2000115-Strengthening-Communities-with-Neighborhood-Data

Advances in Analytic Methods for Neighborhood Data 371
MAUP has several implications for the analysis of data in evaluations
of place-based initiatives. For one, it highlights the potential dependence
of mapping and statistical results on the areal units chosen. Even if target or
neighborhood boundaries are bound by community preferences and/or
the availability of other sources (such as census boundaries, Zip Codes,
school districts, or police beats), it is important to be aware of MAUP
when there are choices about how existing units are grouped or aggregated.
This awareness may become more relevant as large margins of
error associated with key neighborhood-level poverty-related estimates
of the American Community Survey make indicators from alternative
address-based sources with flexible aggregation options (such as pointbased
sales prices) more attractive. As more address-level data become
available, alternative spatial units of analysis also become available as
options to consider. For instance, in crime- and transit-related analyses,
points have recently been aggregated to street segments to obtain more
spatially focused results (Weisburd, Groff, and Yang 2012). In one such
analysis, Weisburd, Morris, and Groff (2009) found that 50 percent of all
juvenile crimes in Seattle were committed on less than 1 percent of street
segments. Such a refinement helps target intervention efforts better than
the standard kernel density hotspot maps with vague boundaries.
MAUP also raises the questions of if and how a scale mismatch (i.e.,
a mismatch in boundaries from different data sources) might influence
results, and how this problem can be addressed methodologically (see the
section on spatial modeling). Substantively, these questions are related
to the fact that the dynamics that influence local poverty concentrations
operate at different scales. For example, housing and labor market dynamics
are regional or larger, but school district outcomes might be more
local. Weissbourd, Bodini, and He (2009, 2) estimate that over one-third
of neighborhood change is related to trends at a larger regional scale. Two
related questions are what other spatially targeted efforts are underway
for a particular target area and how these efforts might affect the processes
and outcomes of the evaluated target area. Examples include health
service areas, neighborhood planning zones, business improvement districts,
transportation plans, and consolidated planning areas.
MAUP is also relevant in the analysis of neighborhood effects, which
can be thought of as an analysis in which one scale (community outcomes)
influences another (individual outcomes). How to delineate and measure
neighborhoods in this research can have important impacts on results.
A good example in this context is the federal Moving to Opportunity