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box in MatLab (Liu and LeSage 2010) also contains spatial econometric
estimators in addition to spatial exploratory tools.
A key potential contribution of these cross-sectional linear models is
that they allow for the measurement of the correlation between contextual
factors and outcome variables, an ability that can inform the question
of whether a program contributed to an observed change in a community.
However, an important research gap exists in space–time model specifications,
estimators, and tools that more comprehensively account for
differences in outcomes before and after programs start to operate. This
gap includes spatial estimators for difference-in-difference–type models.
Existing approaches add so-called spatial fixed effects, that is, spatially
lagged independent variables (Wx) are added to a standard differencein-difference
specification (Galster et al. 2004; Ellen et al. 2001). However,
there are some methodological problems with this approach (Anselin and
Arribas-Bel 2011). A few examples of other recent spatial approaches relevant
to evaluations include spatial seemingly unrelated regression models
that account for spatial dependence by incorporating either a spatial
lag or spatial error term (Anselin 1988). When time periods are pooled so
a model of outcomes and related factors can be estimated before and after
an initiative started, then a test (Wald) can be applied to assess significant
differences in estimates for the two time periods. However, selection
effects are often not accounted for here. In a randomized controlled trial
context, Bloom (2005) advanced solutions for measuring program impacts
in group randomization designs in which spillover effects between the same
or different outcomes are present. An example of integrating spatial concepts
with a classic quasi-experimental design is Fagan and MacDonald’s
(2011) 15 incorporation of information about locations, neighboring areas,
and time in their regression discontinuity design. Kim (2011) 16 included
spatial criteria in propensity score matching that was then used to evaluate
a place-based crime prevention program in Seattle at the street segment
level. See Walker, Winston, and Rankin (2009) for an alternative
matching methodology with some spatial dimensions.
Conclusion and Outlook
I have argued that existing spatial concepts, methods, and tools can add
value to the present use of geographic data in place-based evaluations, especially
by considering the implications of MAUP and spatial dependence