown expect easier commuting to work. Husbands agree on sports, but also mention changes in commuting time regardless of the location of their move. Since they all work, their commuting times are likely to be affected in one direction or another. As observed in other data, these commuting times go in various directions, according to their location change. Weekend life parallels daily life for the wives, with the exception of the work trip. Additional considerations for the weekend are added gardening for those moving to houses and augmented home entertaining for all but those moving from homes to apartments (consistent with the general pessimistic outlook of this group). Men T s expectations parallel those of their wives in terms of gardening, sports, and home entertaining—although the greatest expectations about increases in the last-mentioned appear in men mbving to homes. In contrast, those moving from homes to apartments are high on expectations of "going out" more. As we also expect, men moving to homes expect greater investments 197

of time devoted to interior maintenance. However, we put the obverse notion to the test as well. We asked if there is anything about the new residential environment that would create difficulties in doing what respondents desire. Of the relatively small number of respondents answering positively—about 20 per cent—more than two thirds are future apartment dwellers. They cite the lack of recreation rooms within dwelling units, the lack of privacy vis-a-vis adjacent units, and the lack of private gardens, as major inhibiting factors. The only inhibiting factor, but a noteworthy one among those buying houses, is lack of money as a consequence of having bought the house. Whether these expectations, positive and negative, are translated into reality will be assessed by our longitudinal use of time budget data in the near future.

References

AMERICAN BEHAVIOURAL SCIENTIST. 1966. Issues devoted to "The multinational comparative time budget research project. " A. Szalai, ed. Dec.: 10 No.4. BELL, W. 1960. "The city, the suburb, and a theory of social choice," in S. Greer, D. L. McElrath, D. W. Minar and P. Orleans, eds. The new urbanization. New York: St. Martins Press pp. 132-68. CLARK, S. D. 1966. The suburban society. Toronto: University of Toronto Press. FAVA, S. F. 1956. "Suburbanism as a way of life. " American Sociological Review 21: 34-7. CANS, H. F.

1967. The Levittowners. New York: Pantheon Books.

TOMEH, A. K. 1969. "Empirical considerations on the problem of social integration.' Sociological Inquiry 39: 65-76.

198

13

Household relocation patterns' J. W. Simmons and A. Baker

The greatest proportion of social change and population growth or decline within the city occurs by means of the migration of individuals and families. Although the magnitude of this process has long been recognized, and the motives for moving have been intensively studied (Simmons 1968), the spatial distribution of movement is not well known. The existence of an unusual data set permits an examination of relocation patterns in Metropolitan Toronto between 1958 and 1964. THE DATA As part of the Metropolitan and Region Transportation Study (MTARTS) home interview survey in 1964, a series of questions were asked about the timing and location of the most recent home and job changes (Metropolitan Toronto and Region Transportation Study 1965). From two to five per cent of the households in Metro Toronto were sampled, amounting to 13, 300, and their moves were coded to the census tract level. This information was linked to data on the age, size, occupation, income, and trip patterns of the household, providing the major input to this analysis. Refining the Sample It is important to distinguish two kinds of population which this sample can represent. The sample was originally designed to give an unbiased description of daily trip patterns of all households in Metro Toronto. It can be shown that the sample is indeed representative of the population of Metro households in 1964; but the study of residential location is interested in a different group of households, those that move within the study area within a defined time period. Propensity to move, as has been *This research was initially supported by the Canadian Council on Urban and Regional Research.

199

frequently demonstrated, depends largely on the life cycle stage of the family, and if only recent movers are considered or households are weighted by their propensity to move, quite a different sample emerges. Recent movers are a biased subset of the total population, over-representing persons aged 15-30 years. By considering only moves made in the last year the sample represents current movers—young, apartment-dwelling—and gives a picture of current movement trends. At the same time over 90 per cent of the records have been discarded. After weighing off these alternatives it was decided to restrict the sample so that only those households which moved during the last six years were studied. This limits analysis to a period when the urban environment is roughly constant, and includes about 7, 000 records or 53 per cent of the households in the initial sample. It should also be pointed out that only the most recent move of each of the sample households is recorded. As many as fifty per cent^of households in the study may have moved more than once during the study period. In similar fashion the spatial extent of the study was restricted to those moves which did not cross the Metro boundary. For the most part, then, the remaining sample of 5, 700 households is used to represent the total 203,400 household moves between 1958 and 1964. Such a sample is inadequate to represent the flows among a matrix of 301 X 301 census tracts in Metro so the tracts were aggregated into larger zones of analysis—62 B-level zones to approximate equal population size, and social homogeneity based on Murdie's analysis (Murdie 1969), and 18 C-level zones. The 203,400 households which are represented are assumed to exist both at the beginning and end of the time period. A closed system has been defined which does not grow by either net increase or net migration during the period 1958-1964. The totality of movement among and within the 62 zones is described in Figure 13.1. Origins and Destinations Some of the strengths and limitations of the data are demonstrated in the simple description of where migrants come from and end up. Within Metro itself the migration pattern is characterized by considerable imbalance (Figure 13.2). The suburban areas, rapidly adding new housing units, are experiencing absolute population growth, while the central city—where the number of housing units is relatively fixed appears to be losing population. In fact, the apparent losses are replaced by the creation of new households as young people move 200

* Estimated - We know that 23,200 went from Toronto to other locations within the MTARTS region

Figure 13.1 Household moves: Metro Toronto. 1958-1964, Based on f 5 ? zone system)

Figure 13,2

Origins, destinations and net movers

201

in and marry, and the migration of households from outside the Metro area, both from abroad and other parts of Canada. The net movement, although referring only to moves within the Metro boundary, resembles those previously calculated from Census data (see Paper 9). The present paper however ignores the aggregate net migration into Metro (approximately 50, 000 households during the study period). Nonetheless the patterns are essentially the same. High rates of out-migration are found at the city centre, particularly from the two lower-income sectors— the East End and the Northwest. The suburbs are consistently areas of in-migration with movement focussed on two or three zones of particularly rapid growth in this period—e.g. , zones 2, 9. 15 and 60. On the other hand, the North Toronto corridor (along Yonge Street) which attracts large amounts of apartment construction because of its upper middle class character and subway construction shows little net change. By representing the urban areas as a closed system, the map under estimates in-movers from zones which receive many immigrants from abroad and from other parts of Canada—i.e. . the inner parts of the low-income sectors. At the same time certain parts of Metro which contribute disproportionate numbers of movers to distant parts of Metro's urban field have out-mover estimates which are too low. Migration Rates Two distinct spatial patterns of mobility can be generated for cities. If, at the end of the time interval, households are asked about their previous movement and the results are mapped, a characteristically doughnut-shaped pattern is observed (Murdie 1969). The city centre with its small households, apartments and boarding houses is an area of high mobility, but so are the new suburban districts where entire neighbourhoods have moved in within the last five years. On the other hand, a study which looks at an initial population and asks how many of these people move within a finite time period, leads to a pattern with a single focus—the core of the city—similar to the distribution of the urbanization of family status dimension identified in social area analyses of Toronto (Murdie 1969). Figure 13. 3 is of the latter type. It takes as its base the households at the beginning of the study and asks which ones moved. The mobility rate is the ratio of movers from zone i to the total of stayers plus movers from i. The actual observed range of values is from 25 to 72 per cent. The pattern reveals 202

Figure 13.3 Out-movement rates (percent of households) the expected high mobility rates in the core area dropping off to low rates in the eastern and western suburban areas. More surprising is the core of high mobility rates to the Northwest— a sector of active expansion during this period by the Italian community. OVERALL FLOW PATTERNS

Figure 13.4 presents the movement patterns for the sample households during the period 1958-1964. The pattern is complex. Several zones (5, 15, 28, 38. 60) participate very strongly because of their size, but many small ones, particularly in the Northwest and in the periphery, do not appear to be closely linked to the entire urban system. The map pattern reflects decisions made in the preliminary spatial aggregation and the selection of the map legend. By altering these aggregation units and scales, the results can be modified substantially. Some general regularities can be observed, however. Short distance flows—that is within zones or to contiguous zones—predominate. The strength of outward movements is obvious: the inner suburbs are fed largely by the core, and outer suburbs by the inner suburbs. Four major migration streams can be identified, all essentially sectoral: the East End, the North End (Yonge 203

Figure 13.4

The flow matrix

62 zones (number of households)

