Visualizing Neighborhood Isolation by Spatial Interaction Network Analysis

Authors: TIMOTHY PRESTBY*, University of Wisconsin-Madison, Song Gao, UW-Madison, Yuhao Kang, UW-Madison, Joseph App, UW-Madison
Topics: Cartography, Urban Geography, Social Geography
Keywords: Big-data, Spatial Interaction, Graph Theory
Session Type: Guided Poster
Day: 4/8/2020
Start / End Time: 9:35 AM / 10:50 AM
Room: Agate A/B, Hyatt Regency, Third Floor
Presentation File: No File Uploaded


Hidden biases of racial and socio-economic preferences shape residential neighborhoods throughout America. Thereby, these preferences shape neighborhoods composed predominantly of a particular race or income class. Although residential segregation within neighborhoods continues to decline, the segregation, especially in urban areas, still warrants attention. A growing body of scientific literature argues that disadvantaged neighborhoods of minority and/or poor residents face challenges to access to what many experts refer to as social or opportunity isolation. Among these opportunities includes a lack of safe and healthy living environment, a lack of access to higher-paying jobs, and education. 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.

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