Understanding Illegal Waste Dumping Behaviours with Multi-Source Big Data: Visualized Evidences from Hong Kong

Wendy M. W. Lee (), Weisheng Lu and Fan Xue
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Wendy M. W. Lee: The University of Hong Kong, Hong Kong Special Administrative Region
Weisheng Lu: The University of Hong Kong, Hong Kong Special Administrative Region
Fan Xue: The University of Hong Kong, Hong Kong Special Administrative Region

A chapter in Proceedings of the 24th International Symposium on Advancement of Construction Management and Real Estate, 2021, pp 1819-1830 from Springer

Abstract: Abstract Illegal dumping refers to the unauthorised disposal of waste in public or private land, which impacts on the surrounding environment. In literature, many studies on minor offences focused on qualitative methods such as questionnaire surveys, of which the findings might be confined to social expectation bias, small sample size, questionnaire design and limited applicability. This study aims at understanding illegal dumping behaviour records in the big picture of urban big data from multiple sources, including demography, geography, economy, and household. We georeferenced the penalty records from January 2014 to June 2019 in Hong Kong and connected them to other data sources. We found that old urban areas were more prone to fly-tipping of building debris and half of the districts most stricken with fly-tipping of waste predominantly comprising renovation waste had a higher proportion of population residing in owner-occupied properties. The levels of income and education were found to have no direct impact on the tendency to commit illegal dumping behaviours. The findings in this paper, therefore, provide directions for the government in formulating policies to fight against illegal dumping.

Keywords: Illegal dumping; Waste management; Behaviour analysis; Big data; Hong Kong (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-15-8892-1_127

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DOI: 10.1007/978-981-15-8892-1_127

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