Understanding fly-tipping in urban areas

A social-economic-spatial combinatorial approach enabled by geographically weighted random forest

Datos Bibliográficos

ID11368246
AutoresWeisheng Lu (0000-0001-7504-5316, University of Hong Kong, autor de correspondencia), Bing Yang (0000-0003-4827-0926, University of Hong Kong), Liang Yuan (0000-0002-4716-7073), Yuan Liang (0000-0001-8410-0815, University of Hong Kong), Ziyu Peng (0000-0002-2541-7537, University of Hong Kong)
Año2025
Volumen112
Páginas107858
Fecha de publicación2025-03-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaEnvironmental Impact Assessment Review (JOURNAL)
Identificadores de la revistaISSN: 0195-9255 • E-ISSN: 1873-6432
EditorialElsevier BV (PUBLISHER)
DOI10.1016/j.eiar.2025.107858
OpenAlexW4407246542
IdiomaEN
Citas recibidas2
Referencias citadas53

This study addresses the ongoing challenge of illegal dumping (‘fly-tipping’) in urban environments, specifically focusing on Hong Kong. Utilizing a substantial dataset of 1384 Fixed Penalty Notices issued from 2014 to 2021, the research presents a novel methodological approach that integrates Space Syntax Analysis (SSA) and Geographically Weighted Random Forest (GW-RF). The integration demonstrates robust predictive performance (R 2 = 0.809), significantly outperforming traditional models. Our findings reveal three fundamental patterns: social-economic pressures serve as consistent underlying drivers, urban spatial configuration creates distinct opportunity patterns, and waste disposal infrastructure acts as a local modifier of dumping behavior. These insights challenge conventional assumptions about illegal dumping locations and effectiveness of uniform solutions. The study provides an evidence-based foundation for developing targeted interventions, suggesting that effective policies must consider both stable social-economic pressures and variable spatial-infrastructural conditions. This research not only deepens the understanding of fly-tipping in Hong Kong but also proposes a robust, adaptable analytical framework for other urban contexts, contributing to both urban theory and practice in environmental management. • Integration of SSA and GW-RF reveals patterns in illegal dumping behaviors. • Model achieves strong predictive performance (R 2 = 0.809) in fly-tipping analysis. • Identified key social-economic-spatial factors influencing fly-tipping. • Decoded factor significance for illegal dumping insight. • Offered a framework for targeted urban waste management

Economic geography · Environmental planning · Environmental resource management · Geography · Consumer Retail Behavior Studies · Environmental Science · Human Mobility and Location-Based Analysis · Land Use and Ecosystem Services