An extended spatiotemporal exposure index for urban racial segregation

ABSTRACT

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.

KEYWORDS:

• Spatiotemporal exposure index
• flow network
• hierarchical structure
• spatiotemporal activity and trajectory data
• residential segregation

Acknowledgments

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of these funding agencies. The authors would also like to thank the four anonymous reviewers and editor for their valuable suggestions and contributions. We also are indebted to Winston Yang for proofreading the revised paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed here.

Data Availability Statement

The data that support the findings of this study are openly available in https://doi.org/10.5281/zenodo.4397525.

Additional information

Funding

This work was supported by the National Science Foundation [1739491,1937908]; Texas A&M University start-up funding [241117].