An extended spatiotemporal exposure index for urban racial segregation

Bibliographic Data

ID12750801
AuthorsQingsong Liu (0000-0002-0862-9116, Kent State University), Mengmeng Liu (0000-0001-5409-2949, Tianjin University), Xinyue Ye (0000-0001-8838-9476, Texas A&M University, corresponding author)
Year2021
Volume48
Issue6
Pages530-545
Publication date2021-09-09
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueCartography and Geographic Information Science (JOURNAL)
Journal identifiersISSN: 1523-0406 • E-ISSN: 1545-0465
PublisherTaylor & Francis (PUBLISHER • GB)
DOI10.1080/15230406.2021.1965915
OpenAlexW3201127692
LanguageEN
Citations received4
References cited37

The Segregation Index quantifies the degree of segregation of social groups or classes. Because of the increasing use of fine-grained spatiotemporal activity and flow data, the conventional segregation measurements’ inclusiveness is challenged. We add population flow to the conventional place-based spatial exposure index to identify spatiotemporal segregation changes. Specifically, we considered the population-flow network, hierarchical structure, and time. In Chicago’s demonstration case study, we first used the time-dependent Twitter Origin-Destination flow matrices and their hierarchical structure information to estimate interactions between areal units at the neighborhood level. Then we computed the new population composition of units based on their interactions with other units and estimated the proposed spatiotemporal exposure index for different times. Finally, we systematically compared their differences with the conventional indices at global and local scales to see how population-flow patterns affect the exposure index. The results show that the population-flow patterns reflect valuable information in neighborhood interactions in temporal and spatial dimensions, but it is missing information in the conventional segregation computations. Furthermore, we emphasize that the hierarchical structures of flow patterns and the choice of appropriate parameters are also important factors for a rational segregation evaluation

Data mining · Econometrics · Flow (mathematics · Geography · Index (typography · Information flow · Machine learning · Multilevel model · Population · Computer Science · Demography · Human Mobility and Location-Based Analysis · Mathematics · Urban Transport and Accessibility · Urban, Neighborhood, and Segregation Studies

Tools

Open DOI