Shridhar Athinarayanan: Pinpointing Inequitable Water Distribution in Pune, India

Authors: Shridhar Athinarayanan, Prof. Steven Gorelick, Anjuli Jain Figueroa, Ph.D

Abstract: As the city’s population rises and poor water management practices persist, Pune in Maharashtra, India faces a disconnect between water distribution and demand. Pune’s current intermittent water supply system has barred safe and equitable water distribution. This study details the process of visualizing socioeconomic disparities across Pune and their correlations with water pressure allocation. Using tables of pipe and junction attributes supplied by the Pune Municipal Corporation, an aspatial network was developed. This network was then georeferenced algorithmically with the help of 550+ pipe vector PDF files and was then overlaid with data indicating household income distribution. From the visuals, it was shown that higher income areas receive higher pressures of water from suppliers, indicating a systemic economic issue within Pune’s water distribution scheme.


9 Comments on “Shridhar Athinarayanan: Pinpointing Inequitable Water Distribution in Pune, India

  1. Hi Shridhar,

    Thanks for a very interesting presentation. I think your hours of laborious work have been worthwhile – the dataset you created highlights very clearly water inequity in Pune. And I’m sure you have learnt a number of invaluable technical research skills along the way.

    Could you explain a little about your pressure data? At the start of your talk, you mentioned that the water supply is intermittent – so when were the pressure measurements made? Or do they refer to time-averaged water pressure in the pipes?


  2. Wow! That is impressive, to take all the paper and pdf data and turn it into a geospatial map. Congratulations. What part was the most challenging? how do you think this information can be used to make changes to improve lives?

    • Hi Jenny, thanks so much! The hardest part was definitely figuring out how to extract the data from the tables into a readable format as well as figuring out how to stitch together the maps to create the visualized network. For the visual, I researched and tested multiple methods of extracting and georeferencing coordinates until I finally carved out a process which would work efficiently. It was quite a learning experience!

  3. The visuals were very interesting in showing the correlation between income and water distribution. You summarized high dimensional data very well on 2D mapping. Curious to know what software you used for them?

  4. Hi Shridhar,

    Awesome Presentation! I absolutely love the graphics you made! Your main map as well as the pipe network map are just brilliant and awe-inspiring! I can definitely feel the hours put into measuring the coordinates and referencing them between the spaces. I am now inspired to create figures that are so rich in story like yours.

    A few questions that came from my immediate curiosity.

    1. Have you (or is it possible to) account for the non-uniform population density in each grid/region of Pune? My first thought is that the high population density in low income areas might contribute to the low water pressure simply because of the high demand. It seems like the next step for your project, as least to me, is to disentangle the influence of water demand and the influence of bad infrastructure. My naive hypothesis is that the two competing factors might account for the disparities in your map, such as low income areas with high water pressure. The data table you acquired seems, to me, enough to gauge the infrastructure quality. If it turns out the low-water pressure and limited-hours problems are mainly due to bad infrastructure and not population density, then this might nudge you closer to a prescriptive solution for Pune.

    2. Have you measured the spatial correlation between all the pressure readings and the associated income group? I just wonder if you came up with a number to measure such correlation. Seems like it would be compelling evidence for future presentations.

    3. How did you choose the four bins for the water pressure and income group? Why 15m, 20m, and 25m? Is it arbitrary, is it from the acquired dataset, or is it from a determined threshold?

    4. This is irrelevant, but what is the Hazen-Williams C in the pipe information table?


    • 1. Great insight! Yes, there are definitely many confounding variables associated with these data which must be accounted for. Investigating and disentangling population density could definitely lead me to a stronger conclusion.

      2. I am intending to do this in future work! For now, however, I wanted to put my efforts into the visual aspect, but I definitely agree that including statistics would drive the argument home.

      3. These bins were chosen arbitrarily.

      4. The Hazen-Williams is a coefficient which describes the resistance of water against a certain material.

      Thanks for your questions!

Leave a Reply

Your email address will not be published. Required fields are marked *