Assessment of Housing Conditions for a Developing Urban Slum using Geospatial Analysis: A Case Study of Durumi, Garki-2, Gishiri and Jabi of the City of Abuja, Federal Capital Territory of Nigeria

Yahaya Usman Badaru, Ahmed Sadauki Abubakar, Onuh Spencer, Akiode Olukemi Adejoke, Shuaibu Onimisi Salihu


The parameters used for urban slum classification are water source, accessibility types, wall materials, conditions and types of waste disposal, roof and roof trusses types, and cluster nature of the study areas as detected by NigeriaSAT-1 imagery data. Applications of remote sensing are best and better appropriate way to identify slums through the presence of the following features; housing density, structure, and roof composition. However, it was observed that the study areas had been in a condition of virtual slum before 2005. The results of housing conditions classification shows that slums are often associated and characterized by substandard housing structures, poor living conditions, narrow access that do not allow vehicles, experience a high rate of disease, poor water quality, window and door made from substandard material, and  unhealthy disposal of waste. In addition, the geo-statistical analysis also show positive relationship with the slum index; water 0.0536 (5%), solid and liquid waste 0.3707 (37%), wall to the building 0.7594 (76%), roof 0.3253 (33%), toilet wall 0.5313 (53%), kitchen wall 0.6020 (60%), door 0.3191 (32%), window 0.4255 (43%) and accessibility 0.3167 (32%). In the final analysis, it was observed that the methods agree largely with the areas classified as slum or squatter settlement. This conclusion was made based on the results of the housing conditions classifications, statistical analysis and cluster nature of the study areas displayed in palette of Arcmap-10.1 supervised classification. It is recommended that, this classification approach be used for assessing the state of housing conditions in urban slums.

Keywords: assessment, housing conditions, urban/city slum, geospatial algorithms

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ISSN (Paper)2224-3216 ISSN (Online)2225-0948

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