India is the world’s most populous country, yet also one of the least urban. It has long been known that India’s official estimates of urban percentages conflict with estimates derived from alternative conceptions of urbanization. To date, however, the detailed spatial and settlement boundary data needed to analyze and reconcile these differences have not been available. This paper presents gridded estimates of population at a resolution of 1 km along with two spatial renderings of urban areas—one based on the official tabulations of population and settlement types (i.e., statutory towns, outgrowths, and census towns) and the other on remotely-sensed measures of built-up land derived from the Global Human Settlement Layer. We also cross-classified the census data and the remotely-sensed data to construct a hybrid representation of the continuum of urban settlement. In their spatial detail, these materials go well beyond what has previously been available in the public domain, and thereby provide an empirical basis for comparison among competing conceptual models of urbanization.

Scholars are in agreement that the local food environment is shaped by a multitude of factors from socioeconomic characteristics to transportation options, as well as the availability and distance to various food establishments. Despite this, most place-based indicators of “food deserts”, including those identified as so by the US Department of Agriculture (USDA), only include a limited number of factors in their designation. In this article, we adopt a geodemographic approach to classifying the food access landscape that takes a multivariate approach to describing the food access landscape. Our method combines socioeconomic indicators, distance measurements to Supplemental Nutrition Assistance Program (SNAP) participating stores, and neighborhood walkability using a k-means clustering approach and North Carolina as a case study. We identified seven distinct food access types: three rural and four urban. These classes were subsequently prioritized based on their defining characteristics and specific policy recommendations were identified. Overall, compared to the USDA’s food desert calculation, our approach identified a broader swath of high-needs areas and highlights neighborhoods that may be overlooked for intervention when using simple distance-based methods.