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100			_aPrestby, Timothy _957527
245			_aUnderstanding neighborhood isolation through spatial interaction network analysis using location big data/
260			_bSage, _c2020.
300			_aVol. 52, Issue 6, 2020 ( 1027–1031 p.)
520			_aHidden 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.
700			_aApp, Joseph _957528
700			_aGao, Song _957529
700			_aKang, Yuhao _957530
773	0		_08877 _917103 _dLondon Pion Ltd. 2010 _tEnvironment and planning A _x1472-3409
856			_uhttps://doi.org/10.1177/0308518X19891911
942			_2ddc _cEJR
999			_c14487 _d14487