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Abstract
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.
Highlights
Cities around the world are in different stages of economic development
Urbanization is more complicated for cities in developing countries due to the dramatically increasing impoverished population
As urban studies have pointed out, 20% to 80% of new urbanization in developing countries is occupied by low-income communities [23]
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. In the last decade of the twentieth century, some suburbs were big enough and had developed the same problem as old industrialized cities, and these new suburban cities started to experience the same economic collapse and population decrease [16,17]. Other developing nations (e.g., countries in East Asia, Latin American cities) are facing a similar problem of expanding urban poverty in recent decades [15,24,25,26]. This paper summarizes and evaluates conventional and novel approaches for delineating urban poverty and gentrification with the latest techniques. It discusses the problems and limitations of traditional mapping methods and how these issues could be addressed by emerging technologies.
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