Mapping the Morphology of Urban Deprivation

Abstract

Globally, about one billion urban dwellers live in deprived areas (commonly referred to as slums). However, this figure is highly uncertain due to large data gaps. For example, in many cities, systematic underreporting occurs, which hampers the monitoring of SDG indicators. Earth observation (EO) data can be used to extract consistent spatial information on important aspects of the physical domain of deprivation and can offer essential proxies to not well-covered (e.g., social and economic) domains. However, for the development of a global data repository on deprived areas, several conceptual and methodological issues need to be solved. First, the relationship between concepts of a slum household and a deprived area needs to be defined in the context of information available in EO images. Second, the costs and benefits of different types of EO-data need to be established. Third, at different scales (ranging from communities, city to global scales), meaningful spatial aggregation units need to be established that are suitable to deal with uncertainties, privacy, ethics, and user needs. Fourth, computationally feasible, scalable, and transferable methods are required to produce a global data repository on deprived areas. This chapter provides an overview of methodological advances to address these four major challenges.

Housing forms of poverty in Europe - A categorization based on literature research and satellite imagery

“…because experts criticize the absence of social constructs in morphologically featured slum investigations (Owusu et al, 2021). With it, we aim to contribute to the international demand for consistent, systematic data and a global repository of deprived areas (Kuffer, Grippa, et al, 2021) by filling the gap of unclassified morphologic appearances of housing poverty in Europe.…”

“…So, comparable quantification is one major obstacle. Another one is universal applicability: The UN 'household deprivations' unite housing forms of poverty on a global scale and this set of indices is possible to meet EO findings (Kuffer et al, 2016;Taubenböck et al, 2018), yet not in all of its dimensions (Kuffer, Grippa, et al, 2021) -especially not such being EO-wise remotely undiscoverable (e.g., persons per room, access to drinking water). Thus, due to this complexity, we do not measure poverty remotely by satellite data but rely on documented issues.…”

“…As the literature survey has shown, empirical (in-situ) surveys have been and are already carried out with respect to poverty in Europe; and albeit national as well as supranational institutions, NGOs and academia collect data on the socioeconomic status of the underprivileged, a database of physical appearances, a compendium, Atlas or alike is a next step to document and understand poverty and respond to the international demand for better data and knowledge. Innovative steps are done by Kuffer, Grippa, et al (2021), who established a 'global slum repository' with multiple data sources next to remote sensing.…”

“…In an EO-based empirical measurement Arrival Cities reveal themselves by a wide range of housing morphologies (Taubenböck et al, 2018). A similar approach by Kuffer, Grippa, et al (2021) unites 'deprived areas' and declares still unknown characteristic differences at in-situ living conditions. Furthermore, related to the aforementioned multiple dimensions, Abascal, et al (2022) show 'domains of deprivation' in a framework that contains, e.g., housing, hazards, unplanned urbanization, governance, among others, with which underprivileged people are confronted.…”

Studying Sharp Changes of Urban Landscape: Concession Within Agricultural, Hydraulic, and Maritime Public Domain in Kelibia City, Northern Tunisia

“…Remote sensing and Geographical Information System (GIS) are used in analyzing the progression, pattern, and extent of urban sprawl and future patterns in fringe area using satellite images and spatial analysis (Zhou et al, 2011;Kuffer et al, 2021).…”