2000115-Strengthening-Communities-with-Neighborhood-Data

294 Strengthening Communities with Neighborhood Data
common variance can be assumed to capture an important latent construct,
then these weights make sense because higher weight is given to
those indicators most closely related to that construct. Another method of
establishing weights empirically is to estimate a statistical model in which
the weights for the indictors are derived according to an estimate of each
indicator’s contribution (e.g., regression coefficient) to some criterion that
is valued. Alternatively, it is possible to use public or expert opinion to
provide weights for indicators (Hagerty and Land 2007).
Indexes are particularly useful for describing small areas relative to
one another. Because they capture a broad construct rather than a single
indicator, they are often used to document inequality (Powell et al. 2007)
or to target resources to particular areas based on their relatively high
scores on an index of need. Applied in this way, indexes are not just
research exercises but have important consequences in the real world.
Yet the complexity of multidimensional indexes makes them sensitive to
the accuracy of the indicators that go into them and the methods used
to combine indicators into a single ranking or score. Relatively small
changes in methods or assumptions may yield quite different rankings,
and mistakes in this regard can produce misleading results. For example,
some neighborhood rankings that appear in the popular media combine
multiple indicators without sufficient information on the details of how
they are produced. It is incumbent on those who use such rankings to
demand transparency with respect to the analytic methods underlying
them and the evidence for validity of the resulting indexes.
In contrast to multidimensional indexes, neighborhood typologies
use multiple indicators to classify places into types, and neighborhoods
are differentiated categorically rather than along a continuum. Typologies
are formed through multivariate classification schemes, so that the
neighborhoods within each type share key attributes and differ on those
attributes from neighborhoods of other types. Simply put, typologies
identify mutually exclusive groups of entities that are more similar within
the groups than between the groups. Unlike multi-indicator indexes that
attempt to rank neighborhoods along some continuum, neighborhood
typologies can be thought of as categorical.
Typologies are particularly useful for revealing the intersection of
social and economic factors that shape the differentiation among neighborhoods.
Although neighborhood types are by definition somewhat idealized,
they serve to synthesize a multidimensional set of differences into
a smaller number of groups based on the observed patterns in the data.