Identification and Analysis of Slum Characteristics

Master programme course individual project

Identification and Analysis of Slum Characteristics

Master programme course individual project

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In this project, I was tasked to assume the position of GIS and remote sensing analyst to assess similarities and differences of slums within and across Rio de Janeiro, Brazil and Nairobi, Kenya.


I used Generic Slum Ontology (GSO) approach developed by Kohli (2012) to compare slums with seleced spatial indicators: environs, settlement, and object. To satisfy all indicators, I implemented several GIS techniques.

  • I used Shuttle Radar Topography Mission (SRTM) data and classified its slope distribution, assuming that slums commonly developed in places that are not considered as “safe” by settlement planner, one of which reasons is steep degree of slope (Kuffer et al., 2016).
  • I performed buffer zones of 1 kilometers from all provided slums vector data to determine which landcover types overlaid those zones, assumed that people living in slums commonly looking for place to work and ways to improve their socioeconomic life, which makes them tend to build their living spaces close to job opportunities such as commercial or industrial areas. (Kohli et al., 2012).
  • I compared the vector density of slums and formal residence building from residential areas using 400 x 400 AOI within each, assuming that slums are known to have high density of building to its unplanned nature (Kohli et al., 2012).
  • I created a heat map from number of nodes extracted from road vectors, assuming that slums, being informal settlements, have irregular pattern of access roads which tends to have more nodes (Kohli et al., 2012).
  • I used bounding box orientation analysis for all building vectors including the slums, assuming slums commonly contain highly dense buildings with lack of proper plan, which leads to the buildings tend to have dissimilar main directions from each other (Kohli et al., 2012).

Reference:

Kohli, D.; Sliuzas, R.V.; Kerle, N.; Stein, A. An ontology of slums for image-based classification. Comput. Environ. Urban Syst. 2012, 36, 154–163.

Kuffer, M., Pfeffer, K., Sliuzas, R., 2016. Slums from space-15 years of slum mapping using remote sensing. Remote Sens (Basel) 8.

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