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. 2011;32(3):334-359.
doi: 10.2747/0272-3638.32.3.334.

Identifying and Bounding Ethnic Neighborhoods

John R Logan  1 , Seth Spielman, Hongwei Xu, Philip N Klein
Affiliations

Identifying and Bounding Ethnic Neighborhoods

John R Logan et al. Urban Geogr. 2011.

Abstract

This study presents three novel approaches to the question of how best to identify ethnic neighborhoods (or more generally, neighborhoods defined any aspect of their population composition) and to define their boundaries. It takes advantage of unusual data on the residential locations of all residents of Newark, NJ, in 1880 to avoid having to accept arbitrary administrative units (like census tracts) as the building blocks of neighborhoods. For theoretical reasons the street segment is chosen as the basic unit of analysis. All three methods use information on the ethnic composition of buildings or street segments and the ethnicity of their neighbors. One approach is a variation of k-functions calculated for each adult resident, which are then subjected to a cluster analysis to detect discrete patterns. The second is an application of an energy minimization algorithm commonly used to enhance digital images. The third is a Bayesian approach previously used to study county-level disability data. Results of all three methods depend on decisions about technical procedures and criteria that are made by the investigator. Resulting maps are roughly similar, but there is no one best solution. We conclude that researchers should continue to seek alternative methods, and that the preferred method depends on how one's conceptualization of neighborhoods matches the empirical approach.

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Figures

Figure 1
Figure 1
Geocoded information for a section of Newark, NJ in 1880
Figure 2
Figure 2
K-functions for a Yankee building in Newark
Figure 3
Figure 3
K-functions for a Yankee building in Newark
Figure 4
Figure 4
K-functions for a German building in Newark
Figure 5
Figure 5
K-functions for an Irish building in Newark
Figure 6
Figure 6
Ethnic neighborhoods identified through local k-functions
Figure 7
Figure 7
Illustration of cost calculations in energy minimization
Figure 8
Figure 8
Map of input data for energy minimization
Figure 9
Figure 9
Map of results from energy minimization
Figure 10
Figure 10
Initial values and posterior medians of types of street segments for Yankees in a section of Newark, 1880
Figure 11
Figure 11
Combined Bayesian classification of street segments for Irish, Germans, and Yankees in Newark 1880
See this image and copyright information in PMC

References

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    1. Bailey Trevor, Gatrell Anthony. Interactive Spatial Data Analysis. Englewood Cliffs, NJ: Prentice Hall; 1995.
    1. Bobo Lawrence D, Oliver Melvin L, Johnson James H, Jr, Valenzuela Abel. Prismatic Metropolis: Inequality in Los Angeles. New York: Russell Sage Foundation; 2000.
    1. Boykov Yuri, Veksler Olga, Zabih Ramin. Fast Approximate Energy Minimization via Graph Cuts. IEE Transactions on Pattern Analysis and Machine Intelligence. 2001;23:1222–1239.

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