Divyani Kohli
Advanced image analysis
Choice of study areas based on interest/background of student and additional data availability.
According to UN-HABITAT (2016), the biggest proportion of world’s total slum population is from the developing world. Every year, world’s slum population is increased by about 6 million people leading to greater demand of provision for shelter, employment and urban services. To achieve improvement in conditions, there is a need to have adequate spatial information of slum areas. Proliferation of these settlements to a large extent, symbolize the systemic failure of the urban land use planning process and land registration systems. Information on the underlying causes and patterns of growth can help intervene and plan more inclusive cities
The objective of this project will be to use existing knowledge on the existence of slums and the ontological framework from the Generic Slum Ontology (GSO) to model the growth of slums. The purpose is to combine the indicators from GSO as well as identify the underlying land administration processes responsible for such growth. This will be done by identifying indicators and using spatial models to replicate the phenomenon.
Kohli, D., Sliuzas, R., Kerle, N., & Stein, A. (2012). An ontology of slums for image-based classification. Computers, Environment and Urban Systems, 36(2), 154-163. doi:10.1016/j.compenvurbsys.2011.11.001