Illegal dumping, referring to the intentional and criminal abandonment of waste in unauthorized areas, has long plagued governments and environmental agencies worldwide. Despite the tremendous resources spent to combat it, the surreptitious nature of illegal dumping indicates the extreme difficulty in its identification. In 2006, the Construction Waste Disposal Charging Scheme (CWDCS) was implemented, regulating that all construction waste must be disposed of at government waste facilities if not otherwise properly reused or recycled. While the CWDCS has significantly improved construction waste management in Hong Kong, it has also triggered illegal dumping problems. Inspired by the success of big data in combating urban crime, this paper aims to identify illegal dumping cases by mining a publicly available data set containing more than 9 million waste disposal records from 2011 to 2017. Using behavioral indicators and up-to-date big data analytics, possible drivers for illegal dumping (e.g., long queuing times) were identified. The analytical results also produced a list of 546 waste hauling trucks suspected of involvement in illegal dumping. This paper contributes to the understanding of illegal dumping behavior and joins the global research community in exploring the value of big data, particularly for combating urban crime. It also presents a three-step big data-enabled urban crime identification methodology comprising ‘Behavior characterization’, ‘Big data analytical model development’, and ‘Model training, calibration, and evaluation’.

/pdf/big-data-analytics-to-identify-illegal-construction-waste-1jododaapv.pdf

Big data analytics to identify illegal construction waste dumping: A Hong Kong study

Quantifying the embodied carbon saving potential of recycling construction and demolition waste in the Greater Bay Area, China: Status quo and future scenarios.

An overview of waste crime, its characteristics, and the vulnerability of the EU waste sector:

The first countrywide register of illegal dump sites in Slovenia was created in 2010 and 2011 Due to its extensiveness it allows in-depth analyses of factors that affect illegal waste disposal Prior research has already proven the impact of roads on the incidence of illegal dumps, but in this paper we investigate if regional differences significantly influence its expression We consider the existing landscape typing, which divides Slovenia into 14 landscape-ecological types We find significant differences between landscape types in the attractiveness of roads for disposal They are especially pronounced when comparing the attractiveness of individual road categories

/pdf/attractiveness-of-roads-for-illegal-dumping-with-regard-to-3ybxc5es4m.pdf

Attractiveness of roads for illegal dumping with regard to regional differences in Slovenia

In his 2018 Stockholm prize winner lecture, Goldstein highlighted the need for problem-oriented policing (POP) to be not only effective but also fair. Contributing to the development of POP, this study examines how a wider perspective on problem-solving generally, and scoping in particular, can be adopted to address some of the growing challenges in 21st century policing. We demonstrate that the concept of ‘problem’ was too narrowly defined and that, as a result, many problem-solving models found in criminology are ill-structured to minimize the negative side-effects of interventions and deliver broader benefits. Problem-solving concepts and models are compared across disciplines and recommendations are made to improve POP, drawing on examples in architecture, conservation science, industrial ecology and ethics.

/pdf/the-problem-with-crime-problem-solving-towards-a-second-57w5gei88k.pdf

The Problem with Crime Problem-Solving: Towards a Second Generation Pop?

/pdf/fly-tipping-causes-incentives-and-solutions-4rcfgcc1ja.pdf

Fly-tipping: Causes, Incentives and Solutions

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Fly-tipping: Causes, Incentives and Solutions