2000115-Strengthening-Communities-with-Neighborhood-Data

Advances in Analytic Methods for Neighborhood Data 297
inaccessibility of such resources, and they can also be negatively affected
by their propinquity to environmental toxins, noise, disorder, and other
hazards. Although it may be relatively simple to quantify the resources
or hazards that lie within the boundaries of particular neighborhoods, a
more valid approach is to take distance into account in a more continuous
fashion. GIS tools have been successfully used to produce distance-based
measures that are useful for neighborhood indicators.
In order to craft spatially calibrated measures of access or exposure, it
is first necessary to consider the nature of the phenomenon in question
and how it is manifested in space. This understanding will guide a number
of methodological decisions that must be made. First, the data analyst
must decide how distances to resources or hazards are to be calculated.
Frequently, linear distances are used. However, if travel time is a concern,
it may be more appropriate to calculate distances along roadways
or public transportation routes. For some purposes a threshold may be
established, such as whether a resource is within a specified distance or
beyond a particular boundary. Also, a method of distance weighting must
be considered. For example, it is possible to give more weight to resources
that are closer to the neighborhood than to those that are farther away by
using weights that follow a distance decay function. Second, the spatial
granularity of the data will affect the precision of exposure or access indicators.
When data on locations are in an aggregated form (e.g., number
of resources in each census tract), an assumption will need to be made
about where to place the locations within the unit (e.g., at the centroid,
randomly distributed throughout the area, and so forth). Aggregated data
may also need to be weighted for the size of the aggregation unit so that
small units do not have undue influence on various calculations. Several
applications are discussed below that illustrate some of these alternative
specifications and how they fit the purpose of the analysis.
A study of access to mental health and substance abuse services in the
Detroit, Michigan, area illustrates the use of a buffer with unweighted,
aggregated count data (Allard, Rosen, and Tolman 2003). The researchers
built a geocoded database of all providers that served low-income
individuals in the metro area. Based on interviews with experts, they
established a 1.5-mile buffer as a definition of adequate service access.
For each census tract in the study, they calculated the number of providers
within the 1.5-mile buffer and standardized the score by dividing by
the mean count for all tracts. The service access scores were not adjusted
for competition (i.e., the number of individuals in the buffer eligible for