SPAN-HDI: Spatial Analysis of Human Development in India



Project Team




Project Details


  • Funder: Department for the Economy, Northern Ireland, UK
  • Budget: £19,124
  • Duration: February - July, 2017


Map Visualizations


As part of the project, we generated visualizations of inter-attribute relationships between attributes across various Indian states. We took a variety of attribute pairs, such as job industry and ever attended school (the latter is a binary attribute), and used Fisher Exact Test to see whether there are relationships between them. We were simply interested in the existence/non-existence of a relationship, and not on the direction of relationship. Given the limited size of the dataset, we were unable to get statistically significant results in many cases, and thus, just present the p-values in a plot. A low p-value, specifically p < 0.05 or p < 0.01, exemplifies a statistically significant relationship. We link some representative visualizations herein. It may be noted that the data for Andhra Pradesh was from the pre-2014 Andhra Pradesh, and given the creation of Telengana and usage of a map with modern boundary, Telengana is always shown greyed; however, the p-value for Andhra Pradesh holds for Telengana as well.

  • Ever Failed or Repeated a Class AND Farm Work Hours Per Day Link
  • Industry of Job AND Literacy Link
  • Number of Jobs Done for Money in Last Year AND Literacy Link
  • Government Job AND Ever Attended School Link
  • English Ability AND Farm Work Hours Per Day Link
  • Work Hours Per Day AND English Ability Link
  • Ever Attended School AND Farm Work Hours Per Day Link
  • Number of Jobs Done for Money in Last Year AND Ever Attended School Link
  • Number of Jobs Done for Money in Last Year AND English Ability Link
  • Industry of Job AND Ever Attended School Link
  • Work Hours Per Day AND Ever Attended School Link
  • Government Job AND Ever Attended College or Vocational School Link


Project Goals


  • Overlay IHDS data on maps to enable heatmap-based analysis to understand the overarching trends in facets such as living standards, income and consumption data.
  • Deploy data analysis/mining algorithms for identifying India-wide attribute correlations and use those to formulate questions that merit further attention on India’s human development.
  • Building upon techniques for spatial outlier detection, develop methods to automatically identify geo-clusters that buck particular country-wide trends that have been identified earlier.


Gallery