Introduction
This chapter presents an annotated collection of maps from the co-editors’ research in order to create another less linear portrait of the in-between city. The intent is to complement the essays in the collection, to build on the insights of their case studies, and to emphasize the particular spatiality of the in-between landscape. In several thematic map series, we have charted 2001 census data by census tract. (Census tracts are stable geographic subdivisions in cities of 50,000 or more; tract populations normally range between 2,500 to 8,000, and efforts are made to delineate them with respect to landscape features and socio-economic homogeneity.)
These maps further document the argument explored throughout this collection that although infrastructure is not deterministic, it possesses a dynamic relationship with the social and economic geography of the city. On the face of it, this relationship should not surprise us: economic development necessarily figures in the planning of public infrastructure, and, if social goals are not explicitly embedded in the planning rationale, the social geography of the city nonetheless underlies that process in some form. Unfortunately, there exist many examples of infrastructure development that disregards the social geography it disrupts. Work in environmental justice has testified to the construction of large-scale (often polluting) infrastructure, such as power plants or highways, in residential areas that are poor or racialized (Camacho, 1998; Pulido, 2000; Shrader-Frechette, 2002). In a parallel fashion, other scholars have documented the ability of citizens from wealthier neighbourhoods to organize and successfully challenge similar projects from being built near their homes or to prevent the loss of socially valuable institutions, such as schools (Basu, 2006; 2007).
By no means is the relationship between infrastructure and the social landscape a simple one. Indeed, what this collection of maps reveals is the complexity of the in- between city’s social geography. There are some indications in the maps of the difference between public and private or privatized infrastructure: the varying degrees of accessibility, the social and economic landscapes enabled by that access or lack thereof, social divides becoming further entrenched spatially. Above all, the maps detail the ways in which this place refuses simple definition or classification of landscape, and they hint at the processes that created and continue to shape the in-between city.
A Cartographic Portrait of the In-Between City 31
Context and limitations
It is important to situate this annotation of the maps within a larger discussion of cartographic representation. Maps are generated within particular social and political contexts, from which they cannot be severed. The geographer J.B. Harley (1989) wrote at length about the history of cartography, “how the 'rules of the social order' appear to insert themselves into the smaller codes and spaces of cartographic transcription” (6). Harley argued for the intentionality and purpose of maps, equating them with any other “text”:
I prefer to align myself with Foucault in seeing all knowledge—and hence cartography—as thoroughly enmeshed with the larger battles which constitute our world. Maps are not external to these struggles to alter power relations. (Harley, 1989, 14)
Maps do not “speak for themselves” (see Gregory, 1993). Harley argued maps were more akin to art than science in that each represents a view rather than establishing the view. They do assume an enormous authority and wear a mask of objectivity; we are obliged to deconstruct these. As Harley critically noted, “we often tend to work from the premise that mappers engage in an unquestionably ‘scientific’ or ‘objective’ form of knowledge creation.... It is better for us to begin from the premise that cartography is seldom what cartographers say it is” (Harley, 1989, 1).
Maps can be manipulated in many ways, and, historically, have been put to work in support of specific political agendas (Bassett, 1994; Godlewska, 1995; Monmonier, 2001). The advent of more sophisticated forms of mapping through computer programmes equipped to handle enormous amounts of data illustrated through elaborate and vivid graphics (as our own maps are) has made the need to regard maps with a critical eye all the more important. For despite the advances in critical cartography, “the scientistic rhetoric of map makers is becoming more strident” with advances in GIS (Geographic Information Systems), according to Harley (1989, 2).
Maps are fundamental to the surveillance and governance of the populace by the state (Anderson, 1983; Monmonier, 2002) and can themselves serve as a form of surveillance (Basu, 2005). They are never neutral representations. Explicitly and implicitly, maps selectively reveal and conceal and thus build an argument concerning the area. Inclusion of certain features testifies to their importance; exclusion of other features diminishes their significance and may even justify incursion into (or control of or redevelopment of) apparently “empty” spaces (Bassett, 1994). How we see the places charted on maps has many social, cultural, and political implications (Basu, 2005; Harley, 1989).
In addition to the above considerations and caveats about cartography more generally, our maps (and their supporting data) have their own particular limitations. First of all, the data are spatially bounded according to census tract. Census data themselves are subject to scrutiny for their definitions and categorizations and because of the possible error rate in data collection due to sample size or even to improperly completed forms (Carr-Hill, 1993; Monmonier, 1996; Wright, 1993). Census tracts are determined largely for the purposes of distributing the population into numerically equivalent groupings, but they are not impervious to political machinations (Monmonier, 2001). More important for our purposes here, census tract lines are relatively oblivious to social or cultural groupings, such as neighbourhoods or communities. Thus, mapping according to census tract data may suggest clustering that is stronger or weaker than it appears. For example, two ethnic communities of approximately equal size may live in the same area, with one group’s population falling entirely within a census tract while
32 Patricia Burke Wood and Robert Fiedler
the other’s is split among two or three tracts. The latter group would appear smaller and more dispersed than the former.
Census tracts respect municipal boundaries, but, in our maps, those political divisions are intentionally indistinguishable from other tract boundaries. Our study area encompasses more than one municipal district. In our approach to the in-between city, however, we prefer to emphasize the roughly contiguous landscape of the in- between city that sprawls from one municipality to the next, creating a new kind of centre at the margins.
To make the data legible and accessible on the maps themselves, we have grouped the data primarily according to natural breaks and have represented the results on choropleth maps. Natural breaks employ statistical calculations to determine what are considered to be optimal groupings of information in that they cluster similar values and maximize the difference between groups. Mapping those clusters on choropleth maps dramatically distinguishes one from another and makes them visually effective. However, choropleth maps are easily manipulated and may also serve to hide the other ways the data could be classified (Monmonier, 1996).
