Remote Sensing and Geospatial Data Analytics
Toledo, OH
I am a geospatial professional with 3+ years of experience in solving diverse sets of problems. Currently, I work as a Research Scientist, specializing in data science applications for remote sensing. In my day today, I develop statistical models for tree species classification, use Py6S for radiation calibration or perform a literature review on GHG emissions, to name a few. I have held various geospatial data-centric positions where my responsibilities varied from physical data collection to curation to modeling.
Introduction and methodology:
To understand the relationship between urban poverty and housing, it is necessary (though by no means sufficient) to understand the spatial footprint of what is called “slums” in official state policy and plans. Yet “slums” are often precisely left out of official plans because they exist in tension with official logics of planning, property, and law even as they play a vital role in housing workers that build and run our cities. Not having data about the land area under “slums” also enables public perception of the “capture” of vast areas of land by the income-poor urban families.
The map above has been made by using lists of notified and non-notified “slums” created by various government departments in New Delhi. It combines lists from the Food and Civil Supplies department, the Delhi Urban Shelter Improvement Board, and the Delhi Development Authority. Details of the source is available in Bhan (2016) and Dupont (2008) among others. These lists were used to ascertain “slum” locations and names. Each site was then visited in person by the authors and boundaries verified using GPS co-ordinates, thus enabling a calculation of area under “slums” instead of just having a map that used point locations. See here: Bhan (2016) In the Public’s Interest. Orient Blackswan, Delhi; Dupint (2008) Slum Demolitions in Delhi since the 1990s.
Key Findings
The 2011 Census indicates that 11- 30% of all households in NCT Delhi are classified as living in “slums.” The map shows that these households live in only 0.6% of the total land area of the city, and in only 4% of all land zoned residential under the Delhi Master Plan 2021. The inadequacy of access to land for housing by income-poor urban families is clearly evident.
Possible Limitations:
Since official boundaries of “slums” are not made publicly available, the estimation of boundaries by physical visits to the site may underestimate the area under slums marginally, since researchers use their best judgment to mark the boundaries of the slum. We do not imagine this difference to be significant since most “slum” areas are bounded by other built form that make their edges clear. Google Earth images have been used to demarcate areas and share the limitations to analysis based on such spatial resolution.