Case Study:

Orange County, California

Application

Disaggregating Socioeconomic Data

Socioeconomic data are vital for determining locations for transit routes and facilities. Often, socioeconomic spatial data reside in a zonal layer - census tracts or transportation analysis zones (TAZs), for example - where each zone contains such attributes as population, housing units, and employment. A single zone in such a layer may cover a very large area. While socioeconomic data aggregated to these large zones are useful for regional analyses, they can support only a relatively crude analysis of accessibility to individual bus routes and bus stops. OCTA recognized this problem in analyses using current socioeconomic data at the census tract level. To solve the problem, OCTA used GIS tools with land-use information to disaggregate the census data.

To accomplish the disaggregation, OCTA overlaid the census tract layer shown in Figure 3 onto the layer shown in Figure 4 representing small-area land use. The land use layer has a higher resolution of detail since each feature in the layer is a two- to three-acre area that is encoded with a specific land use class.

Figure 3. Population Density by Census Tract

Figure 4. Orange County Land Use Map

The approach to disaggregation is easily illustrated with a larger-than-normal tract with a moderate-sized population that is concentrated in a few contiguous blocks. Computed at the tract level, the average population density understates the population density - and potential transit market - that exists within a portion of the tract. GIS tools can produce a better representation of the population distribution by tapping information from the land-use layer on actual locations of housing within the tract. The resulting estimate of population locations reveals that most residents of the tract are located in high-density housing in a compact subarea of the tract. Tracts and land use can be combined to determine a more accurate spatial distribution of population within the tract. This is done by dividing the total population of the tract by the land area within the tract devoted to residential use, to obtain a revised estimate of population density. This density is then assigned to the residential area, while non-residential areas are assigned a population density of zero. Figure 5 illustrates the resulting layer that more accurately represents population distribution and density within tracts.

Figure 5. Refined Population Density Map

The same approach can be applied to zone-level employment data as well by using GIS to link it spatially with data on the location and extent of office, commercial, and industrial land uses in each zone. The more precise distributions of population and employment are much more useful to transit planners in efforts to locate bus routes and bus stops to serve areas of high population and employment. Thus, employing the automated spatial analysis utilities of a GIS, OCTA uses multiple map layers to produce a less aggregated and more spatially accurate layer.

GIS can also be used to perform the reverse of this process by aggregating data associated with smaller areas into a larger zone. In general, one of the most useful capabilities of GIS is the manipulation of data that is rich in attributes across different levels of aggregation for use in various analyses.

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