Street), the Northwest (Dufferin Street), and the West End. Considerable flow also takes place between sectors. For example, zone 15 containing Don Mills, a self-contained suburban development, appears to draw migrants from all parts of the city. The unique aspects of the housing stock of this area and its isolated location (among the Don Valley ravines) keeps it apart from other sectors. The cartographic problems in mapping migration data are clearly shown when variations on Figure 13.4 are plotted. Figures 13.5 and 13.6 map total flows to and from each pair of zones i and j (fy + fj^) and net flows (fji - f-y). The former emphasizes the strength of the total linkages between locations and, presumably, those locations which have a strong reciprocal flow (fjj = fji) should be quite similar. The map shows a surprisingly strong pattern of localized radial contacts instead of the broader sectoral flows of Figure 13. 4. Moderately strong flows are less than the threshold size for plotting when taken individually, but appear stronger than the unidirectional sectoral flows when added together. The resultant map accentuates links between areas of similar life cycle stage. The map of net flows, on the other hand, emphasizes the nonreciprocal flows between zones of different life cycle stages. The unidirectional flows to growing suburban areas are shown but the more symmetrical linkages between inner-city zones are ignored. 204

Figure 13. 5 Total flows (number of households) Perhaps the major lesson to be drawn from this series of maps is that the entire Metro Toronto migration system is closely interlinked. There are no isolated communities observable at this scale. Descriptive Parameters A wide variety of measures can be developed to describe a flow matrix. Table 13.1 compares a series of descriptive measures for the basic flow matrices as aggregated at two scales, 62 zones and 18 zones. The value of the trace (that is per cent of within zone flows) increases as the size of zones increases. For the 301 census tracts in the study area the value of the trace is only 11. 9 per cent, a proportion much smaller than that reported elsewhere (Simmons [1968] suggests 20 to 25 per cent). The proportion of moves within tracts, however, should decline over time as a) real income increases, b) the metropolitan area grows, and c) contact fields within the metropolitan area increase in size. All three processes contribute to the low value observed at this date. It is surprising, though, that less than 40 per cent of the recorded moves took place within the 18 large community areas. Again, the conclusion is that Metro Toronto should not be con205

Figure 13. 6 Net flows (number of households)

TABLE 13.1 FLOW MATRIX PARAMETERS, HOUSEHOLD RELOCATION, METRO TORONTO Parameters*

18 Zone system

62 Zone systen

Trace

0.369

0.225

Symmetry

0.263

0.325

Resultant vector Origin concentration Destination concentration Element variance/mean

0.04 km. west 0.390 0.308 1.920

1.45 km. north 0.309 0.333 8.871

* For definitions see Simmons and Baker (1972)

ceived as a set of semi-independent communities, but as a single and widely integrated residential system. Another notable feature is the lack of symmetry in the household flow matrix. The correlation between flow (i.j) and flow (j.i) is 0.263 for large zones, 0.325 for the smaller ones. At both scales the strong outward bias of the migration field makes movement in one direction much more likely than a return flow (contributing, no doubt, to the weak relationship between flows and scale noted earlier). The implication of asymmetry is that a great deal of social change may be occuring because in-movers are not the same as out-movers. The Structure of the Matrix The technique of factor analysis has been widely applied in transportation and commodity flow studies (see Berry[l966))» The flow matrix is generalized by R-mode principal component analysis which treats columns as variables, correlates them, and then generates a smaller number of independent factors which describe common patterns of destination. Locations which load highly on a factor are destinations which share the same set of origins. Locations which have high factor scores are the origins which are linked to that set of destinations. The complement of this analysis is Q-mode, which uses rows as variables and generates factors describing common patterns of origin. The object of the analysis is to gain insight into the structure of the flow matrix by identifying significant sub-structures. However, because of the arbitrary nature of the spatial states used 207

in this study the structure in part reflects the initial aggregation procedure. The results which follow can therefore only be described in general terms. Particular values of parameters have relatively little meaning and interpretations can be modified by altering the minimum threshold values of measures to be plotted. The following discussion provides only very generalized pictures of what is happening. Four different matrices are factor analyzed: the original flow matrix with 62 states is treated in both R-mode and Q-mode. and the matrix of total flows (fy + fji) and net flows (fij - fji), as discussed above, in R-mode. The diagonal values are retained (they equal zero for net flows, and fn for the total flows), and the major dimensions are rotated. The cut-off point for the latter is determined by examining the range of eigenvalues in the initial principal components solution and rejecting those dimensions that are markedly weaker. When summarized (Figure 13.7), the analyses of household movement structure identify several weak migration subsystems, although not all of them are evident in each analysis. Each represents a spatially contiguous set of zones. The East End sector is most consistently defined. Bounded by the Don Valley and Lake Ontario, it contains 25 per cent of the Metro population and absorbs internally almost 75 per cent of the moves which begin within it. Two other sectoral subsystems, the West End and North Toronto, are about the same size and also absorb a high proportion (almost 60 per cent) of their movers. These three sectors reflect major alternative life styles in the city as described by Murdie (1969). The remaining subsystems do not extend through the full range of life cycle environments and are slightly less selfcontained. The Near West and Northwest subsystems could possibly be combined to indicate the fourth life style alternative, since there is a large outward flow from the former to the latter; and the Near East subsystem is partly linked to the East End group. The final grouping identifies the Lakeshore community— an old, somewhat isolated residential area which appears to be rather divorced from the rest of Metro. It is considerably smaller than the other subsystems, however. MOVEMENT OF HOUSEHOLD SUBSETS

The patterns discussed above refer to the aggregate of all households in Metro (within the limitations imposed by the sample bias). 208

Hatching identifies areas of overlap

Figure 13.7

Composite movement subsystems

However, aggregate patterns mask the diversity of movement generated by various sub-samples within the sample. The distance bias of a group of households originating in a single location, for instance, is well-known, and when this bias is superimposed on the particular spatial distribution of certain kinds of households or of housing opportunities of a limited price range, the resultant flow pattern differs considerably from the overall flow matrix. As the aggregate flow matrix is disaggregated to a greater and greater extent, patterns emerge which differ more and more. It should be stressed that the main source of variation in these patterns is the differences in the initial location of migrants and housing opportunities rather than differences in search behaviour and movement friction among social groups. With this in mind some of the observed patterns of movement by subsets of households among the 18 zones of level C are examined. It is possible to observe subsets defined for all combinations of the household measures given by MTARTS. In practice only one set of nominal categories at a time are examined, in order to maintain a useful sample size. The variables may be grouped into life cycle measures, social class measures, and accessibility measures. Unfortunately there is no way of identifying households by ethnic characteristics or religious preferences. 209

TABLE 13.2 MOVEMENT SUBSYSTEMS IN METROPOLITAN TORONTO a) Subsystems

Per cent of Per cent of Per cent of present outmovers who inmovers who Number of relocated relocated households households moved 1958-1964 within zone within zone

Number Name 1 2 3 4 5 6 7

East End West End North Toronto North West Near West Near East Lakeshore

44.6 53.0 53.4 58.7 49.7 43.0 52.7

108,800 70,800 94,600 81,800 63,800 22,100 16,200

74.1 56.2 59.0 56.9 40.5 30.8 53.8

59.9 51.7 53.3 48.9 68.1 45.7 54.8

b) Movement among the subsystems, 1958-1964 (number of households)

1 2 3 4 5 6 7

1

2

3

4

5

31527 2611 5655 3115 3948 5332

1744 15428 1702 3864 5066

2115 3739 4598 19287 8418

1208 1510 2199 2083 17713

510

3698 2458 21011 4977 5993 1148

452

1545

264

576 756

969 308

52640

29859

29549

39489

25990

7

Total

1897

339

390 65 263

1387

6

1913 3876

329 307 721 173

72

3958

42528 27253 35559 33986 43772 12584 7355

8476

7210

203217

The use of sample subsets is partially misleading because of a fundamental measurement problem. Data about household characteristics, age, income, etc. , refer to the household at the time of survey, i.e. , May 1964, not the time of move, which may have taken place up to 6 years before. The map of sixperson households, for instance, could conceivably include households which contained only one or two persons at the time of move. For the most part, though, it can be assumed that categories such as age (within the given categories) occupations and income will not radically change within 6 years. The great number of available subsets makes comparison among them difficult, but by using the series of parameters discussed in the previous section it is possible to say that this matrix of household movements is more symmetrical than that one, etc. It is difficult, though, to define sampling distributions for these parameters. Life Cycle Stages The findings of factorial ecology, in particular Murdie f s (1969) 210

Figure 13. 8 Variations in flow by household size

study of Toronto, suggests that life cycle or family status (i.e. , degree of urbanization) stages vary radially from the centre of the city (the most "urbanized" location in Toronto is at Yonge and St. Clair). Since life cycle changes drive most of the intraurban residential moves, household relocations should therefore take place along vectors radiating from the city centre, and as the city grows these moves should be biased outwards towards the perimeters of the urban area. 211

