Slum rehabilitation is often a long-drawn and complex process due to the conflicting objectives of policy-makers, investors and slum-dwellers. But new tools of urban data science have the potential to accelerate deployments by injecting much needed objectivity in the land ownership disputes. 

We are currently experiencing an unprecedented worldwide demographic shift. People are moving to cities in a pursuit of jobs and economic prosperity, but while cities offer great opportunities they also present immense challenges. Due to lack of affordable housing many people are left to find shelter in large slum areas. Roughly 33 % of the urban population in developing countries is living in slums and with 2.7 billion people moving into cities by 2030, a proactive approach is needed if we are to improve the livelihoods of the working poor.

“It is a complicated planning challenge”, Deepak Bhavsar, Managing Director of Strategic Consulting at JLL, formerly Jones Lang LaSalle, states when explaining the current situation, “Slums develop because the migration pressure is so high that people cannot find adequate opportunities to find shelter.” People move to the cities for economic reasons: They wish to find a job. However, moving to a new city requires a certain transition period to establish oneself and gain economic stability, but our planning efforts do not account for this: “In planning there is no such thing as transit homes where migrants can come and stay until they find a suitable job that enables them to move up the ladder”, Deepak Bhavsar says, “In the absence of these kind of homes, and with high land values in cities like Mumbai and Delhi making it impossible for people at the bottom of the socio-economic pyramid to afford to buy or rent proper shelter, slums have developed.”

Having identified the core problem the solution also becomes apparent: We need to develop transit homes for first time migrants. But as one would expect, it is easier said than done.

Conflicting interests

Undertaking slum rehabilitation projects involves the need to reconcile a number of conflicting interests. Slum dwellers, private property developers, and the state are usually the biggest stakeholders in slum rehabilitation projects, but while they all wish to improve the living conditions in the slums, achievements in these programs can be variable.

One cause of conflict is the high valuations of the land most slums occupy. The potential redevelopment value of the Mumbai slums has, for example, been estimated to exceed $20 billion. Such a price tag generates interest with private property developers looking for profitable investments. While the government agencies do not have the funds to undertake the exercise themselves, the private sector, which does have the funds and can sense the real opportunity, is keen to get in. Still, it is a double edged sword as the challenge of protecting the interests of the slum dwellers while ensuring smooth, fair and efficient rehabilitation processes has to be addressed.

The old saying ‘Time is money’ definitely holds true in property development. Delays can end up costing millions of dollars so private developers have a strong incentive for having as smooth and quick a process as possible. Yet, working in slums is anything but smooth and quick, as Deepak Bhavsar explains: “The developer needs to seek consensus and gain the approval of each of the tenants; it is a very long process. Everyone needs to be on board for it to happen, and the development process can only begin once a consensus has been reached.”

The importance of quality data

In the process of seeking consensus one of the most pressing challenges vividly presents itself: lack of data. As explained by Deepak Bhavsar: “It is important that the baseline data is up to date. You need to know who is eligible for the resettlement: Who is the tenant, who is the landlord, what kind of economic activity is going on?” Without this kind of data it is impossible to devise an adequate plan and to assess the needs of the people living in the areas in question. Obtaining this data is, however, extremely difficult and costly due to the informal nature of these areas and the constant fluctuations in the number of residents.

The call for data is echoed by Carlo Ratti, Director of Senseable City Lab at MIT, when asked about the main priorities for city planners: “[City planners] should focus on three very important things: Data, data, and data. Data is essentially what can help us better understand our cities and hence allow us to respond to that knowledge.” We can only develop successful strategies if we are familiar with the conditions and mechanisms of the areas we are working with. In this respect, quality data is essential for all planning.

The challenges in obtaining quality data previously centered on gathering enough data points, but today, according to Carlo Ratti, the main challenge is that of proper mapping: “Because we have so much data today we need to find new ways of mapping it, in order to make sense of it.” A route worth exploring, according to Carlo Ratti, is that of cell phone data. Even though slum dwellers are amongst the poorest in the world, most of them have cell phones. Mapping the data from these cell phones in a precise and useful way could potentially provide real-time dynamic data, and access to such information could present the next major breakthrough in planning for the slum areas.

No silver bullet

We know a lot about how to address the problems of the emerging slums, but the complexity of the areas in question calls for in situ processes to mitigate the complex web of interests at stake. Getting a fuller and richer understanding of the individual slum areas are therefore essential for the success of future rehabilitation projects. Easily accessible quality data could potentially decrease project delays, minimize cost in the initial surveying phase, and increase satisfaction both among slum dwellers and developers.

However, it is no silver bullet. Developing informal areas are, and will always be, a complex matter. But an inclusionary process based on a rich understanding of the areas stemming from quality datasets will undoubtedly ease the process.