GEOSPATIAL POPULATION MODELLING FOR OSOGBO LOCAL GOVERNMENT AREA, OSUN STATE, SOUTH WESTERN NIGERIA

Abstract

The growing population is putting pressure on natural resources and the environment, reducing agricultural land and causing food security issues. This study focuses on geospatial population modeling for Osogbo Local Government Area, Osun State, Nigeria, aimed at providing a more accurate population estimation to support urban planning. Traditional census methods often fail to capture the dynamic nature of population growth, necessitating the use of advanced geospatial techniques. The objectives were to develop a geospatial model for population estimation, assess its accuracy and reliability, and predict future population trends over the next 20 years. Satellite imagery from Landsat 4TM and Landsat 9 (OLI/TIRS) for 1991 and 2023, along with World-View and GeoEye4, was processed using ArcGIS 10.7 for land Use Land Cover (LULC) classification. Population distribution was estimated through the integration of satellite data with census figures, employing two population models. Data accuracy was ensured through ground-truth validation and statistical evaluation. The analysis revealed significant urban expansion between 1991 and 2023, with population estimates ranging from 589 to 1,450 across various locations. Model validation, performed using Root Mean Square Error (RMSE) and Pearson correlation, indicated a strong correlation between estimated and actual data, with Model 1 yielding a lower RMSE of 8.92. The study projected the population of Osogbo to reach 396,660 by 2043. These findings underscore the utility of geospatial techniques in population modelling, offering a robust tool for decision-makers in urban planning and resource allocation. The study recommends leveraging such models for sustainable infrastructure development in rapidly urbanizing regions.

Keywords

• Population Estimation; Land Use/Land Cover (LU/LC); Geospatial Analysis; Population Modelling