Objectives: Assessing the predictive accuracy of using prior crime, place attributes, ambient population, community structural, and social characteristics, in isolation and combined when forecasting different violent and property crimes. Method: Using multilevel negative binomial regression, crime is forecasted into the subsequent year, in 50-m grid-cells. Incidence rate ratio (IRR), Prediction Accuracy Index (PAI), and Prediction Efficacy Index (PEI*) are interpreted for all combined crime generators and community characteristics. This study is partially a test of a crude version of the Risk Terrain Modeling technique. Results: Where crime has been in the past, the risk for future crime is higher. Where characteristics conducive to crime congregate, the risk for crime is higher. Community structural characteristics and ambient population are important for some crime types. Combining variables increases the accuracy for most crime types, looking at the IRR. Taking the geographical area into account, crime history in combination with both place- and neighborhood characteristics reaches similar accuracy as crime history alone for most crime types and most hotspot cutoffs. Conclusions: Crime history, place-, and neighborhood-level attributes are all important when trying to accurately forecast crime, long-term at the micro-place. Only counting past crimes, however, still does a really good job.

中文翻译：

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评估犯罪历史作为预测因素：探索瑞典马尔默的暴力和财产犯罪热点

目标：评估在预测不同暴力和财产犯罪时单独和组合使用先前犯罪、地点属性、周围人口、社区结构和社会特征的预测准确性。方法：使用多级负二项式回归，在 50 米网格单元中预测下一年的犯罪情况。发病率比 (IRR)、预测准确度指数 (PAI) 和预测功效指数 (PEI*) 针对所有组合的犯罪产生者和社区特征进行解释。这项研究部分是对风险地形建模技术的原始版本的测试。结果：过去发生过犯罪的地方，未来犯罪的风险就更高。在有利于犯罪的特征集中的地方，犯罪的风险就更高。社区结构特征和周围人口对于某些犯罪类型很重要。从 IRR 来看，组合变量可以提高大多数犯罪类型的准确性。考虑到地理区域，对于大多数犯罪类型和大多数热点截止点，结合地点和社区特征的犯罪历史记录与单独的犯罪历史记录相似的准确性。结论：在尝试准确预测微观场所的长期犯罪时，犯罪历史、地点和社区级别的属性都很重要。然而，仅计算过去的犯罪行为仍然非常有效。