Figure 13.8 continued

When particular life cycle stages are examined, however, the patterns grow clearer. Figure 13.8 shows movement patterns by families of varying sizes, the measure which points up lifecycle variations most clearly. One person families are highly concentrated. Origins and destinations are congruent, and no major redistributions take place. Two person households show a similar pattern but they are larger in number and are distributed throughout most of the older parts of the city. The three 212

TABLE 13.3 FLOW MATRIX PARAMETERS—SIZE OF HOUSEHOLD Number of persons

Number of households Trace (per cent) Origin concentration Destination concentration Symmetry

6

8

8 +

1

2

3

4

12999 42.1

41339 38.9

39437 36.7

49222 33.3

29468 34.5

16285 35.8

6449 36.1

3920 39.1

4290 48.0

0.579

0.451

0.392

0.380

0.333

0.434

0.437

0.589

0.628

0.629 0.651

0.418 0.451

0.333 0.162

0.352 0.93

0.347 0.026

0.345 0.088

0.368 0.075

0.513 0.323

0.605 0.502

55

7

person family shows a strong outward movement with the sectoral migration streams clearly visible, and by the time the four person stage is reached most movements are taking place within the suburban ring. This latter pattern is maintained for households of larger size. Further aspects of this sequence are revealed when the parameters of the different flow matrices are compared (Table 13.3). The very large and very small families (8 persons and over and 2 or less) are strongly segregated spatially and move within a narrow range of housing opportunities (high trace values). Families with one to three children are much more apt to shift environments considerably leading to an asymmetrical flow pattern. Social Class Although everyone moves through the life cycle sequence and is eligible to participate in the movement pattern of each household (family) size category, measures of social class on the other hand confine households permanently to one or two subsets in a lifetime and constrain the social space within which they live and relocate. The income measure (Figure 13.9 and Table 13.4) creates a TABLE 13.4 FLOW MATRIX PARAMETERS—HOUSEHOLD INCOME

Sample size Trace Origin concentration Destination concentration Symmetry

($1000's) 8-10 10-12

12-16

16-20

20+

7758 41.2

5134 39.5

1637 44.4

1500 33.8

0.345

0.444

0.425

0.687

0.755

0.425 0.118

0.514 0.187

0.540 0.042

0.691 0.026

0.762 0.359

Under 2

2-4

4-6

6-8

6436 46.5

23004 41.1

48163 37.8

30219 30.9

16848 35.3

0.562

0.472

0.397

0.385

0.537 0.379

0.421 0.479

0.319 0.151

0.385 0.077

213

Figure 13. 9 Variations in flow by household income

sequence of flow matrices with properties much like those created by household size. Income groups at both the high and low ends of the scale are segregated and move among a few locations with little net change (high symmetry). Movements of middle income groups in contrast are less symmetrical and have a much greater range when plotted. It is the latter then who generate observed social changes in a neighbourhood. Spatially (Figure 13.9 a-d) the most apparent patterns of 214

Figure 13.9

continued

movement for the lowest income group are the tendency to relocate near to the previous residence, usually within zones, and the restriction to the oldest areas of the city. It is the next highest income group ($4000-$6000) that begins to participate in the suburbanization process, particularly from the Near West into Etobicoke and the northwest portion of North York. At slightly higher income levels ($6000-$8000) movement towards the Scarborough suburbs begins, but the highest income levels

215

($8000+) operate within narrow limits—the Yonge Street sector and central Etobicoke. CONCLUSIONS The material presented here has been largely descriptive and conclusions about the relative importance of various factors in household relocation must await more detailed analysis. Tentatively, however, the following generalizations can be made: 1) Movement patterns are very complex spatially, with virtually all pairs of zones linked in some fashion, by the aggregate flows. It is only when the migrant households and the distribution of moves and opportunities are disaggregated that highly structured patterns emerge. It will be of interest to examine the composition of migrant streams. 2) The data here represent a portrait of a process which is, itself, changing rapidly. The relocation process will be different in another decade as a) the population changes, and b) the urban environment is altered. The sensitivity of the flow matrix to household composition and opportunity patterns guarantees this difference. 3) Any spatial subsystems--!, e. , partially closed migration fields—are extremely weak. In the aggregate. Metro Toronto should be viewed as a single entity with considerable household movement among all locations. 4) In a rapidly growing city such as Toronto the addition of new housing stock plays an important role in residential relocation. A large proportion (about 50 per cent) of housing opportunities are generated in this way, and these opportunities are basically process-altering in that new housing—either downtown apartments or suburban bungalows—is often different in life cycle and social class characteristics to the previous housing in the area. Also new housing tends to attract householders from a wider range of spatial origins.

216

References

BERRY, B. J. L. 1966. "Essays on commodity flows and the spatial structure of the Indian economy. " Research Paper No. 111. Chicago: Department of Geography. University of Chicago. METROPOLITAN TORONTO AND REGION TRANSPORTATION STUDY. 1965. An analysis report on the 1964 home interview survey. Toronto: MTRTS. MURDIE, R. A. 1969. "Factorial ecology of Metropolitan Toronto 1951-1961." Research Paper No. 116. Chicago: Department of Geography. University of Chicago. SIMMONS, J. W. 1968. "Changing residence in the city: a review of intra-urban mobility." Geographical Review 58; 622-51. --. and BAKER, A. M. 1972. "Household movement patterns." Research Paper No. 54. Toronto: Centre for Urban and Community Studies. University of Toronto.

217

V

Impact of Growth on Rural Environments

Editors 5 comments Growth inevitably alters the form of the city and its region. Any increase in size changes the relationships between parts of a system. Sometimes these changes are subtle, others are more obvious and dramatic. In any case the impact on the system is irreversible. There are many spatial expressions of the impact of growth. Table 1 attempts to summarize the Toronto experience at different levels of spatial aggregation. Major highway improvements increase the directional bias of the transportation network toward the metropolis. The attraction of opportunities present in the metropolis reaches out and captures the population of outlying towns and villages through commuting and migration. Land changes in ownership status and in some instances its current use. New subdivisions appear and recreational facilities are added to serve the population of the core. Each may be apparent at differing scales: the central core, the built-up area, the fringe or urban field, or in the remainder of the metropolitan hinterland. This section looks at examples of the impact of growth on urban form. This impact is probably greatest at the core and on the fringe. One of the principal characteristics of the modern metropolis is its widespread impact on the surrounding "rural" countryside. The metropolis tends to envelop increasingly large areas and in so doing to reorient activities such that they focus on the metropolitan core. Two papers examine the direct impact of metropolitan Toronto on its tributary area. Both are essentially descriptive; in part because there is little available evidence documenting changes with the urban field. In the first paper Hodge summarizes the extent and direction of subdivision activity in the Toronto field. He concludes that the area is vastly under subdivided— there is insufficient land in the process of conversion to a "developable" state to accommodate expected population growth over the next five years. This no doubt is contributing to the escalation 219

TABLE 1 SPATIAL IMPACT OF GROWTH: THE TORONTO EXPERIENCE Scale of measurement Component

I (urbanized area)

Form of development

a) growth at boundary

a) spill-over pressures from expansion of Area I b) redevelopment within b) growth at all nodes, many are gradually enveloped as urbanized area expands

Land use

Four major patterns: a) core redevelopment and replication b) fringe expansion

II (field)

III (hinterland) a) growth at major nodes only and proportional to size and distance from Toronto b) random growth at certain resource sites— unstable

a) structuring of ownership a) structuring of economic and use with respect to activities with respect to Metro market Metropolitan core b) transfer of agricultural b) replacement of production land to residential and priorities by consumption recreational use (recreation) priorities c) decentralization of certain urban activities

c) revisions of transport and utilities networks d) growth of major d) political reorganization nucleations--institu--regional authorities tions, commercial, apartments Social areas

a) continued population a) rapid growth originating a) population growth—with growth, greatest at from urbanized area associated demographic, periphery ethnic and social class changes--concentrated at a few major nodes b) ethnic shifts domin- b) young urban families b) continued net outate internal changes modify older rural migration from rest of as social areas settle population structure region c) increased life cycle c) sectoral social class c) pockets of rural poverty and ethnic segregation patterns of urbanized develop through aging and area super-imposed urbanization processes

Transport systems

a) expressway system surrounds core b) subways focus on core c) peripheral highway networks emerge

a) expansion of commuter a) Toronto-centred expressservices to downtown way network evolves core b) expressways identify b) major highways to congrowth nodes sumption (resort) areas c) extensive singlec) reduced alternatives in purpose facilities; major transport facilities airports and railroads within local areas freight yards

of land costs for urban purposes. In the second paper. Hill documents the extensive pattern of migration within the region. Most of this migration is centrifugal in direction, indicating dramatic decentralization of the metropolitan population. Within this general movement pattern, mobility rates were differentiated primarily by family size and socioeconomic status. The latter in particular appears prominently in identifying migrant streams by sectors. These sectors, 220

radiating outward from the metropolitan core, carry basically the same status characteristics of Metro Toronto into the outlying fringe.

