Using an area wide analysis of contextual data to prioritize NMT infrastructure projects: case study Cape Town, South Africa

Improving mobility is seen as key to facilitating the economic uplift of the urban poor. In South Africa, the majority of urban poor live in the periphery of cities. They travel long distances at high cost to go to work and school and are dependent on public transport and non-motorized transport (NMT) (walking and cycling) for their travel needs. Prioritizing NMT infrastructure projects, especially in view of the extent of the need and limited budgets, poses a great challenge to local decision makers. This paper describes the application of a statistical clustering method to the results of a GIS-based Spatial Multiple Criteria Assessment (SMCA) of contextual data in a city or town to identify areas that are most suited for walking and cycling infrastructure. The method allows for a large amount of land use, socio-economic, environmental and transport data to be included into the assessment in a logical manner, and for statistically robust outputs. The method is demonstrated through the use of a case study in the city of Cape Town, South Africa. The results are analysed in relation to the current NMT planning being done in the city. It is able to identify infrastructure that should be prioritized, or may benefit from a possible realignment. The research demonstrates that contextual information should play a role in infrastructure provision decision-making processes, and shows how the sustainability concerns underlying integrated land use and transport planning can be put into effect within the traditional transport planning environment.