- Understanding heterogeneity in metropolitan India: the added value of remote sensing data for analyzing sub-standard residential areas
- International Journal of Applied Earth Observation and Geoinformation
- Volume | Issue number
- 12 | 5
- Pages (from-to)
- Document type
- Faculty of Social and Behavioural Sciences (FMG)
- Amsterdam Institute for Social Science Research (AISSR)
Analyzing the heterogeneity in metropolitan areas of India utilizing remote sensing data can help to identify more precise patterns of sub-standard residential areas. Earlier work analyzing inequalities in Indian cities employed a constructed index of multiple deprivations (IMDs) utilizing data from the Census of India 2001 (http://censusindia.gov.in). While that index, described in an earlier paper, provided a first approach to identify heterogeneity at the citywide scale, it neither provided information on spatial variations within the geographical boundaries of the Census database, nor about physical characteristics, such as green spaces and the variation in housing density and quality. In this article, we analyze whether different types of sub-standard residential areas can be identified through remote sensing data, combined, where relevant, with ground-truthing and local knowledge. The specific questions address: (1) the extent to which types of residential sub-standard areas can be drawn from remote sensing data, based on patterns of green space, structure of layout, density of built-up areas, size of buildings and other site characteristics; (2) the spatial diversity of these residential types for selected electoral wards; and (3) the correlation between different types of sub-standard residential areas and the results of the index of multiple deprivations utilized at electoral ward level found previously.
The results of a limited number of test wards in Delhi showed that it was possible to extract different residential types matching existing settlement categories using the physical indicators structure of layout, built-up density, building size and other site characteristics. However, the indicator ‘amount of green spaces’ was not useful to identify informal areas. The analysis of heterogeneity showed that wards with higher IMD scores displayed more or less the full range of residential types, implying that visual image interpretation is able to zoom in on clusters of deprivation of varying size. Finally, the visual interpretation of the diversity of residential types matched the results of the IMD analysis quite well, although the limited number of test wards would need to be expanded to strengthen this statement. Visual image analysis strengthens the robustness of the IMD, and in addition, gives a better idea of the degree of heterogeneity in deprivations within a ward.
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