14

Subdivision activity in the periphery of the Toronto urban field G. Hodge

Paralleling what is know popularly as T!people flocking to the big cities , ! f is the dispersion outward from the centre of cities of people and activities over large distances. Once the purview of the well-to-do and mobile Tt exurbanite," the modest subdivision is now appearing as much as forty miles from downtown. This paper reports on a probe of the relatively new phenomenon of subdivisions for modest housing in the periphery of the Toronto urban field. Data Sources Despite the amount of regulation and control over the subdivision of land for urban purposes in Ontario, there is no inventory of the amount and location of subdivision activity, much less an assessment of the rate at which it is proceeding. This study can only partially fill that gap. The research determined that, while every subdivision application is recorded in a municipal ledger and on a map, data on amount and location have never been summarized. Furthermore, the data which does exist covers only applications for subdivisions and not the actual building of houses on the land. 221

Figure 14. 1 Subdivision activity 1950-1959 Within these limitations it is possible to develop a fairly complete picture of the market area for urban subdivisions. By analyzing the applications it can be shown where the subdividers anticipated that the demand for housing in the periphery could best be met. The time consumed in processing new subdivisions of up to two years biases development in favour of existing subdivisions. For this study subdivision activity within 40 miles of the core area is examined, but excluding that taking place at the present perimeter of the built-up Lakeshore corridor. Figure 14.1 indicates the area studied. The data consists of information recorded about "approved subdivision plans" by the Ontario Department of Municipal Affairs, Community Planning Branch (1950-1968). Subdivision activity dating back to 1950 is analyzed. The only data considered reliable are the size of anticipated subdivisions (in acres) and their location. Data up to and including 1968 are available and utilized. Subdivision activity has been recorded by township or town. Three general groupings have then been used to summarize development north, east, or west of the metropolitan core. Rates of development are analyzed for the entire 18 year 1950222

Figure 14. 2 Subdivision activity 1960-1968 1968 period and for the two halves of the period 1950-1959 and 1960-1968. Figures 14.1 and 14.2 and Tables 14.1-14. 3 portray and record the results of this analysis. One further refinement is made from the data: an answer is sought for the question of whether peripheral subdivision shows any tendency to congregate around existing centres or is broadly distributed in rural areas. Thus, the data are arranged according to "town oriented" (located within two miles of an existing town) or "rural oriented" (located more than two miles from an existing centre). Subdivision Activity 1950-1959 In the nearly full decade after 1950, a total of 11, 500 acres are approved for subdivision (Table 14.1). Just over 80 per cent of this occurs in close proximity to existing towns. In this period, the north sector accounted for nearly half of the approved subdivisions, »or 47 per cent. There is slightly more in the east (29 per cent) than in the west (nearly 24 per cent). Subdivision plans for the area north of the metropolitan core are less oriented to existing towns than the other two sectors. Looking within the individual sectors, the west has fairly 223

TABLE 14.1 SUBDIVISION ACTIVITY IN THE PERIPHERY, TORONTO URBAN FIEL 1950-1959 Township West Adjala Albion Caledon* Chinguacousy Erin Esquesing Mississauga Nassageweya Nelson Toronto Gore Trafalgar Sub total

Town oriented Acres Per cent -

68.51 238.86 585.95 25.77 561.23 753.69 -

-

40.7 94.9 80.0 100.0 100.0 93.3

100.0

2411.25

87.8

Total acres

-

-

-

99.61 12.78 146.77

59.3

168.12 251.64 732.72 25.77 561.23 808.10

-

54.41

-

177.24

Rural oriented Acres Per cent

-

5.1

20.0 6.7 -

-

100.0 -

20.14 177.24

12.2

2744.96

-

160.32

14.1 20.3

2076.61 266.48 72.70 890.76 763.31 46.10 528.97 791.59

20.14 -

333.71

North 2076.61 Yonge Sector Gwillimbury East Gwillimbury West 66.04 516.78 King Markham 392.29 46.10 Tecumseth 454.66 Vaughan Whit church 631.27

90.8 58.0 51.4 100.0 85.9 79.7

4183.75

77.0

1252.77

23.0

5436.52

44.3

914.85

Sub total

100.0

-

-

266.48 6.66 373.98 371.02 -

East Pickering Reach Scott Uxbridge Whitby Whitby East

509.18

55.7

405.67

6.79 681.00 1693.22

100.0 90.3 100.0

73.51

Sub total

2890.19

Grand total

9485.19

-

-

-

100.0 9.2

42.0 48.6 -

-

-

9.7 -

6.79 754.51 1693.22

85.8

479.18

14.2

3369.37

82.1

2065.66

17.9

11550.85

SOURCE: Ontario Department of Municipal Affairs Community Planning Branch (1950-1968). * includes Orangeville.

widespread subdivision. Six of the eleven townships have at least 160 acres each (enough for about 700 single family houses, and 3500 people); three have nearly a square mile each (enough to 224

accommodate 7000 persons in single-family suburban densities). Easily recognized are the burgeoning towns of Brampton (Chingacousy), Georgetown (Esquesing), Orangeville (Caledon), and Milton (Mississauga). In the north sector, several townships experience large-scale subdivision activity in the countryside away from towns. However, nearly 40 per cent of all activity is accounted for by a band of proposed development on both sides of the north Yonge Street corridor. In the east sector, the attractiveness of Oshawa is clearly evident in the subdivision proposals. And the emergence of Ajax as a new town shows up in the Pickering figures. Subdivision Activity 1960-1968 In this second nine-year period (Table 14.2). the total volume of subdivision activity drops to less than half of that in the previous period. Just under 5500 acres are approved. The proportion which is town-oriented dropped slightly to just below 80 per cent. But as well as a change in volume, there is also a noticeable shift in direction of the subdivision activity. In this recent period, the sector on the west has received most attention from subdividers. Over half of the approved acreage occurred there as that area continues to receive as much attention as in the previous period. Both the other sectors slow significantly. The north drops to about one-fifth of its earlier level and the east drops to just under one-half of its 1950-1959 level. Proposed subdivisions around Orangeville, Brampton, Milton, and Georgetown continue to dominate activity on the west. Seveneighths of the total is town-oriented. In the north, the ruraloriented subdivisions increase their earlier share. And the Yonge sector adds only 183 acres as against its earlier 2076 acres. In the east, rural-oriented subdivisons appear primarily in Pickering township sufficient to take the rural share up to 31 per cent. The Oshawa area continues to attract the majority of attention. Total Subdivision Activity Aggregating subdivision activity over the 18 years prior to 1969 (Table 14. 3), shows that just over 17,000 acres are included in approved subdivisions within roughly 40 miles of Toronto (excluding the Lakeshore). 1 This amounts to about 27 square •'-Subdivision activity beyond 40 miles, excluding Kitchener-Waterloo and Niagara areas, is very small.

