Large cities in developing countries are growing at a rapid rate. Their “slums” are increasing, too — perhaps even faster than official census data suggests, according to research out of TU Darmstadt and the German Aerospace Centre.
The fast growth of these cities, including the dense, unplanned developments referred to as slums, is putting significant stress on infrastructure. This stress means clean water is harder to come by, especially for a city’s poorest residents. Lack of access to clean water can cause a range of issues, including higher instances of disease and mortality. People also have less to go to work and school, since they must devote so much time to finding water.
The researchers from the Institute for Fluid Systems (FST) at TU Darmstadt and the German Aerospace Centre (DLR) hope to improve access to clean water for those living in slums by learning more about their development and using algorithms to plan infrastructure optimized for them.
The Morphologies of Slums
To achieve their goal, the researchers aim to create a system that’s scalable but customizable based on the needs of different areas. So, they used satellite data to look for patterns in the development of slums across cities.
The team’s analysis of the data from the DLR revealed the characteristic morphologies of slums. They’re typically dense, unplanned and contain small, low houses, which makes it possible to identify them on satellite imagery. Their analysis also revealed, based on the size of the slums, that the proportion of poor people in the cities was likely higher than census counts suggested.
The research also showed that slums are generally the same size, although many of their other characteristics vary.
Using Algorithms to Plan Water Infrastructure
To develop their plan for improving infrastructure in impoverished parts of expanding cities, the scientists used data from the satellite imagery and data mining to create a cost model, which they translated into a mathematical optimization model. They then used algorithms to develop a plan for a supply system.
Algorithms are necessary for this kind of task, the researchers said, because of the complexity it now involves. After all the calculations are complete, they produce a visualization of the system design complete with pipes, water tanks and vehicles for water transportation.
Completing all of these calculations takes several hours for an area of about 20 slums. Clustering the data can reduce the amount of time the process takes, the research team said, because it reduces the number of variables involved. This approach could be useful for regions like Dhaka, which has more than 1,000 slums.
The TU Darmstadt researchers are also now using a Turing mechanism to study how slums develop by exploring data on migration patterns and other variables. Based on the early stages of this study, they found that slums form when the population becomes large enough that the low-income population begins to spread out.
This provides further evidence, the researchers said, that mathematical formulas can explain social phenomena and contribute to bettering people’s situations. They hope to now include other disciplines in their research regarding access to clean water and expect to see more similar collaborative research efforts in the future.
As for the “discrete optimization” method they developed, it can be used to help design water infrastructure in developing cities around the world.
Because it focuses on infrastructure rather than just immediate needs and is scalable as cities change, this method could process more permanent solutions to the lack of access to clean drinking water in developing countries.
Written by Kayla Matthews, Productivity Bytes.