eprintid: 10051936 rev_number: 26 eprint_status: archive userid: 608 dir: disk0/10/05/19/36 datestamp: 2018-11-28 12:20:04 lastmod: 2021-10-15 22:30:06 status_changed: 2018-11-28 12:20:04 type: article metadata_visibility: show creators_name: Groff, ER creators_name: Johnson, SD creators_name: Thornton, A title: State of the Art in Agent-Based Modeling of Urban Crime: An Overview ispublished: inpress divisions: UCL divisions: B04 divisions: C05 divisions: F52 keywords: Agent-based modeling, Strengthening criminological theory, Urban crime, Model documentation note: © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). abstract: OBJECTIVES: Agent-based modeling (ABM) is a type of computer simulation that creates a virtual society and allows controlled experimentation. ABM has the potential to be a powerful tool for exploring criminological theory and testing the plausibility of crime prevention interventions when data are unavailable, when they would be unethical to collect, or when policy-makers need an answer quickly. This paper takes stock of the current literature to discuss the potential contributions of ABM, assess current practice, identify shortcomings that threaten the validity of findings using ABM, and to make suggestions regarding the construction and communication of future work using ABM. METHODS: We systematically searched major databases to find all publications using ABM to simulate urban crime patterns and coded publications to quantify the following information: (1) characteristics of the publication, the model and the agents, (2) model purpose, (3) crime type investigated, and (4) interrogation of the model via sensitivity testing and validation. RESULTS: After sifting papers according to our inclusion criteria, we identified and reviewed 45 publications. Models informed by the opportunity theory framework dominated. Most publications lacked detail sufficient to enable replication. Many did not include clear a rationale for modeling choices, parameter selection or calibration. Rarely were parameters calibrated using empirical data. Model validation was limited and inconsistent across papers. CONCLUSIONS: ABM offers significant potential for criminological enquiry. However, at present, the lack of model detail reported in publications makes it difficult to assess where sufficient evidence exists to support—and where gaps limit—the development of models that reflect extant conditions and offender decision-making. For the field to progress, as a minimum, standardized reporting that encourages transparency will be necessary. date: 2018-02-23 official_url: https://doi.org/10.1007/s10940-018-9376-y oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green article_type_text: Journal Article verified: verified_manual elements_id: 1565951 doi: 10.1007/s10940-018-9376-y language_elements: English lyricists_name: Johnson, Shane lyricists_name: Thornton, Amy lyricists_id: SJOHN86 lyricists_id: AETHO54 actors_name: Johnson, Shane actors_name: Flynn, Bernadette actors_id: SJOHN86 actors_id: BFFLY94 actors_role: owner actors_role: impersonator full_text_status: public publication: Journal of Quantitative Criminology issn: 0748-4518 citation: Groff, ER; Johnson, SD; Thornton, A; (2018) State of the Art in Agent-Based Modeling of Urban Crime: An Overview. Journal of Quantitative Criminology 10.1007/s10940-018-9376-y <https://doi.org/10.1007/s10940-018-9376-y>. (In press). Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10051936/1/Johnson_State%20of%20the%20Art%20in%20Agent-Based%20Modeling%20of%20Urban%20Crime.%20An%20Overview_VoR.pdf