2000115-Strengthening-Communities-with-Neighborhood-Data

376 Strengthening Communities with Neighborhood Data
how programs actually work matches the expectations of how and why
they are supposed to work [popularized in the theory of change (Weiss
1995), which itself is seen as emergent (Patton 2011)]. Here causal attribution
of program impacts is replaced by “contribution analysis” (Mayne
2001), in which the question is whether program investments are correlated
with community change. 2 Recent research on neighborhood effects also
addresses the problem of multiple connected causes through a theory
of “contextual causality” (Sampson 2012). 3 This shift toward contribution
analysis has implications for spatial data analysis because it implies
a greater emphasis on, for instance, tracking indicators of contextual
factors and identifying statistical correlations between program outputs
and community outcomes rather than seeking to isolate causal connections
between outputs and outcomes.
As methods vary by data type, an overview of spatial data types is followed
by a brief discussion of selected spatial methods and tools that are
relevant to place-based evaluations.
Spatial Data
Classic taxonomies of spatial data distinguish between area data, event
points, spatially continuous data, and spatial interaction or flow data
(Bailey and Gatrell 1995). 4 Area data consist of discrete areas with attribute
variation between areas or discrete points (see the MAUP section
above). A common way to define which areas are neighbors is a so-called
spatial weights matrix (often identified as W in equations), which identifies
neighboring areas for a given area based on connectivity criteria,
such as queen border contiguity, nearest neighbors, or bands of network
distance. Event points are points whose location is subject to uncertainty
(such as crimes). Bandwidths around points are often used to define
neighboring points. Spatially continuous or geo-statistical data describe
phenomena that are continuously distributed over space and are measured
at sample points (such as contaminated soil). Spatial interaction
or flow data characterize movement between an origin and a destination,
such as in social networks or walkable access to school. See Radil, Flint,
and Tita (2010) for an application and LeSage and Pace (2010) for spatial
econometrics methods for flow data.
The focus in this essay is on area data, because much socioeconomic
data are collected for fixed administrative units (such as census tracts).
In this context, spatial econometric methods have been developed to