Tata Trusts wanted to be able to visualise granular household level data for a small village in Orissa, in an intuitive, lifelike manner that could help identify various issues and their patterns in occurrence. We built them a map with custom shape files of the houses that represented the data for each house, thus identifying demographic patterns across various issues ranging from income to education. This mapping is now a feature on Collect where you can represent households on a map while collecting data.
Introduction to the Project
Tata Trusts is a compendium of philanthropic organisations that were formed by the Tatas over the years. They aim to be catalysts in development by giving grants to institutions working areas such as Natural Resources Management, Rural Livelihoods, Urban Livelihoods & Poverty, Education, Enhancing Civil Society and Governance, Health and Media Arts, Crafts and Culture.
Tadingipai is a small village in the Bishmakatak district of Orissa with a population of around 200. Tata trusts wanted to develop a mechanism wherein they could collect granular level data for all households in a particular region – such as Tadingipai – and represent it on a map so that they could identify geographic patterns across various issues, and take more informed steps in tackling these particular issues.
Initially, all we had was data about the households in this village, which was collected on paper, and a map obtained from the internet of Tadingipai – which looked like this:
There was no information regarding the households because of which mapping the collected information directly onto an online map wasn’t possible.
Using a physical map of Tadingipai and an internal shape file tool, we created shape files of all the houses and then overlay them with the Tadingipai map. Since we had the information for each house and it was mapped to the house number, we could populate each shape file with the corresponding household data.
Thus, clicking on any of these shape files would display information regarding the household and all the members of the household.
This also meant identifying geographic patterns for socioeconomic indicators would become a lot easier. Here we see us visualising the income levels of the various households across the settlement – with various income levels being represented by different shades of blue.
We realised that on a much bigger scale this could be the most intuitive way to represent geographic patterns that occur for various issues. This would make decision making a whole lot easier for organisations so that they can implement these decisions across certain target locations according to the patterns that emerge.
For example, a map that tracks real-time data on diarrhoea outbreak in a particular village could perhaps easily help in correlating the areas with higher number of diarrhoea cases and the water sources for these areas – allowing organisations to identify the troublesome water sources and implement measures to prevent further damage.
However, we also realised that the process we used to map the household data in Tadingipai was only possible because of the minute size of this particular area. The process involved in mapping Tadingipai would practically be an impossible task to do across – let’s say – a country.
That’s exactly why we now have a separate mapping question type on Collect. So every time a household survey is being taken across a certain location, the organisation has an option to add in a mapping type question which enables the surveyor on field to map the current household/building. This directly gives us house shape files with accurate GPS co-ordinates that could easily be overlaid onto a map – all of it with information corresponding to the household.
Tata Trusts is now using the Collect application to map the information of more than 2 lakh household across Krishna district in Andhra Pradesh.