1Allegheny County Department of Human Services, Pittsburgh, PA, USA.
Health Educ Behav. 2013 Oct;40(1 Suppl):87S-97S. doi: 10.1177/1090198113493090.
To develop a conceptual computational agent-based model (ABM) to explore community-wide versus spatially focused crime reporting interventions to reduce community crime perpetrated by youth.
Agents within the model represent individual residents and interact on a two-dimensional grid representing an abstract nonempirically grounded community setting. Juvenile agents are assigned initial random probabilities of perpetrating a crime and adults are assigned random probabilities of witnessing and reporting crimes. The agents' behavioral probabilities modify depending on the individual's experience with criminal behavior and punishment, and exposure to community crime interventions. Cost-effectiveness analyses assessed the impact of activating different percentages of adults to increase reporting and reduce community crime activity. Community-wide interventions were compared with spatially focused interventions, in which activated adults were focused in areas of highest crime prevalence.
The ABM suggests that both community-wide and spatially focused interventions can be effective in reducing overall offenses, but their relative effectiveness may depend on the intensity and cost of the interventions. Although spatially focused intervention yielded localized reductions in crimes, such interventions were shown to move crime to nearby communities. Community-wide interventions can achieve larger reductions in overall community crime offenses than spatially focused interventions, as long as sufficient resources are available.
The ABM demonstrates that community-wide and spatially focused crime strategies produce unique intervention dynamics influencing juvenile crime behaviors through the decisions and actions of community adults. It shows how such models might be used to investigate community-supported crime intervention programs by integrating community input and expertise and provides a simulated setting for assessing dimensions of cost comparison and intervention effect sustainability. ABM illustrates how intervention models might be used to investigate community-supported crime intervention programs.
开发一个概念性的基于计算代理的模型（ABM），以探索针对社区范围和空间聚焦的犯罪报告干预措施，以减少由青年实施的社区犯罪。
模型中的代理代表个体居民，并在二维网格上相互作用，该网格代表一个抽象的、非经验性的社区环境。青少年代理被赋予实施犯罪的初始随机概率，而成人被赋予随机概率来观察和报告犯罪。代理的行为概率会根据个人的犯罪行为和惩罚经历以及对社区犯罪干预措施的暴露情况而改变。成本效益分析评估了激活不同比例的成年人来增加报告和减少社区犯罪活动的影响。将社区范围的干预措施与空间聚焦的干预措施进行了比较，在空间聚焦的干预措施中，激活的成年人集中在犯罪高发地区。
ABM 表明，社区范围和空间聚焦的干预措施都可以有效地减少总体犯罪，但它们的相对有效性可能取决于干预措施的强度和成本。尽管空间聚焦的干预措施在局部地区减少了犯罪，但这些干预措施会导致犯罪转移到附近的社区。只要有足够的资源，社区范围的干预措施可以比空间聚焦的干预措施更大幅度地减少整个社区的犯罪犯罪行为。
ABM 表明，社区范围和空间聚焦的犯罪策略通过社区成年人的决策和行动产生独特的干预动态，影响青少年犯罪行为。它展示了如何通过整合社区投入和专业知识，使用此类模型来研究社区支持的犯罪干预计划，并提供一个模拟环境来评估成本比较和干预效果可持续性的维度。ABM 说明了干预模型如何用于研究社区支持的犯罪干预计划。
