Data Analytics

By Vishnu Prasad, IFMR Finance Foundation

This post is part of our series on the current state diagnostic step.

At the end of the cadastral mapping and surveying process, our local team had produced detailed maps of physical infrastructure for every street in Srirangapatna, 256 household surveys (5% of the town’s households) and 12 unique business surveys. Our next challenge was to translate this high quality data into meaningful analysis and visually intuitive formats.

As we pondered over the various options, it occurred to us that the best way to represent our analysis was to do it spatially. Using Quantum GIS (Geographic Information Systems), an open source GIS application that is essentially MS Excel with the added dimension of spatiality, we created maps that made our analysis visually appealing and easy to comprehend.

For example, our data depicting garbage collection across the wards which looked like this:

was transformed into:

As we looked at these maps, their many advantages over conventional modes of analysis became apparent. We realized that in the map above, Ganjam (wards 16-23, on the right side) had trash collection rates that were much lower than Srirangapatna Fort Town (wards 1-15, to the left). Spatial representation of the analysis brought out many aspects that would not have been readily apparent otherwise.

We could also run analytics such as the one below:

This map shows all properties with a specified distance of the nearest streetlight. Analytics such as these became vital in determining access (proximity) to infrastructure across Srirangapatna. For example, the spatial representation of our analysis shows that slums are very often worst hit in terms of access to infrastructure.

Having created maps using Quantum GIS software, a freeware available online, we overlaid these on Google Maps. You can access all the data from our cadastral mapping on the maps section of our website.

Our household surveys gathered socio-economic data over 10 sectors: Work Habits, Transportation, Shopping, Drinking Water, Sanitation, Solid Waste, Electricity, Housing, Finance and Technology. The analysis produced reports that included comparisons across metrics between Srirangapatna Fort Town and Ganjam, and wards with and without slums.

We also produced comprehensive ward level reports like the one below which shows data for Ward No. 20.

Since the completion of the report, we have shared our results with the ward council of Srirangapatna TMC. The ease with which they understood our analysis (despite the fact that no one in our team speaks Kannada) has validated the approach that we have taken to represent our results.

We will now use these representations in our visioning exercises with citizens on the future of Srirangapatna.