Forecasting Crime and Understanding its Causes. Applying Risk Terrain Modeling Worldwide

Marco Dugato, Leslie W. Kennedy

Risultato della ricerca: Contributo in rivistaArticolo in rivista

3 Citazioni (Scopus)

Abstract

We are pleased to introduce a special issue on risk terrain modeling (RTM) with expert contributions from around the world. The intent of this issue is to highlight the advances that have been made in the use of the RTM approach and to provide an opportunity to test its applicability in many different settings and geographies. The issue is also intended to provide insights for scholars who are interested in exploring the theoretical and methodological applications of RTM in relation to the unifying themes of environmental criminology and the spatial aspects of crime. Our intention was to address the different aspects of the inquiry inspired by the development of RTM, including theoretical tests, empirical verification, and practical applications. In addressing their research questions, authors were encouraged to present the rationale behind RTM, looking at contextual factors for making crime forecasts that are more useful for guiding interventions and more capable in identifying future possible crime concentrations. We added to this a planned solicitation of papers from researchers working in different countries, faced with diverse data issues, law enforcement practices, and analytical challenges. Indeed, the contributions of this special issue cover four continents, including North America (Andresen and Hodgkinson 2018; Piza et al. 2018), South America (Giménez-Santana et al. 2018a), Asia (Ohyama and Amemiya 2018), Northern Europe (Gerell 2018), Southern Europe (Dugato et al. 2018; Giménez-Santana et al. 2018b), and the transcontinental city of Istanbul connecting Southern Europe and the Middle-East (Onat and Gul 2018).
Lingua originaleEnglish
pagine (da-a)1-6
Numero di pagine6
RivistaEuropean Journal on Criminal Policy and Research
Volume24
DOI
Stato di pubblicazionePubblicato - 2018
Pubblicato esternamente

Keywords

  • risk terrain modeling

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