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ABSTRACT
Most existing poverty research focused on the identification of households in absolute poverty; few studies attempted to identify households in relative poverty (HRP). In this paper, we first developed an improved Bayes’s theorem algorithm to sense individuals’ spatio-temporal behavior characteristics via integrating mobile phone signaling, electricity consumption, and Points of Interest. We then utilized the improved random forest to identify HRP based on individuals’ spatio-temporal behavior characteristics and building properties. A total of 29,370 urban households in Fengchan community, Zhengzhou, China, were selected to conduct this study. The accuracy rate was about 90% when it was verified against the household survey data. Three conclusions can be drawn from our analysis: (1) the individuals’ spatio-temporal behavior characteristics played a more critical role in identifying HRP than building properties, (2) the identification accuracy of multi-source data is higher than that of single-source data, (3) mobile phone signaling records and building footprints data are more important in identifying HRP in low-rise buildings, while electricity consumption data is more crucial in the identification in high-rise buildings. Our proposed methods can accurately identify urban HRP, which is helpful to target interventions in the most needed areas. Our findings can inform relief policies in similar cities.
Acknowledgments
The authors wish to thank anonymous reviewers and the editor for many useful comments and suggestions for the revision.
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No potential conflict of interest was reported by the author(s).
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Ning Niu
Ning Niu received his PhD in Geographic Information System from Sun Yat-sen University. He currently works as a lecturer in Academician Laboratory for Urban and Rural Spatial Data Mining of Henan Province (China). He is a teacher and researcher on issues of GIS and urban planning. He has published on building function identification, big data analysis, and land development, and he has worked for the administration, as a consultant, on issues of urban planning. His work has also been published in International Journal of Geographical Information Science, and Environment and Planning B: Urban Analytics and City Science.
He Jin
He Jin received her PhD in Geographic Information System. She is currently an instructor with the School of Geosciences, University of South Florida. She has authored over five articles. Her research interests include health, built environment, spatial-temporal data analysis, and urban planning. Her work has also been published in Applied Geography, International Journal of Geographical Information Science, and Environment and Planning B: Urban Analytics and City Science.