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Mapping and predicting the intra-urban deprivation degrees using EO data

Luo, Eqi (2021) Mapping and predicting the intra-urban deprivation degrees using EO data.

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Abstract:The rapid global proliferation of slums is a major challenge in urbanisation. Most of the growing urban population in Low- to Middle-Income Countries (LMICs) is absorbed by slums and informal settlements (here called deprived areas). In the last decades, deprived areas have been identified and mapped to a great extent, given the increasing availability of very-high-resolution (VHR) satellite images and the development of machine learning (ML) techniques. Yet, most earth observation (EO) approaches only yield a binary delineation of deprived/non-deprived areas – an oversimplified understanding of urban deprivation that mostly built upon physical or morphological features, with little information inferred regarding the intensity, variation, and diversity of intra-urban deprivation. In this study, we attempt to explore the potential of using VHR EO-based data to predict the degrees of intra-urban deprivation in Nairobi, Kenya. This involves a two-step workflow of characterising and predicting a continuous index of deprivation degrees. First, a principal component analysis (PCA) is conducted to characterize the multi-dimensionality and intensity of deprivation as a set of continuous indices (i.e., the ‘multi-deprivation portfolio’), using 100m standard grids as analytical units. Next, a convolution neural network (CNN) based regression model is trained to directly predict the ‘multi-deprivation portfolio’, using only SPOT-7 images. The PCA results identify four major domains of deprivation, i.e., PC1: Poverty, accessibility to facilities, and maternal health support, PC2: Dense urbanization, absence of green space and waste management, PC3: Air and water contamination, and PC4: Transport infrastructure. Among these deprivation domains, PC2 is the most morphology-based domain and successfully captures the spatial configurations of slums in Nairobi. During the test of EO-based data for predicting the domains of deprivation, the best prediction of the proposed CNN regression model is also obtained in PC2, with an R2 of 0.6543; whereas the CNN fails on other deprivation domains. Based on these results, this study confirms that urban deprivation is by nature a multi-dimensional, complex concept, and PCA is a useful tool to unpack and measure this multi-dimensionality in continuous scales. Most importantly, we demonstrate the potential of an EO-based method to directly capture the degrees of multiple deprivation with relatively high accuracy. We suggest scaling up this method to inter-city, national or even global level and produce larger-scale maps of deprivation degrees in LMICs cities in future studies.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
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