scholarly journals Using Population Mobility Data to Measure Black-White Residential Segregation in the COVID-19 Pandemic

2021 ◽  
Author(s):  
Yongjun Zhang

Racial and ethnic residential segregation has long been the central focus of stratification and inequality research, and it is a linchpin of racial stratification in the U.S. Sociologists and demographers have developed a series of spatial or aspatial measures to capture distinct aspects of segregation. Although the recent development of segregation measures, for instance, spatial exposure, accounts for spatial proximity among different groups, it is static and ignores the social connectedness dimension. This article uses population mobility across communities to correct the potential bias in spatial segregation measures. As population mobility is highly racially segregated, we modify the conventional spatial isolation index by adding an extra layer of social connectedness between communities to create a socially and spatially weighted segregation measure. We then use this spatial and social segregation measure to quantify the level of blacks' isolation with whites in the local neighboring communities. Our approach can be extended to other segregation measures and provide a new perspective to assess racial segregation in the U.S.

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Esteban Moro ◽  
Dan Calacci ◽  
Xiaowen Dong ◽  
Alex Pentland

AbstractTraditional understanding of urban income segregation is largely based on static coarse-grained residential patterns. However, these do not capture the income segregation experience implied by the rich social interactions that happen in places that may relate to individual choices, opportunities, and mobility behavior. Using a large-scale high-resolution mobility data set of 4.5 million mobile phone users and 1.1 million places in 11 large American cities, we show that income segregation experienced in places and by individuals can differ greatly even within close spatial proximity. To further understand these fine-grained income segregation patterns, we introduce a Schelling extension of a well-known mobility model, and show that experienced income segregation is associated with an individual’s tendency to explore new places (place exploration) as well as places with visitors from different income groups (social exploration). Interestingly, while the latter is more strongly associated with demographic characteristics, the former is more strongly associated with mobility behavioral variables. Our results suggest that mobility behavior plays an important role in experienced income segregation of individuals. To measure this form of income segregation, urban researchers should take into account mobility behavior and not only residential patterns.


2011 ◽  
Vol 24 (8) ◽  
pp. 904-910 ◽  
Author(s):  
K. White ◽  
L. N. Borrell ◽  
D. W. Wong ◽  
S. Galea ◽  
G. Ogedegbe ◽  
...  

2021 ◽  
Vol 4 ◽  
Author(s):  
A. Potgieter ◽  
I. N. Fabris-Rotelli ◽  
Z. Kimmie ◽  
N. Dudeni-Tlhone ◽  
J. P. Holloway ◽  
...  

The COVID-19 pandemic starting in the first half of 2020 has changed the lives of everyone across the world. Reduced mobility was essential due to it being the largest impact possible against the spread of the little understood SARS-CoV-2 virus. To understand the spread, a comprehension of human mobility patterns is needed. The use of mobility data in modelling is thus essential to capture the intrinsic spread through the population. It is necessary to determine to what extent mobility data sources convey the same message of mobility within a region. This paper compares different mobility data sources by constructing spatial weight matrices at a variety of spatial resolutions and further compares the results through hierarchical clustering. We consider four methods for constructing spatial weight matrices representing mobility between spatial units, taking into account distance between spatial units as well as spatial covariates. This provides insight for the user into which data provides what type of information and in what situations a particular data source is most useful.


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