Analysing Inequity in Accessibility to Services with Neighbourhood Location and Socio‐Economic Characteristics in Delhi

Abstract

The lack of comprehensive spatial data for neighbourhoods in cities in the global South has posed a significant challenge for examining socio‐economic inequities in accessibility to services. By combining the primary (survey data) and secondary data sources with new spatial data sources (Earth observation data, Google Maps), we create a spatial database of 4,145 residential locations in Delhi, aggregating them into 1 km grid‐shaped neighbourhoods. The neighbourhood's economic status is evaluated using a composite index of the built environment, land price, and household income. Social characteristics are examined through the percentage of the scheduled caste (SC) population, considering their historical marginalization in Indian society. Using the E‐2SFCA method, we calculate accessibility to four key services and employ the geographically weighted regression (GWR) model to explore inequities in accessibility based on neighbourhood location and socio‐economic characteristics. Findings reveal inequity in accessibility to services at the neighbourhood level is primarily driven by spatial location rather than income or percentage of SC population. Moreover, the influence of socio‐economic characteristics on accessibility varies across locations. The spatial data mapping approach employed in this article can be applied to numerous rapidly urbanizing cities in the global South lacking block or neighbourhood‐level spatial data.

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