论文标题
通过人口统计数据分析Foursquare和Streetlight数据对未来犯罪预测的影响
Analyzing the Impact of Foursquare and Streetlight Data with Human Demographics on Future Crime Prediction
论文作者
论文摘要
找到导致犯罪活动及其后果的因素对于改善定量犯罪研究至关重要。为了应对这一问题，我们从不同的角度和解释中研究了一系列特征。我们的研究旨在建立以数据驱动的模型来预测未来的犯罪事件。在本文中，我们建议使用路灯基础设施和FourSquare数据以及改善未来犯罪事件预测的人口特征。我们根据各种特征组合以及基线模型评估分类性能。我们提出的模型在加拿大哈利法克斯的每个最小地理区域进行了测试。我们的发现证明了整合多种数据来源以获得令人满意的分类性能的有效性。
Finding the factors contributing to criminal activities and their consequences is essential to improve quantitative crime research. To respond to this concern, we examine an extensive set of features from different perspectives and explanations. Our study aims to build data-driven models for predicting future crime occurrences. In this paper, we propose the use of streetlight infrastructure and Foursquare data along with demographic characteristics for improving future crime incident prediction. We evaluate the classification performance based on various feature combinations as well as with the baseline model. Our proposed model was tested on each smallest geographic region in Halifax, Canada. Our findings demonstrate the effectiveness of integrating diverse sources of data to gain satisfactory classification performance.
