2000115-Strengthening-Communities-with-Neighborhood-Data

Advances in Analytic Methods for Neighborhood Data 379
A different approach to finding clusters is based on so-called spatially
constrained clustering algorithms (Duque, Anselin, and Rey 2012).
This approach uses optimization methods to group spatial units with
similar characteristics into new regions. An application of this method
to measure neighborhood change based on changing boundaries and
social composition was highlighted in the MAUP discussion. The
clusterPy library (GeoGrouper with a graphical user interface) and
PySAL’s regionalization code implement these methods.
Cluster maps can be linked to other spatial and nonspatial representations
of data (such as parallel coordinate plots, scatterplots, conditional
plots, histograms, or trend graphs) in open-source desktop programs
such as OpenGeoDa, web platforms such as Weave (WEb-based Analysis
and Visualization Environment), or through customizing visualization
libraries. 11
Spatial Modeling
The cluster techniques discussed in the previous section are exploratory
in the sense that they generally only allow for spatial pattern detection
in a uni- or bivariate context or when multiple variables are considered
together; this pattern detection is done visually rather than statistically.
As such, these techniques are best suited for generating rather than testing
hypotheses. To control for a larger number of variables in a model
that tests hypotheses related to an underlying theory, multivariate spatial
models are needed. If the goal is to model spatial heterogeneity (i.e.,
spatial variation among regions whose similarity is not due to interaction),
specialized spatial econometric methods are not needed. Discrete
differences between subregions are often modeled through so-called
spatial regimes with separate coefficients that can be tested for equivalence
with spatial Chow tests (Anselin 1988). Further, geographically
weighted regression has been popularized by Fotheringham, Brunsdon,
and Charlton (2002) to estimate continuous forms of spatial variation
through locally varying coefficients.
However, in the context of spatial dependence, an intersection
between the fields of econometrics, program evaluation, and spatial
analysis continues to be missing. This lack persists despite the fact that
recent research has started to strengthen the bridge between econometrics
and program evaluation and the older existing connection between
the fields of statistics and evaluation. 12 One of the few research projects