2000115-Strengthening-Communities-with-Neighborhood-Data

Advances in Analytic Methods for Neighborhood Data 321
good example is the one developed by The Reinvestment Fund (TRF),
which constructed a typology of Philadelphia neighborhoods based on
their real estate market characteristics, linking different types of neighborhoods
to different types of housing interventions. 3 This typology
reduced data on hundreds of thousands of properties to a manageable
number of neighborhood types and helped Philadelphia’s government
prioritize interventions and better target its resources.
Several marketing and data companies have created neighborhood
typologies of some sort by developing household segmentations based
on consumer patterns, for the purposes of targeting product marketing
and store locations. The PRIZM segmentation developed by Claritas,
Inc., for example, defines the US market via 66 lifestyle groups characterized
by different spending patterns. It then classifies neighborhoods
based on their composition in terms of these segments. Academics and
researchers also have developed numerous typologies of neighborhoods
over the years, either as descriptive exercises or for analysis of particular
phenomena. 4
In fact, since the output of a typology depends entirely on what factors
are used as inputs for the clustering algorithm, an infinite number
of neighborhood typologies can be created, and none of them is necessarily
more right or wrong than the others. Rather, typologies can only
be evaluated in terms of how useful they are for the purposes for which
they were developed. In this respect, existing neighborhood typologies
have useful applications, but for various reasons none of them addressed
the broader economic development issues tackled by the DNT project:
they are often local (as in the case of the housing typology in Philadelphia)
or based on a particular aspect of neighborhoods because they were
designed to address a specific issue (consumer preferences in the case of
PRIZM, housing investment in the case of TRF, and so forth). Many other
existing typologies often end up being simpler descriptive exercises; they
tend to present a static picture and are not grounded in an analysis of
neighborhood dynamics and what drives neighborhood change.
The typology presented here was designed to help inform a broad
range of community and economic development interventions, building
upon the neighborhood analysis conducted by the DNT project. As such,
it has some distinctive features that differentiate it from other neighborhood
typologies. For example, it is multidimensional and grounded
in the DNT project analysis of patterns and drivers of neighborhood
change. By incorporating many of the factors that proved to make the