2000115-Strengthening-Communities-with-Neighborhood-Data

292 Strengthening Communities with Neighborhood Data
approximation and better address the problem of unreliability. The
value of considering distance was demonstrated in a study that compared
survey-based neighborhood measures using empirical Bayes estimates
that were nonspatial to those that took spatial contiguity into account
(Savitz and Raudenbush 2009). Using survey data from the Project on
Human Development in Chicago Neighborhoods, the researchers selected
only a small subsample of the cases to simulate a situation of sample
sparseness. A first-order contiguity matrix was used as the model for spatial
dependence. Applying a two-level spatial hierarchical linear model to
a measure of collective efficacy, they demonstrated that the neighborhood
estimates using spatial dependence have less error and more predictive
validity than do unadjusted estimates or nonspatial empirical Bayes estimates.
Mujahid et al. (2008) also provide a straightforward illustration
of how spatial dependence can be leveraged to improve the reliability of
census tract measures based on sample surveys using GeoDa, a spatial
data analysis software package.
Too often, neighborhood indicators are based on sparse sample estimates
that may be unstable and can be misleading in practice if they are
taken at face value. Placing a confidence interval around the estimates is
one way to communicate such uncertainty when simple description is the
aim. However, for cross-neighborhood comparisons, analysis of trends,
or more complicated studies that attempt to uncover relationships among
neighborhood measures, the unreliability of the sample-based estimates
can attenuate the findings. Shrinkage estimates developed using spatial
analysis software can provide more reliable metrics for descriptive and
comparative purposes.
Combining Indicators for Multidimensional
Metrics and Classification
Communities vary in innumerable ways, making single indicators of limited
usefulness for research and planning. But the presentation of numerous
individual indicators at once can be unwieldy and difficult to interpret,
especially if communities are not in the same rank order on all of them, or
if the indicators fall into several, perhaps overlapping, domains. In addition,
combinations of indicators may be stronger or more accurate predictors
of community needs or outcomes than single indicators. However,
it is not a simple matter to combine indicators so that communities can
be compared or classified. Two areas in which there have been important