COVID-19 Metrics across Parliamentary Constituencies in India

India has the second-highest number of confirmed cases and ranks third for the greatest number of COVID-19 deaths since September 2020. We present the first COVID-19 metrics and data visualization for 543 Parliamentary Constituencies in India that allow elected officials and their constituents to monitor and develop testing and vaccine deployment strategies to efficiently enable the reopening of the country.

Related Publications

© Authors: Akhil Kumar, Weixing Zhang, S V Subramanian. Geovisualizing COVID-19 Metrics across Parliamentary Constituencies in India. December 2020, Cambridge MA, Harvard Center for Population and Development Studies.

28e244e4ecbda4173080551c2629bcfa

Additional Information

Data

We acquired COVID-19 data from covid19india.org which is a crowd-sourced data site for COVID-19 stats and patient tracing in India. The API can be accessed here. covid19india.org adjusts cumulative deaths occasionally which results in negative daily new deaths. For instance, the cumulative number of deaths in Palghar of the state of Maharashtra was changed from 952 to 876 on November 3, 2020. The negative deaths are changed to zero in our dashboard. Lakshadweep is also not shown in our dashboard because of the lack of data available. The dashboard updates daily at 3pm EST.

Methodology

We generated a crosswalk between districts and parliamentary constituencies. Assuming the indicators were distributed uniformly across the population in one district, crosswalk uses GIS techniques to estimate population distribution in granularity and reaggregate to PC level for estimation. See the workflow below and read the working paper here for more details.

  1. Calculate district area (dist_area): perform 'Calculate Geometry' function on the district shapefile, using the Kalianpur 1975/India Zone lla (EPSG: 24379) coordinate system.
  2. Intersect PC and district boundaries, link each segment with district ID and PC ID: perform 'intersect' command to district and PC shapefile, create a new shapefile called Dist_PC_intersect.
  3. Calculate segment area (seg_area): perform 'Calculate Geometry' function on the district shapefile, using the Kalianpur 1975/India Zone lla (EPSG: 24379) coordinate system.
  4. Calculate segment population (seg_ment): perform 'zonal statistics' command using segment shapefile as the 'zone' and the WorldPop2020 as the data raster; derive pc level population by aggregating segment population by PC ID.
  5. Calculate the percentage of district area (Pct_area) by dividing segment area by district area. Remove segments that were less than a hundredth of a percent of the original district area. Those extremely small areas are created because of the slight boundary inaccuracies between district and PC shapefiles. In total, there are 3468 segments.
  6. Calculate segment headcount = district rate * segment population, assuming district prevalence rate unchanged across the whole district
  7. Aggregate segment headcounts by PC ID to get PC level headcount.
  8. Calculate PC level prevalence rate by PC headcount divided by PC population.