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9. The years 1990 and 2000 were selected for this exercise because the most data
were available for these years. However, to make the results more applicable, when the
typology is applied to a particular place, each neighborhood is assigned to a type based
on the most current data available.
10. Different factors tend to define different layers of the taxonomy. At the highest
level, a neighborhood’s type appears to be defined primarily by its housing stock, the
income of its residents, and the share of the population that is foreign-born. The next
differentiation happens based on the age of the population (which is likely related to
the preferences for different types of neighborhood amenities), land use patterns, and
business presence.
11. Income was chosen as a key dimension for two reasons: it is a very important
outcome from an economic development standpoint, and it plays a very important role
in determining neighborhood type.
12. The validity of a typology cannot be tested based on differences in the variables
used for the clustering, as those will, by definition, differ more across types than within
types. However, it is possible to test the validity of a typology based on differences in
variables not used for the clustering. If those variables are well differentiated across types
(as was the case for this typology), this indicates that the clustering surfaced truly distinct
neighborhood types.
13. As described in the previous section, the RSI was not a defining variable in the
typology. However, the RSI was included in the neighborhood profiles, and it was used
to validate the typology results by verifying that neighborhoods grouped together by the
typology also tended to have similar values in the RSI.
14. For the purposes of the DNT project, social capital was measured based on the
presence in the tract of selected types of organizations, including civic and social associations,
churches, and membership organizations. See the DNT final report for details.
15. These comparisons can also be drawn across time: since the typology includes
1990 observations, a neighborhood today could find other neighborhoods that were in
the same situation 20 years ago, and see what those neighborhoods did and how they
evolved.
References
Chipman, Hugh, and Robert Tibshirani. 2006. “Hybrid Hierarchical Clustering with
Applications to Microarray Data.” Biostatistics 7: 302–17.
McMillen, Daniel P., and Jonathan Dombrow. 2001. “A Flexible Fourier Approach to
Repeat Sales Price Indexes.” Real Estate Economics 29 (2): 207–25.
McWayne, Christine M., Paul A. McDermott, John W. Fantuzzo, and Dennis P. Culhane.
2007. “Employing Community Data to Investigate Social and Structural Dimensions
of Urban Neighborhoods: An Early Childhood Education Example.” American
Journal of Community Psychology 39: 47–60.
Weissbourd, Robert, Riccardo Bodini, and Michael He. 2009. Dynamic Neighborhoods:
New Tools for Community and Economic Development. Chicago: RW Ventures, LLC.
Available at www.rw-ventures.com/publications/n_analysis.php.