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

被引:23
|
作者
Prestby, Timothy [1 ]
App, Joseph [1 ]
Kang, Yuhao [1 ]
Gao, Song [1 ]
机构
[1] Univ Wisconsin, Dept Geog, Geospatial Data Sci Lab, 550 N Pk St, Madison, WI 53706 USA
来源
关键词
Neighborhood isolation; human mobility; big data; spatial interaction; RESIDENTIAL SEGREGATION;
D O I
10.1177/0308518X19891911
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页码:1027 / 1031
页数:5
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