225

TABLE 14.2 SUBDIVISION ACTIVITY IN THE PERIPHERY, TORONTO URBAN FIELD, 1960-1968

Township

Town oriented Per cent Acres

Rural oriented Acres Per cent

Total acres

West Adjala Albion Caledon* Chinguacousy Erin Esquesing Mississauga Nassagaweya Nelson Toronto Gore Trafalgar Sub total

-

250.49 1821.78 40.29 88.30 278.37

-

47.4 100.0 100.0 100.0 100.0

75.23

100.0

75.23

277.66

52.6

528.15 1821.78 40.29 88.30 278.87

-

-

-

2479.73

87.5

183.35

100.0

-

352.89

12.5

2832.62

19.20

100.0

183.35 19.20

North Yonge Sector Gwillimbury East Gwillimbury West King Markham Tecumseth Vaughan Whit church

40.37 115.20 239.64 108.74 158.75

84.4 29.6 100.0 100.0 100.0

7.44 274.02

15.6 70.4

47.81 389.22 239.64 108.74 158.75

Sub total

846.05

73.8

300.66

26.2

1146.71

325.63 16.36 -

40.7 87.7

474.17 2.30 _

59.3 12.3

799.80 18.66 -

40.20 657.64

100.0 100.0

Sub total

1039.83

68.6

476.47

31.4

1516.30

Grand total

4365.61

79.4

1130.02

20.6

5495.63

East Pickering Reach Scott Uxbridge Whitby Whitby East

40.20 657.64

SOURCE: Ontario Department of Municipal Affairs Community Planning Branch (1950-1968). * includes Orangeville.

miles, or enough to accommodate about 200.000 persons. Of this amount, 81 per cent are included in subdivisions for areas adjacent to, or near, existing towns. The remainder are scattered. 226

TABLE 14.3 SUBDIVISION ACTIVITY IN THE PERIPHERY, TORONTO URBAN FIELD, 1950-1968

Township West Adjala Albion Caledon* Chinguacousy Erin Esquesing Mississauga Nassagaweya Nelson Toronto Gore Trafalgar Sub total

-

68.51 489.35 2407.73 66.06 649.53 1032.56 -

75.23 99.61 290.44 146.77

-

40.8 62.8 94.3 100.0 100.0 95.0

-

54.41

-

177.24

100.0

4890.98

87.7

North 2259.96 Yonge Sector Gwillimbury East 66.04 Gwillimbury West 557.15 King Markham 507.49 285.74 Tecumseth 563.40 Vaughan 790.02 Whitchurch Sub total

Rural oriented Acres Per cent

Town oriented Acres Per cent

-

20.14

100.0 -

90.8 59.4 44.0 100.0 88.3 83.1

100.0 59.2 37.2 5.7 5.0 -

Total acres 75.23 168.12 779.79 2554.50 66.06 649.53 1086.97 -

100.0

-

-

20.14 177.24

686.60

12.3

5577.58

285.68 6.66 381.42 645.04

100.0 9.2 40.6 56.0

74.31 160.32

11.7 16.9

2259.96 285.68 72.70 938.57 1152.53 285.74 637.71 950.34

5029.80

76.4

1553.43

23.6

6583.23

51.3 12.3

1714.65 18.66

9.2

6.79 794.71 2350.86

East Pickering Reach Scott Uxbridge Whitby Whitby East

834.81 16.36

48.7 87.7

879.84 2.30

6.79 721.20 2350.86

100.0 90.8 100.0

73.51

Sub total

3930.02

80.4

955.65

19.6

4885.67

13850.80

81.2

3195.68

18.8

17046.48

Grand total

SOURCE: Ontario Department of Municipal Affairs Community Planning Branch (1950-1968). * includes Orangeville.

Within the metropolitan periphery, several foci of subdivisions are evident. The most prominent is around Brampton .followed closely by Oshawa. Both of these centres are about 25 miles, northwest and east respectively, from the metropolitan 227

core. The Yonge street sector north of Toronto receives the next highest level of activity. These three areas account for 40 per cent of the approved subdivision acreage in the period. Orangeville, Milton, Georgetown in the west; Markham, Woodbridge, Kleinberg. Bolton, Aurora in the north; and Ajax and Whitby in the east are other concentrations of new subdivision proposals. Conclusions In the area analyzed, up to 40 miles from downtown Toronto, just over 17,000 acres are approved for subdivision, or enough land to accommodate about 200, 000 persons. Of this amount 81 per cent is for subdivisions adjacent to or near existing towns, such as Brampton, Oshawa, Georgetown, Orangeville, Aurora, and Ajax. In the first half of the period, 1950-1959, most subdivision activity is to the north and east of Toronto. In the second half, 1960-1968, most activity is to the west and northwest. As well, the total volume of subdivision activity slows in the second half to only half the volume of the first half. From recent experience, there is likely to be a continuation of this peripheral subdivision activity at least at the 1960-1968 pace. The effect of the recent imposition of provincial subdivision regulations over the whole region is hard to assess. Volume will probably remain high but location will be controlled so as to ensure adequate water and sewerage service. This will probably increase the orientation of these subdivisions to towns with adequate utilities. It is mentioned above that the area under approved subdivision plans from 1950 to the end of 1968 in the periphery could accommodate about 200,000 persons, if it were mainly in singlefamily "suburban" densities (12 per gross acre). This is not a large number of people when it is realized that by 1966 the same peripheral area contained about 270,000 persons. Most of the growth of this area has occurred since 1950; most of it probably in the subdivisions we have referred to. Finally, this is a relatively small volume of subdivided land in a metropolitan region which is growing by about 60, 000 persons per year. It refutes the conventional wisdom that we are vastly over-subdivided. Indeed, the sharp slowdown in subdivision activity after 1960 could well be leading to a shortage of land available for urban development outside the suburban fringe areas. 228

References

ONTARIO DEPARTMENT OF MUNICIPAL AFFAIRS. Community Planning Branch. 1950-1968. Approved subdivision plans. Toronto.

15

Migration in the Toronto-centred region' F.I. Hill

Studies of population redistribution have not kept pace with the recognition that the traditional concept of the city or metropolis is an inadequate unit for the analysis of the ecology of our postindustrial society. At a scale between studies of inter-regional or inter-metropolitan migration on the one hand, and studies of intra-urban migration on the other, few investigations of migration within units which more closely approximate the life-space of the metropolitan population have been carried out (Berry, Goheen and Goldstein 1968). One such unit, the "urban field!T as defined by Friedmann and Miller (1965), is an area extending perhaps 100 miles from a metropolitan core of at least 300, 000 people (Hodge 1970). This paper examines the pattern and selectivity of migration *The present paper is a revised and shortened version of an earlier research paper, F.I. Hill, "Migration in the Toronto-Centred (MTARTS) Region," Research Paper No. 48, Centre for Urban and Community Studies, University of Toronto 1971. The assistance of James Simmons is gratefully acknowledged.

229

Figure 15.1 Urban places in MTARTS area (as defined on basis of traffic zones) within a large portion of the urban field of Toronto. Emphasis is placed on the effect of the migration exchange between Metro Toronto and the remainder of the region on the size and socioeconomic composition of the population of Metro Toronto and the surrounding area. The study region (see Figure 15.1) is that of the Metropolitan Toronto and Region Transportation Study (MTARTS). Although this region does not encompass the entire area within the Toronto urban field as delimited by a 100-mile radius, it does approximate a meaningful migration field. The MTARTS area has been an area of substantial population growth and net in-migration. All counties in the region had a net in-migration during the 1951-61 period; in fact the counties of Ontario, York, Peel, and Halton had a net rate of in-migration of over 25 per cent. Most townships and all cities over 20, 000 in the region had a rate of population increase of more than 30 per cent during this period (Dean [1969] plates 15, 18, and 23). The Data Data derive from the MTARTS home interview survey. A 3.3 per cent sample of households were asked their place of previous 230

residence and length of time in their present residence. Locations of present and previous residence are coded at the level of some 900 areal units within the MTARTS region. Those units with predominantly urban land use in 1964 are aggregated into 47 urban places. A migration matrix is constructed portraying the number of migrant households to and from each centre, based upon the most recent residence of each household, regardless of the time of migration. A word of warning is necessary concerning the sampling method and the resulting biases introduced into the data (Traffic Research Corporation [1965] pp. 27-28). Lists of every tenth or fifth customer were provided by municipal or provincial Hydro Commissions. First, although 99.5 per cent of households are served, and less than 1 per cent on bulk meters, residents of particular characteristics, concentrated in particular areas, tend to be excluded. Second, many of the household characteristics recorded in the interview pertain only to the head of the household. Young, single persons in particular are less likely to be heads of households; even though they have migrated from their parents1 home, they are likely to live in various types of group quarters or as lodgers, and to be missed in the sample. Third, household characteristics are recorded as they exist on the date of the interview, not as they were on the date of migration. Some of these characteristics may have changed since the most recent change of residence. The Pattern of Migration Figure 15.2 showing the destinations of the largest numbers of migrant households from each of the 47 centres in the region, reveals the overwhelming dominance of Metro Toronto as a centre of destination of out-migrants. In spite of the pervasive influence of Toronto, however, Oshawa. Barrie, Aurora, Richmond Hill, Georgetown, Oakville and Hamilton carve out their own migration fields, composed of a number of smaller centres around them. The hierarchical nature of migration fields is suggested by such a pattern. Migration volumes outwards from Toronto, on the other hand, appear to be little influenced by the size of the centre of destination. With the two exceptions of Glen Williams and Whitby, Metro Toronto is the origin of the largest number of in-migrant households to all centres as far removed as Oakville in the west, Oshawa in the east, and the boundary of the MTARTS region in the other directions. 231

