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Census tract ethnoracial diversity does not always lead to block-level diversity (see below):
Census Tract 2037.01, Block Diversity
Census Tract 5700.02, Block Diversity
Changing Ethnoracial Diversity in SW Los Angeles County from 2000 to 2010 (see below):
LA County (SW) Ethnoracial Diversity, 2000
LA County (SW) Ethnoracial Diversity, 2010
Can New Urbanism Create Diverse Communities?
Can New Urbanism Create Diverse Communities?
This article examines the differences in diversity between a new urbanist subdivision and a standard suburban subdivision. We examine diversity at the macro level in terms of demographic characteristics such as age, sex, and income. We then examine diversity at the micro level, using a network sample to analyze diversity at the level of the social interaction. We find at the macro level that the new urbanist community is more diverse in a number of factors including education, income, and especially age. However, we find this increased diversity does not translate to diverse social interactions at the micro level.
NUS = New Urbanist CommunitySSS = Standard Suburban Community
Figure 8 indicates the differences in variation in term of age between and NUS and an SSS community. The distributions suggest the NUS has substantially more variability in ages than does the SSS. Generally, the NUS has a more even distribution across all age ranges over 20 years, while the SSS is more concentrated in the 20 and 30 year age range.
Figure 8 indicates the differences in variation in term of age between and NUS and an SSS community. The distributions suggest the NUS has substantially more variability in ages than does the SSS. Generally, the NUS has a more even distribution across all age ranges over 20 years, while the SSS is more concentrated in the 20 and 30 year age range.
Cabrera & Najarian (2013). "Can new urbanism create diverse communities?" Journal of Planning Education and Research, 33 (4): 427-441.
Spatially-Based Rules for Reducing Multiple-Race into Single-Race Data
Spatially-Based Rules for Reducing Multiple-Race into Single-Race Data
There is a discord between the categorization of mixed-race data in spatial studies, which has become more complex as the mixed-race population increases. We offer an efficient, spatially-based method for assigning mixed-race respondents into single-race categories. The present study examined diversity within 25 Metropolitan Statistical Areas in the United States to develop this racial bridging method. We identify prescriptions for each two-race category based on average diversity experiences and similarity scores derived from census tract data. The results show the following category assignments: 1) black-Asians to black, 2) white-others to white, 3) Asian-others to Asian, 4) white-blacks to other, 5) white-Asians to white (if Asian > 4.5%), 6) white-Asians to Asian (if Asian < 4.5%), 7) black-Asians to other (if black > 10.0%), and 8) black-Asians to black (if black < 10.0%). We argue that the proposed method is appropriate for all race-based studies using spatially relevant theoretical constructs such as segregation and gentrification.
Cabrera & Dela Cruz (in progress). "Spatially-based rules for reducing multiple-race into single-race data."
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