Predictive modeling of transport infrastructure space for urban growth phenomena in developing countries’ cities: a case study of Kano-Nigeria
Research output: Contribution to journal › Article › peer-review
- Ahmadu Bello University
Global urbanization has the most tremendous negative effects on the changing landscapes in many developing countries’ cities. It is necessary to develop appropriate monitoring techniques for tracking transport space evolution. The work explores the impacts of urban growth dynamics of transport space over the past decades as a basis for predicting future space demands in Kano, Nigeria. Three epochs of Landsat images from 1984, 2013 and 2019 were processed, classified and analyzed. Spatial classifications of land-use/land-cover (LULC) types in Kano include transport space, built-up areas, vegetation, farmland, bare land and water. The data analysis involves model calibration, validation and prediction using areas using the hybrid modeling techniques-cellular automata-Markov (CA-Markov) in IDIRISI SELVA 17.0 and remote-sensing ARC-GIS 10.7 softwares. The result finds significant expansion of transport and built-up areas while other LULC receded throughout the entire study period. Predictive modeling of transport infrastructure shows spatial expansion by 345 km2 (3.9%) and 410 km2 (11.7%) in 2030 and 2050 respectively. Kappa reliability indices of agreement (KIA) classified images and ground maps were 85%, 86% and 88%, respectively, for 1984, 2013 and 2019 time series. The calibration quality met the 80% minimum suggested in literature for the spatial-temporal track and prediction of urban growth phenomena.
|Number of pages||20|
|Publication status||Published - 31 Dec 2020|
- Land use land cover change, Mobility infrastructures, Spatial and temporal modeling, Sustainable future cities, Transport, Urban growth dynamics