2000115-Strengthening-Communities-with-Neighborhood-Data

306 Strengthening Communities with Neighborhood Data
terms of the statistical power of these comparisons. And as with any
method, the analysis is strengthened by including appropriate covariates
that affect the outcomes. Known as the adjusted interrupted time
series method, this type of design has been applied in neighborhood
studies mainly to address the impact of various policies and practices
on housing values or crime because of the long time series that can be
developed from police and property records.
Localizing Outcomes in Space
When researchers apply experimental designs that have come out of
human subjects research to neighborhoods, they typically treat the neighborhood
units as nongeographic entities. As such, neither the location of
the neighborhoods relative to other places nor the geographic subareas
within them are taken into account. However, spatial concepts can be
used to craft more refined counterfactuals, especially when there is reason
to suspect that the impact of an intervention has some type of spatial
parameters. Increasingly, researchers use GIS tools and spatial locations
to calibrate where exposures and effects are occurring. Combined with
interrupted time series or comparison group methods, spatial analysis
can bolster the validity of the designs.
For example, a study of the impact of supportive housing developments
on crime rates in Denver, Colorado, neighborhoods drew buffers
of varying sizes around supported housing units and compared crime
rate trends in these zones with trends farther away (Galster et al. 2002).
Similarly, a study of the effect of dispersed public housing on home values
in Denver also examined sales prices around public housing units at
various geographic scales and compared them to areas without public
housing within similar distances (Santiago, Galster, and Tatian 2001).
In New York City, researchers examined the relative effect of housing
rehabilitation carried out by commercial and nonprofit developers on
sales prices. They also examined whether the differences between these
two types of development depended on the size of the buffer used in the
analysis (Ellen and Voicu 2006).
There is growing awareness that scale matters in neighborhoods and
that the effect of particular policies or practices may vary both within
and between so-called neighborhoods. A study in Seattle of the impact
of subsidized housing on nearby property values found that the effects
of the program were quite different across subareas, and these differ-