2000115-Strengthening-Communities-with-Neighborhood-Data

Advances in Analytic Methods for Neighborhood Data 295
Indeed, since it is often a combination of factors that interacts to advantage
or disadvantage particular communities relative to others, typologies
are a useful starting point for studying the dynamics of urban structure or
the impact of place on the well-being of the population. Such classifications
serve many practical purposes as well, such as describing patterns
of change over space and time and tailoring public policy decisions to the
unique conditions of places that are of different types.
Meaningful classification of neighborhoods is a data- and analytically
intensive exercise. The indicators used in the investigation are determined
by the purpose of the typology and relevant theories about the processes
driving the differences among places. The variables selected for inclusion
represent relevant distinctions among places. Though each neighborhood
has a unique profile on the set of variables, the goal of the analysis is
to find a smaller number of groupings that account for the patterns in the
data. With even a few variables, the number of possible profiles becomes
impossibly large, so it is necessary to apply analytic methods to uncover
meaningful patterns of similarities and differences.
Cluster analysis has been widely used in urban affairs to classify cities,
suburbs, and rural areas for a variety of purposes (Mikelbank 2004).
This technique has also been applied to the classification of small areas
such as communities and neighborhoods. (Cluster analysis to identify
types should not be confused with spatial clustering methods, which are
discussed in another section of this chapter.) Chow (1998), for example,
found that four clusters adequately differentiated among neighborhoods
in Cleveland, Ohio, on 10 social problem indicators. Stable neighborhoods
had low scores on all social problems. Transition neighborhoods
were beginning to show health problems but had low rates of other problems.
Distressed areas had economic problems but still remained relatively
safe, and extremely distressed neighborhoods had high rates on all
social problems.
A typology generated through cluster analysis of Chicago neighborhoods
was found to be useful in a study of Chicago’s New Communities
program (Greenberg et al. 2010). (This initiative is also discussed in
chapter 5.) The cluster analysis, which used indicators of neighborhood
demographics and economic conditions at the start of the program,
revealed five types of neighborhoods. This typology was helpful for interpreting
early program results because the clusters were used to identify
similar types of neighborhoods for comparison on trends in quality-oflife
indicators such as crime and foreclosure rates.