Glasner Philip, Johnson Shane D, Leitner Michael
1Department of Geoinformatics-Z_GIS, University of Salzburg, Salzburg, Austria.
SynerGIS Informationssysteme GmbH, Vienna, Austria.
Crime Sci. 2018;7(1):9. doi: 10.1186/s40163-018-0083-7. Epub 2018 Aug 20.
In this paper, we introduce two methods to forecast apartment burglaries that are based on repeat and near repeat victimization. While the first approach, the "heuristic method" generates buffer areas around each new apartment burglary, the second approach concentrates on forecasting near repeat chain links. These near repeat chain links are events that follow a near repeat pair of an originating and (near) repeat event that is close in space and in time. We name this approach the "near repeat chain method". This research analyzes apartment burglaries from November 2013 to November 2016 in Vienna, Austria. The overall research goal is to investigate whether the near repeat chain method shows better prediction efficiencies (using a capture rate and the prediction accuracy index) while producing fewer prediction areas. Results show that the near repeat chain method proves to be the more efficient compared to the heuristic method for all bandwidth combinations analyzed in this research.
在本文中，我们介绍了两种基于重复和近重复受害情况来预测公寓盗窃案的方法。第一种方法是“启发式方法”，它围绕每起新的公寓盗窃案生成缓冲区；第二种方法则专注于预测近重复链环节。这些近重复链环节是指在空间和时间上接近的、由一起始发事件和（近）重复事件组成的近重复对所引发的事件。我们将这种方法称为“近重复链方法”。本研究分析了奥地利维也纳2013年11月至2016年11月期间的公寓盗窃案。总体研究目标是调查近重复链方法在产生更少预测区域的同时，是否显示出更好的预测效率（使用捕获率和预测准确性指数）。结果表明，在本研究分析的所有带宽组合中，近重复链方法比启发式方法更有效。
