Geographically Weighted Regression and Ordinary Least Squares in Assessing Urban Growth; A Case Study of Uyo Metropolis, Nigeria

The need for urban studies has grown in recent years as issues on urbanization continue to contribute to global processes. Sustainability of urban development is currently receiving more attention due to the fact that urban lands play significant roles in ecologically, socioeconomically, and culturally shaping global ecosystem. This paper performed GIS-based Ordinary Least Squares (OLS) regression and Geographically Weighted Regression (GWR) in order to analyse and explain urban growth in Uyo metropolis and its environs. The spatio-temporal land use/land cover analysis performed with Landsat satellite images using the maximum likelihood supervised classification showed that spatial extent of urban land had increased from 75.64 km2 to 128.56 km2 while all other land uses continued to diminish between 2003 to 2023. The OLS model averagely explained 50 % of the variations and relationships between identified urban drivers and urban growth with an adjusted R-squared value of 0.5172 while GWR explained over 70% of the variations in urban dynamics of Uyo metropolis with adjusted R-squared value of 0.7156. All the urban variables used in the analysis were statistically significant at 95% confidence interval. The study highlighted the effectiveness of remote sensing and GIS as useful tools in environmental modelling. It also highlights performance of OLS and GWR in assessing urban expansion and predicting urban growth patterns within a given region thereby providing powerful insights to city planners and developers.