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Remote Sensing of Urban Poverty and Gentrification

by 1,2, 1,2,*, 1,2, 1 and 1
1
Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22032, USA
2
Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA 22032, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Hua Liu, Qihao Weng, Umamaheshwaran Rajasekar and Li Zhang
Remote Sens. 2021, 13(20), 4022; https://doi.org/10.3390/rs13204022
Received: 8 September 2021 / Revised: 1 October 2021 / Accepted: 5 October 2021 / Published: 9 October 2021
In the past few decades, most urban areas in the world have been facing the pressure of an increasing population living in poverty. A recent study has shown that up to 80% of the population of some cities in Africa fall under the poverty line. Other studies have shown that poverty is one of the main contributors to residents’ poor health and social conflict. Reducing the number of people living in poverty and improving their living conditions have become some of the main tasks for many nations and international organizations. On the other hand, urban gentrification has been taking place in the poor neighborhoods of all major cities in the world. Although gentrification can reduce the poverty rate and increase the GDP and tax revenue of cities and potentially bring opportunities for poor communities, it displaces the original residents of the neighborhoods, negatively impacting their living and access to social services. In order to support the sustainable development of cities and communities and improve residents’ welfare, it is essential to identify the location, scale, and dynamics of urban poverty and gentrification, and remote sensing can play a key role in this. This paper reviews, summarizes, and evaluates state-of-the-art approaches for identifying and mapping urban poverty and gentrification with remote sensing, GIS, and machine learning techniques. It also discusses the pros and cons of remote sensing approaches in comparison with traditional approaches. With remote sensing approaches, both spatial and temporal resolutions for the identification of poverty and gentrification have been dramatically increased, while the economic cost is significantly reduced. View Full-Text
Keywords: gentrification; urban poverty; remote sensing; machine learning gentrification; urban poverty; remote sensing; machine learning
MDPI and ACS Style

Lin, L.; Di, L.; Zhang, C.; Guo, L.; Di, Y. Remote Sensing of Urban Poverty and Gentrification. Remote Sens. 2021, 13, 4022. https://doi.org/10.3390/rs13204022

AMA Style

Lin L, Di L, Zhang C, Guo L, Di Y. Remote Sensing of Urban Poverty and Gentrification. Remote Sensing. 2021; 13(20):4022. https://doi.org/10.3390/rs13204022

Chicago/Turabian Style

Lin, Li, Liping Di, Chen Zhang, Liying Guo, and Yahui Di. 2021. "Remote Sensing of Urban Poverty and Gentrification" Remote Sensing 13, no. 20: 4022. https://doi.org/10.3390/rs13204022

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