Remote Sensing of Urban Poverty and Gentrification

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.

Validation and refinement of cropland data layer using a spatial-temporal decision tree algorithm

“…However, these methods usually only focus on a few crop types and are not scalable to border categories due to the shortage of training samples and labeled datasets 9 . In addition, although machine learning approaches could achieve high accuracy results, the training models used in machine learning are highly localized and often lose functionality when the study area is changed 24 . Thus, most of these studies concentrated on small regions.…”

“…On one hand, the metrics captured overall accuracy and thus provided a quick assessment for our algorithm; on the other hand, more thoughtful and detailed evaluations are needed to better understand the spatial-temporal variation for our data compared to CDL. Lastly, similar to the previous statement, this research mainly focuses on crop classification correction, and the algorithm’s performance on non-agricultural fields is not evaluated, especially for areas that experienced rapid changes, such as expanding/shrinking cities 24 . The accuracy for these areas may be lower and would require future validations.…”

Assessment of Urban Neighbourhoods’ Vulnerability through an Integrated Vulnerability Index (IVI): Evidence from Barcelona, Spain

“…Gentrification is another process related to urban vulnerability, as it may imply a potential threat to the locals living in a specific neighbourhood, in relation to their permanence or expulsion by the hike in housing and other prices (Hübscher 2018;Lin et al 2021). Processes such as gentrification and touristification are generated when an urban area is appropriated by a population segment that did not previously live there, consequently expelling local inhabitants (Janoshcka and Hidalgo 2014;Hübscher 2018;Sorando and Ardura 2016).…”

“…The discussion regarding these phenomena is divided between those who consider them a source of income and urban improvement, and those who view them as a factor for vulnerability and subjugation. Whichever the case, more and more definitions of urban vulnerability consider this dimension of analysis (Lin et al 2021).…”

Residential Buildings’ Real Estate Values Linked to Summer Surface Thermal Anomaly Patterns and Urban Features: A Florence (Italy) Case Study

“…In addition, our work only focused on urban surfaces' characteristics and did not investigate further potential factors affecting real estate values, such as the temporal changes of buildings and landscape. Therefore, our future studies will also focus on these dynamics, especially on the urban and green gentrification, already explored in recent studies by using remote sensing data [66,67].…”