Abstract
Many cities in the Global South are facing rapid population and slum growth, but lack detailed information to target these issues. Frequently, municipal datasets on such areas do not keep up with such dynamics, with data that are incomplete, inconsistent, and outdated. Aggregated census-based statistics refer to large and heterogeneous areas, hiding internal spatial differences. In recent years, several remote sensing studies developed methods for mapping slums; however, few studies focused on their diversity. To address this shortcoming, this study analyzes the capacity of very high resolution (VHR) imagery and image processing methods to map locally specific types of deprived areas, applied to the city of Mumbai, India. We analyze spatial, spectral, and textural characteristics of deprived areas, using a WorldView-2 imagery combined with auxiliary spatial data, a random forest classifier, and logistic regression modeling. In addition, image segmentation is used to aggregate results to homogenous urban patches (HUPs). The resulting typology of deprived areas obtains a classification accuracy of 79% for four deprived types and one formal built-up class. The research successfully demonstrates how image-based proxies from VHR imagery can help extract spatial information on the diversity and cross-boundary clusters of deprivation to inform strategic urban management.
Highlights
Official maps often omit the existence of deprived areas [1] or declare them to be homogeneous [2,3]
In an earlier study [14], we developed a typology of deprived areas for Mumbai using very high resolution (VHR) imagery
Analyzing the Correlation of Potential Features. Both the ability to distinguish the five built-up types and the correlation of all 34 image features (Figure 7) are analyzed for all image features aggregated at the level of built-up homogenous urban patches (HUPs)
Summary
Official maps often omit the existence of deprived areas [1] or declare them to be homogeneous [2,3]. Finding reliable information on deprived areas is a complex problem, as illustrated by population estimates in the large Mumbai slum Dharavi, which, according to [4], range from 300,000 to 900,000 inhabitants. [5,6]), hiding spatial differences within wards and clustering across ward boundaries. This is a particular problem if wards are rather large, as is the case of the health wards in Mumbai (of which there were 88 at the time of the 2001 Census, with an average population of 136,000). In the 2011 Census data, the metropolitan area of Mumbai is divided into 24 administrative wards, with populations ranging from 127,290 (city [7]) to 941,366 people (suburban [8]). Linking and integrating spatially detailed information on slums to such large and aggregated spatial units is a problem [9], even when data on slums are available they are often not used as useful spatial relationships cannot be built
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