论文标题
使用时空数据的犯罪预测
Crime Prediction Using Spatio-Temporal Data
论文作者
论文摘要
犯罪是对个人及其社会有害的犯罪罪行。显然可以理解犯罪活动的模式以防止他们。研究可以帮助社会预防和解决犯罪激活。研究表明，只有10％的罪犯犯下了总犯罪的50％。如果执法团队有早期的信息和有关城市不同点犯罪活动的早期信息和预识，他们可以更快地做出回应。在本文中，有监督的学习技术用于以更好的准确性预测犯罪。提出的系统通过分析包含先前承诺的犯罪及其模式记录的数据集来预测犯罪。该系统位于两种主要算法上 - i）决策树，ii）k -nearest邻居。随机森林算法和adaboost用于提高预测的准确性。最后，使用过采样用于更好的准确性。拟议的系统是由旧金山市十二年的犯罪活动数据集提供的。
A crime is a punishable offence that is harmful for an individual and his society. It is obvious to comprehend the patterns of criminal activity to prevent them. Research can help society to prevent and solve crime activates. Study shows that only 10 percent offenders commits 50 percent of the total offences. The enforcement team can respond faster if they have early information and pre-knowledge about crime activities of the different points of a city. In this paper, supervised learning technique is used to predict crimes with better accuracy. The proposed system predicts crimes by analyzing data-set that contains records of previously committed crimes and their patterns. The system stands on two main algorithms - i) decision tree, and ii) k-nearest neighbor. Random Forest algorithm and Adaboost are used to increase the accuracy of the prediction. Finally, oversampling is used for better accuracy. The proposed system is feed with a criminal-activity data set of twelve years of San Francisco city.