Figure 15. 2 Destinations of maximum out-migration This set of migration regions or fields is in substantial agreement with the middle-order functional regions delimited by Carol (1969) on the basis of consumer travel for professional and medical services, shopping, and urban recreation, and with Russwurm r s (1970) findings with respect to newspaper and mail flows. Figure 15.3 reveals that only 10 out of a possible 46 centres suffer a net loss to Toronto. All except Pickering and Glen Williams are in the periphery of the region. Apparently small towns too far from Toronto to act as dormitory towns for Toronto have suffered losses to the city. Hamilton experiences an essentially even exchange of households with Metro Toronto. Guelph, Barrie, and Oshawa. on the other hand, show substantial net gains from Toronto. The pattern of net migration among the other centres of the region is one of progressive outward movement away from Toronto, especially north as far as Bradford, west as far as Georgetown, and southwest as far as Oakville. East of Toronto, the pattern of net flows is less consistently outward from Toronto. When migration to and from all other urban MTARTS centres together is considered (Figure 15.4) only 12 centres have a net 232

Figure 15,3 Ratio of in-migrant households from Toronto to out-migrant households to Toronto

Figure 15. 4 Ratio of in-migrant to out migrant households to/from urban MTARTS centres 233

loss. The most severe net losses to the other urban centres are sustained by some smaller peripheral centres and by Hamilton and Toronto. Both Toronto and Hamilton receive only 0.27 times as many in-migrant households from other urban MTARTS centres as they send to the other centres in the MTARTS region. By comparison, the ratios of in-migrant to out-migrant households in Guelph and Barrie are 3.6 and 2.4 respectively, while Oshawa has an even exchange. The MTARTS region beyond Metro Toronto is subdivided into four sectors and three concentric zones centred upon downtown Toronto (Figure 15.1). Although it may have been desirable to use sectors which are continuations of those used by Murdie (1969) in his study of the factorial ecology of Metro Toronto, the spatial arrangement of centres beyond the Metro boundary precludes a strict adherence to these sectors, if sizeable numbers of migrant households are to be retained in the sample for each sector. The sectors adopted, however, largely straddle the main transportation routes leading from the city. Table 15.1 shows that there is much more variation among zones than among sectors in the ratio of households migrating to and from Metro Toronto. Zone 3 stands in sharp contrast to the other two, since Toronto has almost an even exchange of households with zone 3. All but two of the centres which have a net loss of households to Toronto are in zone 3. Through time, the inter-zonal range in the ratio of inmigrant to out-migrant households decreases, not as a result of any change in the zonal distribution of out-migrants, but because of the increasing proportion of in-migrants from zone 1 and the decreasing proportion of in-migrants from zones 2 and 3. On the other hand, the later period saw an increased inter-sectoral range in the in-/out-migration ratio for Metro Toronto, which results from sectoral changes in the distribution of both inTABLE 15.1 SECTORAL AND ZONAL VARIATION IN MIGRATION BETWEEN METRO TORONTO AND MTARTS REGION

To Metro Toronto Before 1959 1959-64 Number Per cent Number Per cent

From Metro Toronto Before 1959 1959-64 Number Per cent Number Per cent

To/From Before 1959 1959 -64

Sector or zone

Households 1964

East North West Southwest

30,972 26,294 29,045 124,278

661 927 440 1203

20 29 14 37

554 1218 598 1925

13 28 13 45

1814 3889 2231 5590

13 29 16 41

3091 5029 2246 4219

21 34 15 29

0.36 0.24 0.20 0.22

0.18 0.24 0.27 0.46

Zone 1 Zone 2 ZoneS

30,942 38,462 141,170

918 1115 1199

28 34 37

1983 1175 1136

46 27 27

7931 4313 1281

59 32 9

8494 4649 1442

58 32 10

0.12 0.26 0.94

0.23 0.25 0.79

Total

210,574

3232

100

4294

100

13525

100

14585

100

0.24

0.29

234

TABLE 15.2 LENGTH OF TIME SINCE MIGRATION TO AND FROM METRO TORONTO BY ZONES OR SECTORS* Urban Length of time MTARTS East North West Southwest Zone 1 Zone 2 Zone 3 Migrants to Metro Toronto (Percentage of households) Under 5 years 5-10 years 10-20 years Over 20 years

57 27 11 5

46 46 9 0

57 24 15 5

58 32 4 6

62 22 11 5

68 23 7 2

51 26 16 6

49 35 11 6

Migrants from Metro Toronto (Percentage of households) Under 5 years 5-10 years 10-20 years Over 20 years

53 30 14 3

63 21 12 3

56 31 11 1

50 39 7 3

43 31 21 5

52 29 16 4

52 36 11 1

53 25 15 7

* Based on only the most recent previous residences of MTARTS households. Once a household has moved within Metro, for example, it is no longer an in-migrant household. Considerably less than 57 per cent of all households which have moved to Toronto from the MTARTS area by 1964 and remain in Toronto would have moved there within 5 years.

migrants and out-migrants, particularly among the eastern and southwestern sectors. These changes are explainable in terms of the maturity of suburban development (Table 15.2). By the early f sixties, the suburbs in zone 1 and the southwestern sector in particular have matured to the stage of contributing large numbers of households back to Metro Toronto. Migration to the eastern sector has been more recent, while a higher proportion of migrants from Toronto to zone 2 than the other zones has moved in the 1954-59 period. Zone 1 has even received a considerable proportion of its migrants from Toronto during the 1944-54 decade. Most important, however, is the fact that, in spite of this suburban maturity in selected areas of the MTARTS region, the decentralizing stream is still over three times as great as the in-migrant stream, for the 1959-64 period. In order to estimate the impact which this movement has upon a centreTs population, a measure of the magnitude of migration, in comparison with the size of the resident population, is essential. Figure 15.5 reveals that over 50 per cent of the households in six centres near Toronto have moved to their 1964 residence directly from Metro Toronto but the proportion of households which have moved from Toronto declines with distance from the city. Migration has been extremely important in the derivation of the population in zones 1 and 2 (Table 15. 3). Even 235

igure 15. ;3 i ercentage of households with most recent revious residence in Metro Toronto TABLE 15,3 ZONAL VARIATION IN RATES OF MIGRATION TO AND FROM TORONTO, 1959-64* Proportion of households Rate of inRate of out- Net rate of with most recent previous migration migration to migration within Toronto residence in Toronto Zone from Toronto Toronto

1 2 3 Total

21.0

1.0

6.4 3.1 0.8

9.0 0.2

53. 1 per cent 23.3 per cent 2.0 per cent

6.9

2.1

4.8

13.4 per cent

27.4 12.1

* Migration rates are the proportion of resident households which have migrated to or from Metro Toronto within five years, as indicated by their most recent previous residence.

the net exchange with Metro Toronto in the 1959-64 period represents one-tenth of zone 2 T s households and one-fifth of the households in zone 1 in 1964. Migration between zone 3 and Toronto has a minute effect on the size of the population in either area, since the net exchange represents only one-fifth of one per cent of the number of households in zone 3. Although migration from Toronto has had a substantial im236

pact upon the population of the MTARTS area, particularly zones 1 and 2, the effect of this migration upon Metro Toronto's population is proportionately smaller, because of Toronto r s larger population. For example, only 1.6 per cent of Metro Toronto's households have moved to their 1964 residence from all the rest of the urban centres in the MTARTS region. A further 3. 3 per cent of Toronto's other urban MTARTS centres, have moved directly from beyond the MTARTS region but within Ontario. In fact, Toronto has received more households from each of Ottawa, London, Windsor, St. Catharines, Kitchener, and Peterborough, than from Hamilton. Metro Toronto is itself divided into 18 zones, on the basis of size and homogeneity with respect to a number of socioeconomic characteristics (Figure 15. 6). 1 All 18 zones have a net loss of households to the other urban centres of the MTARTS region considered together (Table 15.4). Ratios of in-migrant to out-migrant households ranged from 0.06 in zone 13 in the heart of the Italian area to 0. 75 in zone 5 in southeastern Scar-

Figure 15,6

Net flows: Metro zones to MTARTS sectors

^The 18 zones resemble those used by Simmons and Baker in their study of migration within Metropolitan Toronto (see Paper 13).

