The article maps urban poverty, using the `livelihoods assets framework' to develop a new index of multiple deprivation, examining the implications for area and sector targeting by policy-makers. This article deals with the index and the results for Delhi. The study maps: the spatial concentration of poverty; the diversity of deprivation at ward level; whether poverty is concentrated in slums; and correlations between voting patterns and poverty levels. The index uses census data disaggregated to electoral-ward level for multicriteria analysis, through GIS. Results show that hotspots of poverty are diverse in character, but are not concentrated in slum areas, with strong implications for policy-making and poverty studies methodology. These results suggest that the new index allows better insight into poverty with better targeting possibilities for policy-makers.
Many cities in the global South are facing the emergence and growth of highly dynamic slum areas, but often lack detailed information on these developments. Available statistical data are commonly aggregated to large, heterogeneous administrative units that are geographically meaningless for informing effective pro-poor policies. General base information neither allows spatially disaggregated analysis of deprived areas nor monitoring of rapidly changing settlement dynamics, which characterize slums. This paper explores the utility of the gray-level co-occurrence matrix (GLCM) variance to distinguish between slums and formal built-up (formal) areas in very high spatial and spectral resolution satellite imagery such as WorldView-2, OrbView, Quickbird, and Resourcesat. Three geographically different cities are selected for this investigation: Mumbai and Ahmedabad, India and Kigali, Rwanda. The exploration of the utility and transferability of the GLCM shows that the variance of the GLCM combined with the normalized difference vegetation index (NDVI) is able to separate slums and formal areas. The overall accuracy achieved is 84% in Kigali, 87% in Mumbai, and 88% in Ahmedabad. Furthermore, combining spectral information with the GLCM variance within a random forest classifier results in a pixel-based classification accuracy of 90%. The final slum map, aggregated to homogenous urban patches (HUPs), shows an accuracy of 88%-95% for slum locations depending on the scale parameter.
In Mumbai, new forms of cooperation between local government and citizens seek to improve local representation and the quality of services. This paper examines which residents are represented or excluded in these arrangements, the mandates and processes by which the arrangements are negotiated and the outcomes. Local representation through elected councillors is compared with that through voluntary neighbourhood groups (Advanced Locality Management groups, or ALMs), which work with the executive wing of local government. ALMs, involving middle-class groups, work on environmental, security and upgrading issues. They are expanding their claim to both political and public space, often excluding "unwanted" people. Elected councillors are channels mainly for lowincome groups, addressing issues relevant to municipal services but also responding to personal grievances and concerns. Confl ict between political representatives and their parties and ALMs is not unusual. Both of these "negotiated spaces" give citizens some way of holding government to account, although middle-class citizens are fi nding greater scope for action.
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