Examining factors affecting illegal waste dumping using gis-based spatial regression analysis

Abstract

This study investigates the primary factors driving illegal municipal waste dumping in developing countries, focusing on the question: What socioeconomic, demographic, and spatial factors most influence illegal waste dumping practices? In the context of urbanizing regions like Semarang City, Indonesia, where inadequate waste management infrastructure exacerbates environmental pollution and greenhouse gas emissions, understanding these factors is critical. The research aims to identify key determinants of illegal dumping to inform targeted waste management strategies. Using spatial regression methods—Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR)—the study analyzes data from 177 subdistricts, incorporating eight variables such as elevation, population density, and waste collection facilities. Results indicate that elevation (positively correlated) and population density (negatively correlated) significantly influence illegal dumping, with GWR outperforming OLS by increasing the adjusted R-squared from 0.24 to 0.61, highlighting spatial heterogeneity. The article employs a mixed-method approach, combining quantitative spatial analysis with literature reviews to discuss findings and propose solutions. Key findings suggest that higher elevations and lower population densities areas, often rural or mountainous, are prone to illegal dumping due to limited infrastructure access. These insights provide a foundation for policymakers to reduce illegal waste dumping by enhancing decentralized waste management systems and targeting high-risk areas in metro cities like Semarang.