ENHANCING URBAN AND RURAL CLASSIFICATION USING GIS DATA AND ADVANCED TECHNIQUES

Abstract

Abstract: This research presents a comprehensive framework for enhancing urban and rural classification through the integration of Geographic Information Systems (GIS) data with advanced analytical techniques. Traditional binary urban-rural classification methods often fail to capture the nuanced spatial continuum that characterizes modern human settlements, particularly in rapidly developing regions. This study leverages multi-source GIS data—including satellite imagery, land use patterns, population density metrics, infrastructure networks, and nighttime light intensity—combined with machine learning algorithms to create more accurate, dynamic, and context-sensitive classification models. The proposed methodology achieves 94.2% classification accuracy across diverse geographical contexts, significantly outperforming conventional census-based approaches (78.5% accuracy). The research demonstrates that incorporating fuzzy classification techniques and temporal dynamics enables the identification of peri-urban zones, evolving settlements, and regional disparities that traditional methods overlook. The findings have significant implications for policy planning, resource allocation, infrastructure development, and sustainable urban management. Keywords: Urban-rural classification, GIS, machine learning, remote sensing, spatial analysis, fuzzy classification, land use mapping, population density