The tragedy of data-hunting-gathering
A defining feature of urban poverty alleviation programs in India in the present times is their ever increasing size, scale and speed of implementation. The Government plays a leading role in the planning and execution of these programs in collaboration with large (often multi-national) private sector consultants, think-tanks and non-profits with financial support from international donors and philanthro-capitalist foundations.
Increasingly, the implementation of these projects involves the creation of vast amounts of digital data, which fuels an ever growing data-hunger among all kinds of development organisations and professionals.
Everyone is perenially looking for data these days – students, researchers, the organisation that just partnered with the government, the organisation that wants to partner with the government, the organisation that will never partner with the government, the odd travelling scholar in search of a good case study...the list goes on.
Most of the time, the DHG (data hunter-gatherer) community is not even aware of what kind of data they need, the reason they need it for or the use they intend to put it to once they acquire it. The belief seems to be ("belief" is the right word, for there seems to be very little of science in such an approach), that once these mythical Himalayan data-sets would be acquired all the other questions would magically get answered too.
Rather than searching for data in such a hopeless manner, it could be far more useful to understand the technical aspects of the development programs that generate this data and the operational parameters they need to adhere to. Exploring in detail "how" a program is executed, also throws light on "who" actually implements it and with what tools and techniques. One can then also understand "what" data gets generated in such programs and what uses it could have.
The usefulness of studying operational parameters
Compared to the data, which may be extremely hard to obtain, the goals, objectives and standard operating procedures guiding the implementation of these projects are more accessible. Even if they are not available in the public domain, the government is generally far more comfortable sharing these documents than the actual data.
Having even a general understanding of the operational parameters of a project can help one deduce what kind of data may or may not have been produced as part of the project.
For example, in the case of Jaga Mission, the operational parameters were clearly laid out in the legislation - "The Odisha Land Rights to Slum Dwellers Act, 2017" that guided its implementation.
Let’s look a bit deeper into the operational parameters of the mission in order to understand what kind of data was produced and why it was produced.
The operational parameters of Jaga Mission
According to the Act
- A slum is defined as a compact settlement of minimum 20 households;
- Land rights shall be granted based on the area of actual occupation;
- The maximum ceiling for granting land rights is 45 sqm for slums located in Municipal Councils and 60 sqm for slums located in Notified Area Councils (NACs);
- Land rights shall be granted free of charge for up to 30 sqm for families belonging to the Economically Weaker Section (EWS) category;
- Non-EWS households shall pay a certain percentage (later fixed at 50 percent) of the benchmark value of land for securing the land rights and EWS households shall pay a lesser percentage (25 percent) for the amount of land occupied above 30 sqm.
From the above parameters, we can get a pretty clear idea of what kind of data we require in order to execute the program. It is clear that the implementation of the Mission would not only require the boundaries of slum settlements to be clearly delineated (according to the definition) but also an exact mapping of every single dwelling inside the slums in order to calculate the area under actual occupation. In its first phase, Jaga Mission covered 168000 households in 1725 slums in 109 cities, which are spread across the length and breadth of the state of Odisha (155707 sq.km), turning the mapping and survey process of the Mission into a huge logistical challenge.
It was this massive geographical scale and the need to complete the survey within a reasonably short time which made the Government consider the use of drones. Teams of 3 to 4 professional surveyors would travel by road and reach the cities allotted to them. They would then team up with the local municipal staff and NGOs to visit the slum settlements. GIS companies and NGOs were hired and simultaneously deployed to cover all the cities and slums of Jaga. On reaching the slum, the survey team would prepare the drone flight plan (based on inputs from the municipal staff, NGO representatives and slum dwellers), set up the ground control points, fly the drone and then move on to the next slum. For an average sized slum (1.2 hectares), this process would take about 1.5 to 2 hours. After covering all the slums in a city, the survey team would pack up and drive on to the next city. The raw drone captures for a batch of cities would then be sent for processing and turned into extremely high-resolution (2 cm) ortho-images (i.e. geographically corrected images which allow true distances to be measured). While the image processing was being done, the survey teams would continue their surveys in other cities, thus ensuring a simultaneity of data capturing and data processing activities. The high resolution allowed the digitization of even the tiniest dwellings to be done directly on the ortho-image, without the need for additional DGPS surveys to be conducted in the slums (as was done in the case of the RAY project a few years earlier).
The following three map layers (vector data) were the most crucial and where prepared for each slum -
i) Slum boundary layer – showing the exact extent of the slum on the date of the survey.
ii) Slum dwellings layer – polygons showing each dwelling unit inside the slum (along with the household survey data).
iii) Revenue parcels layer – showing the ownership details and formal records of the land parcels on which the slum is located.
Together with the ortho-image (raster) layer and an additional land-use (vector) layer, this translated into a huge dataset of 8625 map layers for the 1725 slums of Jaga.
But, once the above map layers were ready, all sorts of geographical and mathematical operations related to mission implementation could be done easily. Let's consider the following cases in a slum located in a Municipal Council (with a maximum ceiling of 45 sqm for being eligible). Let’s assume the benchmark value of land to be INR 80,00,000/acre (i.e. INR 1977/sqm).
- Beneficiary A - EWS family occupying 27 sqm.
- Beneficiary B - EWS family occupying 33 sqm.
- Beneficiary C - Non-EWS family occupying 35 sqm
- Beneficiary D - EWS or Non-EWS family occupying 50 sqm
Solution of land rights eligibility criteria for each case would be -
- Beneficiary A - Family would get land right free of cost for 27 sqm.
- Beneficiary B - Family would get land right for 33 sqm on payment of INR 1482.75/- (0.25 x 1977 x3)
- Beneficiary C - Family would get land right for 35 sqm on payment of INR 34597.5/- (0.5 x 1977 x 35)
- Beneficiary D - Ineligible until family agrees to surrender the extra 5 sqm.
We can see clearly from the above example that the operational parameters of the project as defined in the Act pretty much dictated what kind of data needed to be generated and how that data was to be used to calculate the eligibility criteria for settling the land.
But it also becomes clear from the above, what kind of data was not generated as part of the Mission.
The truth is in the grind
The bottom-line is that the data that governments produce for the implementation of their schemes and projects may or may not be of any use to researchers, scholars, practitioners and organisations which are not directly involved with the project. The operational parameters help us understand what kind of databases may or may not have been generated.
It is far better to have ones own objectives and questions clearly articulated and produce the datasets accordingly. In this digital age it is easy to imagine that the grind of being a solid development researcher can be avoided by just scooping up all the data that exists in government offices and working the magic of the computer on it - but that does not work.
No amount of aimless data-hunting-gathering can replace the power of scientific knowledge, well articulated research questions and a robust methodology.
The truth is in the grind.
