In order to explain how crimes are carried out, and why at a particular place and time and against a specific target, crime researchers increasingly engage with theory from behavioural ecology, in particular Optimal Foraging Theory (OFT). However, an overview of their main findings does not exist . Given the growing focus on OFT as a behavioural framework for structuring crime research, in this article we review the extant OFT-inspired crime research. Search in Google Scholar and Web of Science yielded 32 crime studies, which were grouped into four categories according to the focal decision being modelled. Empirical results largely support predictions made by OFT. There remains much potential for future crime research, however, in particular regarding the theoretical foundation of OFT in criminology, and through the application of contemporary extensions to OFT using specific tools developed for the study of animal foraging decisions.

In order to explain how crimes are carried out, and why at a particular place and time and against a specific target, crime studies increasingly harness theory from behavioural ecology, in particular Optimal Foraging Theory (OFT). However, an overview of their main findings does not exist. Given the growing focus on OFT as a behavioural framework for structuring crime research, in this article we review the extant OFT-inspired empirical crime research. Systematic search in Google Scholar and Web of Science yielded 32 crime studies, which were grouped into four categories according to their research topic. Empirical results largely support predictions made by OFT. However, there remains much potential for future OFT applications to crime research, in particular regarding the theoretical foundation of OFT in criminology, and through the application of contemporary extensions to OFT using specific tools developed for the study of animal foraging decisions.

Introduction Information on years of life lost (YLL) due to premature mortality is instrumental to assess the fatal impact of disease and necessary for the calculation of Belgian disability-adjusted life years (DALYs). This study presents a novel method to reallocate causes of death data. Materials and methods Causes of death data are provided by Statistics Belgium (Statbel). First, the specific ICD-10 codes that define the underlying cause of death are mapped to the GBD cause list. Second, ill-defined deaths (IDDs) are redistributed to specific ICD-10 codes. A four-step probabilistic redistribution was developed to fit the Belgian context: redistribution using predefined ICD codes, redistribution using multiple causes of death data, internal redistribution, and redistribution to all causes. Finally, we used the GBD 2019 reference life table to calculate Standard Expected Years of Life Lost (SEYLL). Results In Belgium, between 2004 and 2019, IDDs increased from 31% to 34% of all deaths. The majority was redistributed using predefined ICD codes (14-15%), followed by the redistribution using multiple causes of death data (10–12%). The total number of SEYLL decreased from 1.83 to 1.73 million per year. In 2019, the top cause of SEYLL was lung cancer with a share of 8.5%, followed by ischemic heart disease (8.1%) and Alzheimer’s disease and other dementias (5.7%). All results are available in an online tool https://burden.sciensano.be/shiny/mortality2019/. Conclusion The redistribution process assigned a specific cause of death to all deaths in Belgium, making it possible to investigate the full mortality burden for the first time. A large number of estimates were produced to estimate SEYLL by age, sex, and region for a large number of causes of death and every year between 2004 and 2019. These estimates are important stepping stones for future investigations on Disability-Adjusted Life Years (DALYs) in Belgium.

Objectives: Drawing upon optimal foraging theory, we examine graffiti writers’ individual target preferences to establish the diversity in their target choices (henceforth called “target specialization”). Ecological research implies that the total population of writers can consist of target specialists, generalists, or both. Target preferences are either similar or dissimilar among individuals.Methods: One year of graffiti removal data relating to 1,904 incidents committed by 263 individuals were extracted for a medium-sized city in Belgium. Individual target specialization and preferences were analyzed using ecological network methods.Results: The total diversity in target choices at the aggregate level is primarily the result of substantial between-individual variation. The results indicate that the total population of graffiti writers largely consists of target specialists, and can be divided into subgroups that share similar target preferences. Aggregate patterns of target selection do not accurately reflect individual variation in target choice specialization, at least for graffiti writing.Conclusions: We recommend future research to account for individual differences in target specialization. The patterns observed here are similar to those observed in animal ecology studies supporting the idea that crime patterns might correspond to common behavioral ecological patterns.