The maps
Our choropleth maps argue that there is a significant socio-economic divide between the north and south portions of the map, where Steeles Avenue is the dividing line. Below Steeles, the landscape is intensely “in-between,” with a great mix of residential, commercial, industrial, and other institutional uses (such as York University), criss-crossed by several major transportation corridors. Above Steeles, the in-between city begins to give way to more “typical” suburban development as we move north away from the rail yard (which creates a very large census tract). Steeles Avenue also constitutes the political boundary between the City of Toronto and York Region. There are other patterns to discern as well, but the north-south divide asserts itself again and again as the maps shift from representing population density, diversity, poverty, family size and form, types of housing, employment, and education. There is also a significant difference between the degrees to which the use of space is mixed. Much of the lack of mixed use is due to the enormous scale of certain land uses, such as the rail yard (see Fiedler, in this volume).
Map Series B: There is a clear difference in the age and type of housing above and
below Steeles Avenue. The housing north of Steeles is newer, with most of the residences built in the past 20 to 30 years. In the north, housing stock is also, on average, larger (with more bedrooms), even though there is not a significant difference in household size across the region. In Map Series B, more detailed information is available about the type of buildings within the housing stock. There is a greater percentage of single, detached housing above Steeles Avenue, while high-rise apartment towers are significantly more common south of Steeles. It is also worth noting, however, how mottled the southern portion of the map is. In this region, there is a great variety of housing type within a relatively small area.
Map Series C: In a map series of education and employment data, we see two spatial
patterns emerge: the usual north-south divide but also an east-west distinction when it comes to level of education. The higher number of persons with a university degree in the eastern portion of the area may be a correlation between income and proximity to York University. The rate of unemployment is distinctly higher south of Steeles Avenue.
A Cartographic Portrait of the In-Between City 33
Map Series D: Household data regarding families and children are more mixed. There are households across the area with children, but married couples with children are more prevalent north of Steeles Avenue while one-parent families are more common south of Steeles. There are more multi-family and non-family households south of Steeles, as well as a significantly higher number of senior citizens, which may suggest intergenerational households as well.
Map Series E: Data regarding renter households show one of the starkest distinctions between north and south in the study area. The vast majority of renter households are south of Steeles Avenue, where rentals also dominate the housing stock in many parts of that area. What rental housing there is north of Steeles leases for a higher-than-average monthly rate.
Map Series F: This series of income data maps documents a sharp contrast between
the prosperity north of Steeles Avenue and the mix of middle-class and poor households south of the avenue. The census measures income several ways, and multiple variables have been mapped here. In addition to mapping the data in whole numbers, the data have also been retabulated to show relative position, indicating where a given census tract stands relative to the average.
Map Series G: The patterns of immigrant settlement in the area are not as stark as
those for other socio-economic characteristics. Although it is clear that more recent immigrants are much more likely to live south of Steeles Avenue, as are visible minorities, immigrants are dispersed throughout the area. Rates of formal Canadian citizenship are high across the area but strongest in the tracts with high levels of single, detached housing, higher household income, and the lowest proportions of visible- minority communities.
Map Series H: The geographies of work and transportation reveal significant differences between north and south in the study area as well. In the north, most people work outside of their residential area and drive to their place of employment by private car. Below Steeles Avenue, residents are slightly more likely to work within their census subdivision or CSD (though many do not) and to travel to work by foot, bicycle, public transit, or as a passenger in someone else’s car. Another series of maps breaking down this data by gender shows that these spatial patterns are similar in terms of the north-south break but also that women are relatively more dependent on other forms of transit than driving one’s own private car. Significantly, more women are reliant on public transit than men, particularly south of Steeles Avenue.
Map Series I: There is a high degree of religious diversity in the study area. Each
religious group has a strong clustering in a given census tract or adjacent tracts, though most are represented in small numbers elsewhere in the area as well.
Map Series J: The ethnic background of the residents in the study area is similarly
diverse, with similar clustering of particular groups—although we want to stress that, unlike for religious affiliation data, clustering of an ethnic group does not necessarily represent an ethnic neighbourhood or “community.” The very definition of ethnicity in the census is various and unstable as well: it is measured by self-identification, national origin, and language, among other factors. What is additionally striking about the ethnic clustering in the study area is that “visible minorities” have a stronger presence in the census tracts south of Steeles Avenue than in the northern part of the area.
46 Patricia Burke Wood and Robert Fiedler Conclusion
There exists great diversity within the in-between city, by every measure. For every variable mapped within our study area, we see great variation. Along every axis of identity, land use, and economic status, we find juxtapositions of difference in close proximity. This “diversity” often constitutes uneven development of and investment in the region.
Broadly speaking, the maps from our research project illustrate two constellations of different clusters of socio-economic characteristics. These constellations, in turn, reflect and generate different kinds and degrees of access to all types of resources. Some of the difference is attributable to the usual patterns of suburban growth, but when we incorporate the age of residential construction, another possibility emerges. The single, detached housing in the study area, particularly at the edges, was built more recently than most of the other housing. The in-between city predates this construction. This construction, then, participates in the fragmentation of the area as a whole; it is a process of enclaving certain populations within the area. From a socio-economic perspective, the area continues to fragment within, despite the forces of convergence that build up the area.
These clusters, however tightly packed they may be in certain areas, are part of a highly fractured pattern. Mapping several variables at the level of census tract highlights the importance of this relatively micro-scale level of analysis in understanding the splintered in-between city: these neighbourhoods lie right next to each other—and yet stand worlds apart.
A Cartographic Portrait of the In-Between City 47
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