Understanding neighborhood isolation through spatial interaction network analysis using location big data

Timothy Prestby, Joseph App, Yuhao Kang and Song Gao
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Timothy Prestby: Geospatial Data Science Lab, Department of Geography, University of Wisconsin–Madison, Madison, USA
Joseph App: Geospatial Data Science Lab, Department of Geography, University of Wisconsin–Madison, Madison, USA
Yuhao Kang: Geospatial Data Science Lab, Department of Geography, University of Wisconsin–Madison, Madison, USA
Song Gao: Geospatial Data Science Lab, Department of Geography, University of Wisconsin–Madison, Madison, USA

Environment and Planning A, 2020, vol. 52, issue 6, 1027-1031

Abstract: Hidden biases of racial and socioeconomic preferences shape residential neighborhoods throughout the USA. Thereby, these preferences shape neighborhoods composed predominantly of a particular race or income class. However, the assessment of spatial extent and the degree of isolation outside the residential neighborhoods at large scale is challenging, which requires further investigation to understand and identify the magnitude and underlying geospatial processes. With the ubiquitous availability of location-based services, large-scale individual-level location data have been widely collected using numerous mobile phone applications and enable the study of neighborhood isolation at large scale. In this research, we analyze large-scale anonymized smartphone users’ mobility data in Milwaukee, Wisconsin, to understand neighborhood-to-neighborhood spatial interaction patterns of different racial classes. Several isolated neighborhoods are successfully identified through the mobility-based spatial interaction network analysis.

Keywords: Neighborhood isolation; human mobility; big data; spatial interaction (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:envira:v:52:y:2020:i:6:p:1027-1031

DOI: 10.1177/0308518X19891911

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