Authors: Elijah Knaap*, University of California - Riverside, Sergio Rey, University of California, Riverside, Renan Xavier Cortes, University of California, Riverside
Topics: Urban Geography, Spatial Analysis & Modeling, Ethnicity and Race
Keywords: segregation, neighborhoods, urban analytics, data science
Session Type: Paper
Presentation File: No File Uploaded
In this paper we use 17 years of annual block-level data from every MSA in the United States to examine the space-time dynamics of residential and workplace segregation. Following Kim and Hipp (2019), we rely on the concept of egohoods, which we term “neighorhoods” for home locations and “laborhoods” for workplace locations. We define a local egohood as the set of census blocks reachable within a 20 minute walk along the pedestrian transportation network from the centroid of a focal block. Thus, we first construct multiscalar segregation profiles for every MSA in the United States for both home and workplace egohoods. We use the Spatial Information Theory Index with increasingly large bandwidths for our egohoods. We repeat this processs for each year between 2002 and 2017, measuring the difference between home and workplace segregation at each scale. This yields a rich set of analytics that describe how measured levels of segregation fluctuate over space and time for each MSA, where the shape of each segregation profile describes the geographic scale of neighborhood and laborhood segregation in each MSA, and the distance between each curve describes the increase or decrease in segregation at different scales between consecutive years. The distance between neighborhood and laborhood curves describes the changing context of segregation over the course of a typical day. With these metrics in hand, we proceed with analyzing the relationship between home and workplace segregation dynamics, assessing whether temporal trends are consistent or divergent and offering suggestions for why these patterns emerge.
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