EXPLORING THE POTENTIAL OF CROWD SOURCED DATA TO MAP COMMUTER POINTS OF INTEREST: A CASE STUDY OF JOHANNESBURG

• Main Authors: T. Moyo, W. Musakwa
• Format: Article
• Language: English
• Published: Copernicus Publications 2019-06-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W13/1587/2019/isprs-archives-XLII-2-W13-1587-2019.pdf

Modern African cities are faced with various mobility and transportation challenges. In developing smart sustainable cities, city planners need to create a balance between supply and demand for public transportation. Development of multi-mobility mode models has contemporarily received a special interest in smart cities development. Globally, the use of bike sharing services to complete the first kilometre or last kilometre of the trip has been highly received, with commuters using either rail or road mobility modes for the middle section of their trip. Within the developing world context, the use of multi-mobility modes in daily commuting is still new, and little research has been done to guide this. Notwithstanding the influence of uncertainties and fragmentation over demand and supply in public transportation provision. In the South Africa context, various modes of public transportation have been developed which seek to be smart, sustainable and efficient such as the fast train (Gautrain), Bus rapid transport (Rea Vaya and Gaubus) and Bikes sharing platforms (Upcycles), however most of these modes are currently not spatially connected. Hence the researcher sought to develop a stepping stone in planning for future mobility demand. Using an explorative methodology, the authors collected quantitative and spatial data in the form of land-use data and crowd sourced data (from twitter) to map commuter points of interest in and around the city of Johannesburg. The results reveal hot and cold spots in the city. The hot spots reveal areas where commuters frequently travel to, and when overlaid with transportation data, we are able to identify potential locations to develop new transportation hubs as these will overtime become key points of interest.