犯罪预测软件

犯罪预测软件

By
. .
犯罪预测软件是由美国加州圣克拉拉大学的数学教授乔治·莫勒及其率领的团队设计的一款软件。它可用来预测案件的发生时间和地点。警方在这套预测软件的指导下，有针对性地进行巡逻，可大幅降低犯罪率。 中文名: 犯罪预测软件 设计团队:乔治·莫勒及其率领的团队 国家: 美国 意义:在指导下，有针对性地进行巡逻 简介 犯罪预测软件是由美国创业公司PredPol开发的一套犯罪预测程序，它能根据此前犯罪活动发生的时间和地点进行预测，同时参考与犯罪行为和犯罪模式有关的社会学信息。这套软件并非用来预测谁会成为罪犯，而是用来预测哪些地区即将发生犯罪事件。 设计者 犯罪预测软件的设计者是加州圣克拉拉大学的数学教授乔治·莫勒及其率领的团队。

设计原理

某些犯罪事件有很强的可预测性。例如，一起入室盗窃案件之后，几天内附近地区也可能发生同类案件，和地震后发生余震的规律相似。2010年，该软件设计团队基于地震后预测余震的原理，以预测余震的方程为基础，设计了预测犯罪软件。该软件系统会为用户呈现一张城市地图，根据某一地区过往的犯罪活动统计数据，借助特殊算法，计算出某地发生犯罪的概率，犯罪类型，以及最有可能犯罪的时间段。红框标注的区域——面积500×500英尺（约合150×150米）——就是有可能发生犯罪活动的地区。此外，这一系统还会为用户呈现一张热图，标注出哪些地区更有可能发生盗车案、入室行窃案和抢劫案。

预测内容

该软件要预测的犯罪事件有三种：入室盗窃、汽车盗窃和盗窃车内物品。软件每天预测三种案件各10起，并将预测的案发地点局限在方圆150平方米以内，然后每天晚上更新数据并进行第二天的预测。

测试实验

模拟测试

在模拟测试中，设计团队让预测犯罪软件根据2004年—2005年任意一天的入室盗窃案件数据，预测第二天可能发生的同类案件。结果软件预测出了第二天所有入室盗窃案件中四分之一的发生地点和时间。

实地测试

2011年，这套犯罪预测软件在美国加州海滨城市圣克鲁兹进行了首次实行测试。圣克鲁兹警方在这套预测软件的指导下，有针对性地进行巡逻，期望警察的存在能震慑可能的罪犯。即使不能阻止犯罪，警察也能尽快赶到，援救受害者、逮捕罪犯。2011年末，洛杉矶警方也对这套软件进行了测试。

测试结果

两地警察局的测试结果均显示这套犯罪预测软件拥有不俗的预测能力，可大幅降低犯罪率。在洛杉矶，使犯罪预测软件的地区的犯罪率降低了13%，而同一时间段内，洛杉矶全市的犯罪率增长了0.4%。在圣克鲁兹，2011年7月的盗窃犯罪比2010年同期减少了27%。

作用

1、该软件能够分析已发生犯罪的时间地点，预测“犯罪余震”会在什么时间发生在什么地方。2、能够减少文书工作，提高警方的工作效率，让他们将精力放在他们最擅长的事情上。3、预防犯罪，降低犯罪率。

实际应用

犯罪预测软件是一项全新的警务技术，有望应用到美国各地的执法部门。不过，这款软件的应用也面临一系列不利因素，例如紧张的预算、官僚作风和美国的文化，让警察接受和适应此类型的新技术需要一个时间和过程。此外，这款软件并不能取代警察。在进入锁定区域进行调查时，仍需要派遣优秀的警察。

Predictive Policing Technology

The PredPol Algorithm

PredPol is based on a decade of detailed academic research into the causes of crime pattern formation. That research successfully linked several key aspects of offender behavior to a mathematical structure that is used predict how crime patterns will evolve from day-to-day, from moment-to-moment. The mathematics looks complicated, but the behaviors upon which the math is based are very understandable. There are three aspects of offender behavior that make their way into our model.
  1. Repeat victimization, which describes – taking burglary as an example – that if a house is broken into today, the risk that it is broken into tomorrow actually goes up. This is because it is “rational” for offenders to return to the places where they have been successful before. It makes less sense to go to some other unknown house where they don’t know if the house is empty of people, they don’t know how hard it is to break in, and they don’t know what there is to be stolen. The house they broke into two or three days ago is much less risky.
  2. Near-repeat victimization, which recognizes that not only is your own house at greater risk of being broken into again, but your neighbor’s house is also at greater risk. Your neighbor is a lot like you: they they have similar socio-economic status, work similar hours, have a house a lot like yours and are going to have much the same stuff to steal. The offending ‘script’ the offender used to break into your house maps to your neighbor’s house almost perfectly.
  3. Local search ties it all together. We know that offenders rarely travel very far from their key activity points such as their home, work and play locations, meaning that crimes tend to cluster together.
The actual patented algorithm used by PredPol is displayed below: PredPol uses data from your agency’s records management system (RMS) to pull current and historical crime data. We then feed this into our machine-learning algorithm to create our predictions. We work with you and your RMS vendor to make sure that the data we use is accurate and complete. We only use 5 data points for each incident to generate our predictions:
  • Incident Identifier – For each crime, we need a unique identifier, such as docket number, incident ID or anything else used by the department to uniquely identify the crime.
  • Crime or Event Type – The violation code and/or crime description assigned to a particular incident type as used in your RMS.
  • Location of Incident – For best accuracy, latitude and longitude are desired. Your latitude and longitude must use the WGS 84 coordinate system. If latitude and longitude are not available, then the complete address of the incident is required. A complete address is Street Number, Street Name, City, State/Region.
  • Timestamps with Start and End Date/Time for Incident – We use these two fields because in some cases the exact date and time that the crime occurred is not known. For example, an auto theft may occur between midnight and 8 AM, or a burglary may occur over a weekend.PredPol calculates the incident occurrence time by taking the midpoint between beginning date/time and ending date/time. Incidents with a span of more than 72 hours between the beginning and ending date/time are excluded because it reduces the accuracy of our predictions.
  • Record Modified Date/Time for Incident – This is an optional field, but where possible we also request that you include a “record modified” date/time field to allow us to catch RMS records that may have changed (i.e. crime code has been reclassified).