2000115-Strengthening-Communities-with-Neighborhood-Data

Advances in Analytic Methods for Neighborhood Data 311
unevenness in terms of data availability at the point level, with most
examples of point-identified data coming from housing or law enforcement
sources. Social and demographic data at the point level are hard to
come by because they are usually based on samples from surveys, such as
the American Community Survey. The advent of geographically enabled
mobile devices holds great promise for generating spatially granular data
on social relationships and activities that can be examined for their configurations
in time and space to inform the understanding of neighborhood
dynamics.
Third, it is time to have rising expectations for building the evidence
base for practices and policies that aim to improve neighborhoods and
benefit residents. This chapter gives examples of the clever application
of research designs that provide leverage on the problem of the counterfactual
and begin to provide greater confidence about the evidence of
impact. In addition, analyses that explore spatial variation in outcomes are
promising for yielding more nuanced understanding of program effects.
It is true there has been some discouragement about the ability to evaluate
comprehensive approaches to community change in total because they are
community driven and have numerous components. However, the ability
to rigorously evaluate limited and controlled innovations within existing
community programs is feasible and probably can be done at reasonable
cost if the outcomes can be evaluated using available data. Indeed, the idea
of low-cost randomized trials related to social spending is gaining traction
at the national level, and we can anticipate further development of the
methodologies to support such work in neighborhoods.
Fourth, the time is ripe to invest in analytic methods and data sources
that can contribute to a better understanding of the dynamic processes
that shape neighborhoods and the experiences of residents as they
traverse time and space. As pointed out in this volume by Galster and
Hedman in their discussion of residential selection and neighborhood
effects, research has tended to focus on isolating these processes rather
than modeling them holistically. However, data sources that are up to the
task of dynamic modeling are rapidly evolving, especially as more longitudinal
datasets are being created from administrative records or mobile
devices that capture locations at frequent or continuous time intervals.
Recent developments in computational social sciences, including data
mining, agent-based modeling, and simulation, are also promising and
need to be evaluated to determine how they can be applied in new ways
to examine questions of neighborhood impact and change (O’Sullivan