开发犯罪预测模型的考虑因素和衡量其准确性的新方法
Considerations for developing predictive models of crime and new methods for measuring their accuracy
摘要：
近二十年来，开发时空犯罪预测模型，并在较小程度上开发其准确性和运作效率的衡量标准，一直是一个活跃的研究领域。尽管要求对模型性能进行严格和独立的评估，但此类研究很少。在本文中，我们认为，研究不应该集中在寻找一个预测模型或一个措施，这是最合适的，在任何时候，而是认真考虑的几个因素，影响选择的模型和措施的选择，找到最好的措施和最好的模型，为手头的问题。我们认为，因为每个问题都是独特的，重要的是要制定措施，使从业者有能力输入的选择和偏好，是最适合手头的问题。我们开发了一种新的措施，称为惩罚预测准确性指数（PPAI），赋予这种灵活性。我们还建议使用预期效用函数，以适合于给定问题的方式结合多个措施，以评估模型对多个标准。我们进一步建议使用平均对数分数（ALS）的措施，这是适合于许多犯罪模型和措施的准确性不同于现有的措施。这些措施可与现有措施一起使用，以提供一种更全面的手段来评估时空犯罪预测模型的准确性和潜在效用。
英文摘要：
Developing spatio-temporal crime prediction models, and to a lesser extent, developing measures of accuracy and operational efficiency for them, has been an active area of research for almost two decades. Despite calls for rigorous and independent evaluations of model performance, such studies have been few and far between. In this paper, we argue that studies should focus not on finding the one predictive model or the one measure that is the most appropriate at all times, but instead on careful consideration of several factors that affect the choice of the model and the choice of the measure, to find the best measure and the best model for the problem at hand. We argue that because each problem is unique, it is important to develop measures that empower the practitioner with the ability to input the choices and preferences that are most appropriate for the problem at hand. We develop a new measure called the penalized predictive accuracy index (PPAI) which imparts such flexibility. We also propose the use of the expected utility function to combine multiple measures in a way that is appropriate for a given problem in order to assess the models against multiple criteria. We further propose the use of the average logarithmic score (ALS) measure that is appropriate for many crime models and measures accuracy differently than existing measures. These measures can be used alongside existing measures to provide a more comprehensive means of assessing the accuracy and potential utility of spatio-temporal crime prediction models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
