Socio-economic survey data from 36 slums
Updated: Feb 19, 2018
New, extensive data on slums in Bangalore provide more insights into poverty in India, which can lead to a better slum policies and poverty reduction.
This study is based on a field survey of 36 slums in Bangalore, India and involves collection of highly granular data at the level of individual slum dwellers. The results were published on Friday, 9 January in the journal Nature Scientific Data and will significantly advance well-being in slums by generating new knowledge about underlying spatial and social process.
Participatory Data Collection
The data collected includes around 267,894 data points spread over 242 questions for 1107 households and can facilitate interdisciplinary research on spatial and temporal dynamics of urban poverty.
In 2010, an estimated 860 million people were living in slums worldwide with around 60 million added to the slum population between 2000 and 2010. In order to address and create slum development programs and poverty alleviation methods, it is necessary to understand the needs of these communities. Therefore, we require data with high granularity in the Indian context. Unfortunately, there is a paucity of highly granular data at the level of individual slums. Previous studies, dominantly based on aggregated data and low spatial resolution, is deemed unsatisfactory for the theoretical advancement of the field. "We collected the data in partnership with the slum dwellers in order to overcome the challenges such as validity and efficacy of self-reported data. Our survey in Bangalore covered 36 slums across the city, chosen based on stratification criteria which included the location of the slums, size of the slum, ethnicity and the religious profile" says Prof. Peter Sloot, Director of Institute for Advanced Studies, University of Amsterdam. "With this data we can build high-resolution computational models to gain a better understanding of the development and growth of slums in India" says Michael Lees, Assistant professor at the Computational Science Lab at the UvA.
Focus on Horizontal Inequality
This study combined three academic disciplines (sociology, geography and computer science) into one research project. It involves socio-demographic and spatial study of slums using methods from geographical information system and (agent-based) computer simulation. By combining the social and spatial constructs, this study provided a more complete synthesis of the problem, which can potentially lead to a deeper understanding and, consequently, better approaches for tackling the challenge of slums. "We have investigated Tilly's theory of group segregation and how it reproduces or reinforces inequality within the slums of Bangalore. Our results show that targeting horizontal inequality (as compared to vertical inequality) may increase the rate of successful interventions in slums." says Dr. Debraj Roy, postdoctoral fellow in the project.
Using these insights from the unique dataset the team has developed an agent-based model, namely DYNASLUM
The team have found that high rate of home leaving among young adults is the key determinants for the large variation in the life cycle of slum households. Therefore, reducing home leaving among young adults will reduce the formation number of new slum households and contribute to a higher but stable household size. This will lead to efficiency and higher per capita resource consumption when building capacity for slum development (resettlement colonies) as policy makers would be able to plan for a stable household size.
The project will evaluate wider impacts, including water infrastructure, water management, and sanitation practices, increased formal employment, and decreased violence against women and girls, who will no longer have to travel long distances to find clean water or access toilets will be evaluated. The three-year project will lead to evidence-based interventions relevant to Bangalore, India. The team is building a high-resolution decision support system which will allow policy makers to evaluate their policies ex-ante.