237

TABLE 15.4 MIGRATION BETWEEN METRO TORONTO ZONES AND MTARTS AREA, TO 1964* Metro zone Households Ration of in- to outmigrants to/from MTARTS urban centres: migrants to and from other Metro zones being Included Excluded

1

2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

8,861 25,158 21,478 6,418 27,432 46,501 39,410 22,791 22,462 42,281 28,202 14,452 26,326 41,9.85 20,011 31,618 17,618 39,115

0.49 0.40 0.26 0.56 0.75 0.33 0.09 0.18 0.26 0.20 0.23 0.15 0.06 0.21 0.18 0.20 0.29 0.45

2.57 2.23 1.76 2.48 3.61 1.92 0.45 1.10 1.09 0.98 0.48 0.29 0.27 0.59 1.31 0.50 0.94 2.56

Rates (per 100 households), excluding migrants within Metro Toronto In

Out

3.6 2.8 2.6 2.3 2.6 1.4 0.5 0.6 1.2 0.8 0.9 1.6 0.2 1.5 1.5 1.3 4.4 2.4

7.3 6.9 10.0 4.2 3.5 5.1 6.0 3.3 4.6 4.1 4.1 10.2 4.4 7.2 8.3 6.6 16.0 5.2

Net

-3.7 -4.1 -7.4 -1.9 -0.9 -3.7 -5.5 -2.7 -3.4 -3.3 -3.2 -8.6 -4.0 -5.7 -6.8 -5.3 -11.6 -2.6

* Based upon most recent previous residence, regardless of the recency of the move.

borough. Most of the peripheral zones have less severe losses than the inner zones. Exceptions among the peripheral zones are zone 17, including the old lakeshore communities of Mimico, New Toronto, and Long Branch, and zone 3 astride Yonge Street north of Highway 401, which have a substantial loss to both Thornhill-Langstaff and Richmond Hill. Losses by the peripheral zones to the MTARTS area, however, are more than compensated by gains from the more central portions of Metro. Figure 15. 6 reveals the directional tendencies in the net migration flows from Toronto to the four sectors of the MTARTS region. The origins of the net flows to each of these sectors are predominantly those parts of Toronto lying in the direction of the sector of destination. This directional bias applies to both in-migrant and out-migrant households. The relationship between Metro Toronto and the rest of the MTARTS region, in connection with migration patterns, bears a striking resemblance to the relationship between Toronto city 238

proper and the remainder of the Census Metropolitan Area, During the period from 1956 to 1961. Toronto city proper has received only 0.28 times as many persons from the remainder of the CMA as the latter has received from the central city. 2 This ratio is almost exactly the ratio of in-migrant to outmigrant households exchanged between Metro Toronto and the MTARTS region for the 1959-64 period. Whether at the Toronto city proper—Toronto CMA scale or at the Metropolitan Toronto— MTARTS area scale, the centrifugal stream predominates in the pattern of migration. So strong has this stream been that 13. 8 per cent of the households in the MTARTS region outside Metro Toronto are known to have moved to their 1964 residence directly from Metro Toronto, while direct movers in the reverse direction comprise only 1.6 per cent of Metro Toronto households. Direct movers from the remainder of Ontario comprised a further 3.3 per cent of Toronto!s households, and 4.3 per cent of those in the rest of the MTARTS region. Since the MTARTS data provide information on only the most recent previous residence of households, it is impossible to determine to what extent Toronto has been the ultimate source of the population in the remainder of the region ; but, even allowing for return migration, the proportion is probably considerably higher than the 13.8 per cent supplied directly. With this description of the pattern of migration within the region, it is possible to examine the characteristics of households which have migrated to and from the various parts of the region, particularly migrants exchanged between Metro Toronto and the remainder of the region. Mobility Rates The composition of the various migration streams depends to a large extent upon the mobility rates of households of particular characteristics. These rates are therefore discussed first. 3 Migration rates for households in 44 categories are calculated, using.as a measurement the proportion of households of a particular characteristic which are known to have moved from ^Figures obtained from the 1961 Census of Canada, which compare the 1956 and 1961 residences of a 20 per cent sample of the Canadian population aged 5 years and over. See 1961 Census of Canada, "Migration, Fertility and Income by Census Tracts", Bulletin CX-1 (Ottawa: DBS, 1965). The city to ring flow amounts to 63,000 persons, compared to only 18,000 in the opposite direction. 3 Detailed tabulations supporting the generalizations made in this and subsequent sections of this paper can be found in a paper of the same title which appeared as Research Paper No. 48. Centre for Urban and Community Studies.

239

one zone to another within the last five years. 4 This measure is referred to as the inter-zonal migration rate. For all urban MTARTS households, the inter-zonal migration rate is 20.67. The intra-zonal mobility rate of 16.92, representing the percentage of urban MTARTS households which have made an intrazonal move within five years, can be added to the inter-zonal migration rate to produce a fT total !f five-year mobility rate of 37.59. Still excluded from this "total" are those who have moved to the MTARTS region from beyond the region or from the rural portions of the region to an urban MTARTS centre, or from an unspecified previous residence. Together, these three mobile categories excluded from the total mobility rate account for a further 8 per cent of the urban MTARTS households, since 47 per cent of all households in the urban centres of the MTARTS region have moved within five years. Overall, the MTARTS data provide further substantiation of the dominant role of stage in the lif:e cycle in influencing mobility rates. Whether stage in the life cycle is approximated by variables relating to age of the head of the household, the presence of children under five, or apartment versus single detached dwellings, variations in intra-zonal and inter-zonal mobility appear to be much better differentiated by life cycle considerations than by indicators of socio-economic status (Simmons 1968). The well-documented tendency towards high mobility rates among persons in the early years of family formation and entry into the labour force is again found to prevail. Higher mobility rates among the higher status groups also confirm previous findings, as does the preference for short-distance moves by those with low-status occupations. Selectivity of Migration Streams^ Both migrants to Metro Toronto from the remainder of the urban MTARTS region, and the corresponding reverse stream, tend to form larger households than the average in the area of destination. This exchange, however, would have resulted in increases in the This measure, therefore, includes those who have moved from one of the eighteen zones in Metro Toronto to another, as well as those who have moved to and from any of the other MTARTS centres. Excluded are those households whose most recent move has been within any of the 18 Metro zones or any of the other centres, and those who have made an interzonal move in the last five years followed most recently by an intrazonal move. Comparisons in this section are based upon the characteristics of households which have migrated within five years of the interview date. The period covered is 1959-1964.

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proportion of large (5 or more person) households in Metro Tor onto, in spite of the fact that Toronto's proportion of such households is low, relative to that of the rest of the area. Toronto T s loss is particularly marked among 4-person households, especially in the exchange with zone 1. The household size composition among migrants between Metro Toronto and the rest of the region varies considerably throughout the zones and sectors surrounding Metro, and in fact in many cases the net effect works against the establishment of Toronto's characteristically small households. Both streams of migrants between Metro Toronto and the rest of the region have an over-representation of households with children under 5 years old, but the differential between inmigrating and out-migrating households in Metro Toronto in terms of the presence or absence of children under 5 years of age amounts to almost 11 percentage points. This differential has contributed towards the low proportion of households with pre-school children in Metro Toronto. It applies to all sectors except the west (where there is no differential), and to all zones with approximately equal strength. In this respect, a similarity with traditional suburbanization processes at the metropolitan scale is evident. The age structure of the migrant household heads reinforces this similarity. The net effect is towards increasing proportions of households with younger heads (under 35) beyond Toronto, and an increase in the older groups in Metro Toronto. The net change, therefore, favours the establishment of the age differential which is characteristic of the resident population. The most marked difference lies in the bias towards out-migration from Toronto by households in the 26-35 year range, and in-migration to Toronto by the 46-55 year group. This differential is accentuated in zone 3 which is also exceptional for its large differential of 12 per cent in the proportion of household heads over 65 in the migration streams to and from Toronto. Data pertaining to the number of wage earners in each household corroborate the findings based on age and size of household data. Households which migrated from Toronto to the rest of the MTARTS region are over-represented among households with only one wage-earner, while households with no wage-earners and more than one wage-earner are under-represented. Zone 3 again is exceptional for its large proportion (15 per cent) of inmigrant households with no wage-earners. Differing proportions of in-migrant and out-migrant households with two or more wage-earners contribute to the larger proportion of such house241

holds in Toronto than in the rest of the urban MTARTS area (34 per cent and 28 per cent respectively), since the differential amounts to 14 per cent. On the basis of the age, wage-earner, and size composition of households which migrate from Toronto to the rest of the MTARTS region, it may be concluded that migration into the two inner zones represents an extension of the typical suburbanization processes often observed within metropolitan areas, but migration to the periphery of the region exhibits a somewhat different character. Zonal variations in these respects are greater than sectoral ones. In view of the primarily concentric pattern of the family status dimension revealed in Murdie T s (1969) factorial ecology of Metro Toronto, sectoral variations in the age and household size characteristics of migrant households would not be anticipated in the region surrounding Metro Toronto, given the pattern of migration within the region. Streams of migrants between Metro Toronto and the rest of the urban MTARTS region in both directions are over-represented in three occupational categories: managerial, professional and technical, and transport and communication. This selectivity is greatest and most consistent in the case of the managerial and professional and technical occupations—a reflection of the high mobility rates of these groups. The under-representation by the labourer and retired groups is also quite consistent among the seven zones and sectors. As expected from the previous discussion, the high proportion of retired migrants from Toronto to zone 3 is the only exception to the under-representation of this "occupational" group. Zone 3 again stands out by virtue of the low proportion of craftsmen in its migration exchange with Toronto in both directions, in spite of the fact that this zone's proportion of craftsmen is the highest of any zone. In view of the similarity in the occupational composition of the migration streams between Metro Toronto and the other urban MTARTS centres, the net effect of this exchange is small. In only one occupational category is there more than a 2 percentage point difference in the proportions of in-migrant and out-migrant households. This exception is the craftsmen category. While 27 per cent of the household heads which moved from Toronto to the rest of the urban MTARTS region are craftsmen, only 23 per cent of the reverse stream are in this category. This differential is largely established by migration to and from the east and southwest sectors. 242

