Redefining Neighborhoods Using Common Destinations: Social Characteristics of Activity Spaces and Home Census Tracts Compared

Abstract

Research on neighborhood effects has focused largely on residential neighborhoods, but people are exposed to many other places in the course of their daily lives—at school, at work, when shopping, and so on. Thus, studies of residential neighborhoods consider only a subset of the social-spatial environment affecting individuals. In this article, we examine the characteristics of adults’ “activity spaces”—spaces defined by locations that individuals visit regularly—in Los Angeles County, California. Using geographic information system (GIS) methods, we define activity spaces in two ways and estimate their socioeconomic characteristics. Our research has two goals. First, we determine whether residential neighborhoods represent the social conditions to which adults are exposed in the course of their regular activities. Second, we evaluate whether particular groups are exposed to a broader or narrower range of social contexts in the course of their daily activities. We find that activity spaces are substantially more heterogeneous in terms of key social characteristics, compared to residential neighborhoods. However, the characteristics of both home neighborhoods and activity spaces are closely associated with individual characteristics. Our results suggest that most people experience substantial segregation across the range of spaces in their daily lives, not just at home.

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Notes

  1. 1.

    Because people might reasonably travel a small distance beyond their destinations—for example, to go to lunch while at work—we also tested versions in which we added a buffer area of one-half or one-quarter mile around the minimum convex polygons. These buffers substantially increased the number of census tracts that were included but had very little influence on the average and range of characteristics.

  2. 2.

    Although one could include only the portion of the tracts that fall inside the polygons, this approach requires the assumption that population is evenly distributed within the tracts, which is not the case, particularly in rural tracts. Sensitivity testing reveals that eliminating tract portions that are outside the polygons has very little effect on the average or range of social characteristics reported here.

  3. 3.

    In calculating geographic areas, we used the coordinate system 1983 US State Plane California VI. We used the TIGER/Line map of 2000 census tracts provided by the U.S. Census Bureau (2000b).

  4. 4.

    Information on SPAs is available online (http://publichealth.lacounty.gov/chs/SPAMain/ServicePlanningAreas.htm).

  5. 5.

    Because we use census tracts to define nodes and because tract size is determined by population density, the total area of the nodes is highly dependent on the population density of the nodes. Therefore, a similar analysis of node total size is not informative.

  6. 6.

    To produce the figures, the predicted values of each outcome variable were calculated using the regression coefficients (shown in Table 5 and Online Resource 1, Table S1) and the means of independent variables, except for the independent variable under consideration, which is set at a specified value. For example, to calculate the percentage Latino in the activity spaces of African American respondents, all x-variables were set at their sample means except for the dummy variable for African American race/ethnicity, which was set to 1. For all predictions besides those for race/ethnicity, race/ethnicity was set to Latino (so the predicted values are for a Latino individual).

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Acknowledgments

This research was supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (Grants R01HD35944 and R01HD41486) and by the Russell Sage Foundation.

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Correspondence to Malia Jones.

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Jones, M., Pebley, A.R. Redefining Neighborhoods Using Common Destinations: Social Characteristics of Activity Spaces and Home Census Tracts Compared. Demography 51, 727–752 (2014). https://doi.org/10.1007/s13524-014-0283-z

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Keywords

  • Neighborhood
  • Activity spaces
  • Isolation
  • Segregation
  • GIS methods