2000115-Strengthening-Communities-with-Neighborhood-Data

Advances in Analytic Methods for Neighborhood Data 375
subsidies to target area firms end up reducing growth in similar nearby
firms). Another question focuses on the extent to which outcomes
within a target area depend on interactions with other areas given that
spatial target areas are open systems (i.e., they constantly interact with
other areas). For instance, do target areas with stronger economic linkages
to other areas perform better economically? Such linkages can pertain
to business networks or spatial infrastructure such as roads, public
transit, railways, or broadband that connect areas in physical or virtual
space. RW Ventures’s Dynamic Neighborhood Taxonomy is an example
of a tool to help contextualize neighborhoods and cities that are part of
place-based efforts within the broader regional economy. At a household
level, a question in this context is to what extent residents’ social
networks are defined by a target area and what the consequences are for
place-based program goals (Livehoods.org is an interesting recent application
to delineate neighborhood boundaries in near real time based on
social media use in neighborhoods).
The predominant focus in place-based approaches and evaluations
that have used spatial data (as in program evaluation generally) has been
on spatial heterogeneity, not on spatial dependence. Maps in this context
often visualize the extent of heterogeneity of key indicators within a target
area and how a target area compares with the region. Interpretation
of these maps often involves statements such as “as one can see, poverty
is concentrated in these areas.” In cases of strong concentration, such as
highly segregated cities, such statements will hold against a null hypothesis
of spatial randomness. However, in many common but less extreme cases,
people tend to overdetect clusters where none exist. In these cases, analytical
(as opposed to visual) spatial methods are helpful in distinguishing
spurious clusters from statistically significant ones. Hence this essay argues
for a broader application of spatial concepts and methods, beyond spatial
heterogeneity, to include spatial significance testing and modeling.
Spatial Data, Methods, and Tools for Evaluation
One of the key developments in evaluating place-based initiatives that
address wickedly complex problems has been a move away from measuring
the impacts of these initiatives within a causal framework designed
for single causal effects. Instead of asking the extent to which programs
caused an outcome, evaluation approaches emerged that ask whether