Ability of crime, demographic and business data to forecast areas of increased violence
Ability of crime, demographic and business data to forecast areas of increased violence
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DOI：
10.1080/17457300.2018.1467461
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发表时间：
2018-01-01
影响因子：
2.3
通讯作者：
Sumner, Steven A.
中科院分区：
文献类型：
--
作者：
Bowen, Daniel A.;Mercer Kollar, Laura M.;Sumner, Steven A.
确定暴力增加的地理区域和时间段对于预防规划非常重要。这项研究比较了多个数据源的表现，以前瞻性预测人际暴力增加的领域。我们使用了一个大都市县 2011-2014 年人际暴力（凶杀、袭击、强奸和抢劫）的数据，并预测了人口普查街区组层面和一个月移动时间窗口内的暴力行为。随机森林模型的输入包括警察部门的历史犯罪记录、美国人口普查局的人口数据以及许可企业的管理数据。在 279 个区块组中，利用所有数据源的模型被发现可以前瞻性地提高前 5% 最暴力区块组月份的识别（阳性预测值 = 52.1%；阴性预测值 = 97.5%；敏感性 = 43.4%；特异性 = 98.2%）。使用简单输入的预测模型可以帮助社区更有效地在地理上集中暴力预防资源。
Identifying geographic areas and time periods of increased violence is of considerable importance in prevention planning. This study compared the performance of multiple data sources to prospectively forecast areas of increased interpersonal violence. We used 2011-2014 data from a large metropolitan county on interpersonal violence (homicide, assault, rape and robbery) and forecasted violence at the level of census block-groups and over a one-month moving time window. Inputs to a Random Forest model included historical crime records from the police department, demographic data from the US Census Bureau, and administrative data on licensed businesses. Among 279 block groups, a model utilizing all data sources was found to prospectively improve the identification of the top 5% most violent block-group months (positive predictive value = 52.1%; negative predictive value = 97.5%; sensitivity = 43.4%; specificity = 98.2%). Predictive modelling with simple inputs can help communities more efficiently focus violence prevention resources geographically.