Occupational differentials are small in zone 1, the largest being only 5 per cent in the professional and technical category, which also has the highest differential (11 per cent) in zone 2. These zones differ from zone 3, which has relative losses in both the managerial and professional categories, compensated by gains in the retired and craftsmen. In terms of the broad "white-collar" and TT blue-collar" categories the net effect of this redistribution is towards increasing proportions of white-collar workers in Metro Toronto and bluecollar workers in the rest of the urban MTARTS area, although the differential is a mere 3 per cent. This tendency is opposite that revealed in Stone (1969) for the 1956-61 intra-metropolitan exchange of males aged 25-64 in the labour force for all Canadian CMA f s together. Within the broader nurban field" of Toronto, then, migration differentials may have been working at odds with the tendencies within Canadian metropolitan areas. It is significant, however, that the decentralization of craftsmen, production process, and related workers is taking place at both the central city - ring scale and the Metropolitan Toronto urban field scale. Metro Toronto household incomes are biased towards the higher categories, while the lowest income categories claim a large proportion of zone 3 households. Zone 1 has higher proportions of the three income categories over $6,000, than zones 2 and 3. In the two classes over $8000, however, Metro Toronto exceeds all the zones and sectors. The most consistent characteristics of the streams of migrants into and out of Metro Toronto with respect to household income is the low proportion of low-income households (under $4000). Only among migrants from Toronto to zone 3 is there an overrepresentation of the lowest income category. This over-representation, however, is strong: 26 per cent of this stream of households has incomes under $4000, compared with only 7 per cent of those migrating to zones 1 and 2. For Metro Toronto, migration to and from the rest of the MTARTS region has contributed towards an increasingly dispersed income distribution of households, for relative gains are experienced among very low income (under $4000) and high income households ($8000-$12000 and over $12000), with losses especially in the $4000-$6000 category. Zone 3 exhibits considerable deviation from the other two zones in this respect, for a positive differential of 18 per cent in the under-$4000 class and negative 243

differentials of 15 per cent in the over-$12000 class and 14 per cent in the $8000-$12000 class clearly point to the lowering of incomes in zone 3 as a result of migration to and from Toronto. In zone 1 the largest positive differential is in the $6000-$8000 category, while in zone 2, this is in the $4000-$6000 class. The most peripheral suburban towns are developing into centres of lower socio-economic status than the more mature suburbs just beyond Metro Toronto!s boundary. Among the sectors, the north and east show the greatest tendency toward the $4000-$6000 group; the eastern one does not attract over $12000 households, while the northern and southwestern ones reject $8000-$12000 households. The western sector also gain most in the $4000-$6000 category, but differentials in this sector are smaller than elsewhere. On the basis of zonal and sectoral variations in the characteristics of households which have migrated from Metro Toronto, there is some evidence that their family status characteristics like those of Metro Toronto T s population, are distributed more in a concentric-zonal pattern, while economic status characteristics are in both cases distributed in a sectoral fashion. The evidence is crude, since no tests of significance or measurement of variability within zones or sectors have been carried out. Nevertheless, the role of migration in the extension of the pattern of the population characteristics of Metro Toronto into the surrounding region is in evidence. The overwhelming fact regarding migration in the region, however, is that all 44 categories of households considered have a net out-migration from Metro Toronto to the remainder of the MTARTS region during the period from 1959 to 19641 Although evidence of suburban selectivity according to many characteristics is in evidence, not a single type of household is immune from this decentralization process. Suburbanization by Metro Toronto residents takes place most rapidly among the young, mediumsized families, with pre-school children, and in the middleincome range. Migration between Toronto and zone 3. among all categories of households, plays a very minor role in establishing the characteristics of zone 3. Conclusions With respect to the gross pattern of migration in the region, perhaps the most dramatic conclusion is the overwhelming degree to which centrifugal migration from Metro Toronto exceeds the 244

centripetal stream. All parts of Metro Toronto have contributed to the population of the surrounding area, and Metro T s households have dispersed in substantial numbers to several centres beyond the boundary of the Census Metropolitan Area. Households which have migrated directly from Metro constitute at least five per cent of the households in centres as far removed as Sutton, Port Perry, Whitby, Orangeville, and Waterdown. However, Metro Toronto's contribution to the population of zone 3 as a whole is meagre, especially when expressed in terms of the net migration rate between Toronto and zone 3. Mobility rates are differentiated on the basis of both the basic demographic variables and socio-economic variables. These rates seem best discriminated by the status variables. In terms of household size, and age and sex composition, zone 2 most typifies the suburbanization process. Zone 3, the most peripheral zone, containing sizeable cities with their own economic bases, differs sharply from the other two zones both in the demographic and status characteristics of migrants exchanged with Metro Toronto, and in the strength of the net effect of this migration in volume alone. The family status characteristics of migrants from Toronto appear to be distributed zonally at the scale of the MTARTS region. The components of the typical socio-economic status dimension appear more sectorally distributed at this scale. Simultaneous control for zones and sectors offers a further possibility for investigation, although problems of small sample size would be intensified. A s a pioneer investigation of the character of migration at a scale approaching an urban field, this study demonstrates the impact of an important demographic process moulding the social geography of an urban-centred region.

References

BERRY, B. J. L.; GOHEEN, P. G. and GOLDSTEIN, H. 1968. "Metropolitan area definition: a re-evaluation of concept and statistical practice." Working Paper No. 28. Washington, D 0 C.: U. S. Bureau of the Census, GPO. CAROL, H. 1969. "Development regions in Southern Ontario based on city-centred regions." Ontario Geography 4; 13-29. 245

DEAN, W. G. , ed. 1969. Economic Atlas of Ontario. Toronto: University of Toronto Press. DOMINION BUREAU OF STATISTICS. 1965. 1961 Census of Canada. "Migration, fertility and income by census tracts." Bulletin CX-1. Ottawa: DBS. FRIEDMANN, J. and MILLER, J. 1965. "The urban field. " Journal of the American Institute of Planners 31: 312-19. HILL, F. I. 1971. "Migration in the Toronto-centred (MTARTS) region." Research Paper No. 48. Toronto: Centre for Urban and Community Studies. University of Toronto. HODGE, G. 1970. "A probe of living areas in the periphery of the Toronto urban field." Research Paper No. 30. Toronto: Centre for Urban and Community Studies. University of Toronto. MARSHALL, J. 1969. "The location of service towns: an approach to the analysis of central place systems." Research Publication No. 3. Toronto: University of Toronto Press. MURDIE, R. A. 1969. "Factorial ecology of Metropolitan Toronto, 1951-1961: an essay on the social geography of the city. " Research Paper No. 116. Chicago: Department of Geography. University of Chicago. RUSSWURM, L. H. 1970. "Development of an urban corridor system: Toronto to Stratford area 1941-1966." Research Paper No. 3. Toronto: Ontario Department of Treasury and Economics. Regional Development Branch. SIMMONS, J. W. 1968. "Changing residence in the city: a review of intra-urban mobility." Geographical Review 58; 622-51. STONE, L. O. 1969. Migration in Canada; some regional aspects. Ottawa: DBS. TRAFFIC RESEARCH CORPORATION. 1965. An analysis report on the 1964 home interview survey. Toronto: TRC. WHEBELL, C. F. J. 1968. "Net migration patterns 1956-1961 in Southern Ontario." Ontario Geography 2: 67-81.

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