犯罪时空预测方法研究综述与展望

doi:10.12082/dqxxkx.2021.200247

地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (1): 43-57.doi: 10.12082/dqxxkx.2021.200247

犯罪时空预测方法研究综述与展望

顾海硕¹^,^*, 陈鹏¹(), 李慧波²

1.中国人民公安大学信息网络安全学院,北京 102600
2.社会安全风险感知与防控大数据应用国家工程实验室,北京 100043

收稿日期:2020-05-19 修回日期:2020-06-29 出版日期:2021-01-25 发布日期:2021-03-25
通讯作者: 顾海硕,陈鹏 E-mail:chenpeng@ppsuc.edu.cn
作者简介:顾海硕（1995—）,男,硕士生,浙江嘉兴人,研究方向为警务大数据分析。E-mail: 603263780@qq.com
基金资助:
北京市自然科学基金项目(9192022);社会安全风险感知与防控大数据应用国家工程实验室主任基金项目

Overview and Prospect for Spatial-Temporal Prediction of Crime

GU Haisuo¹^,^*, CHEN Peng¹(), LI Huibo²

1. School of Information Network Security, People's Public Security University of China, Beijing 102600 China
2. National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data (PSRPC), Beijing 100043, China

Received:2020-05-19 Revised:2020-06-29 Online:2021-01-25 Published:2021-03-25
Contact: GU Haisuo,CHEN Peng E-mail:chenpeng@ppsuc.edu.cn
Supported by:
National Natural Science Foundation of Beijing(9192022);National Engineering Laboratory Director Fund for Social Security Risk Perception and Prevention and Control of Large Data Applications

摘要/Abstract

摘要：

犯罪时空预测作为预测警务的核心支撑技术,自2000年左右至今得到了快速的发展。本文介绍了犯罪时空预测的实践背景和理论基础,将犯罪时空预测解构为利用历史案件的时空位置、时空环境和个体行为等要素,结合相应的算法模型预测未来案件时空分布的过程。然后,从输入要素的视角对当前的犯罪时空预测方法进行了总结和归纳,将其划分为基于案件时空位置信息的犯罪时空预测、基于时空环境要素的犯罪时空预测,以及融合行为轨迹和时空环境要素的犯罪时空预测3种类型,详细总结了不同类型犯罪时空预测的方法原理,并从适应场景和预测效果等方面对不同的方法模型进行了比较。最后,结合当前的大数据技术发展趋势,对未来的犯罪时空预测进行了展望。本文认为犯罪时空预测未来需要从数据角度重点解决输入数据的体系融合、粒度细化和新型数据融合等问题,从模型优化角度应着重提高多源异构数据融合能力,平衡模型的可解释性与预测效果。

关键词: 犯罪预测, 犯罪时空风险, 大数据, 预测警务, 预测技术, 环境犯罪学

Abstract:

As the core technology of predictive policing, Spatial-Temporal (ST) prediction of crime has developed rapidly from around 2000 to the present. We introduce the basic theory of ST prediction of crime at the beginning. We regard the ST prediction method of crime as a process combining corresponding models to predict the ST distribution of crimes in the future and deconstruct it into relationships between three objects: case, ST backcloth, and individual behavior. Then, based on the input factors of prediction models, we sum up three current main methods, including ① the prediction method based on the information of cases' ST location, ② the prediction method based on the backcloth and the information of cases' ST location, and ③ the prediction method based on individual behavior, the backcloth, and the information of cases' ST location. We further summarize the mechanisms of different methods in detail respectively. In addition, we compare and analyze each method based on their applicable scenarios and predictive capacities. Finally, with the development of big data technology, we present solutions to improve current prediction methods, that are to construct a data-fusion system, refine data granularity, and integrate new types of data. For model optimization, we need to improve the ability of integrating heterogeneous data from multiple sources and balancing the interpretability and predictive ability of models.

Key words: crime prediction, crime spatio-temporal risk, big data, predictive policing, predictive methods, environmental criminology

顾海硕, 陈鹏, 李慧波. 犯罪时空预测方法研究综述与展望[J]. 地球信息科学学报, 2021, 23(1): 43-57.DOI:10.12082/dqxxkx.2021.200247

GU Haisuo, CHEN Peng, LI Huibo. Overview and Prospect for Spatial-Temporal Prediction of Crime[J]. Journal of Geo-information Science, 2021, 23(1): 43-57.DOI:10.12082/dqxxkx.2021.200247

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表5

不同研究人员所采用的输入变量、模型、对比形式及预测效果"

输入变量	模型	对比形式	效果	提出/使用者	研究时间/年
案件：犯罪数据时空静态类：POI数据时空动态类：浮动车数据	线性回归模型负二项回归模型 GWNBR	模型间对比	基于地理加权回归的负二项回归模型效果最佳^[85]	Wang等^[84] Wang等^[85]	2016 2017
案件：犯罪数据时空静态类：POI数据时空动态类：签到数据	负二项回归模型线性回归模型随机森林模型	模型间对比	随机森林模型预测效果最佳^[86]	Shakila Khan Rumi等^[86]	2019
案件：盗窃案件数据时空静态类：POI数据空间静态时间动态类：基站信令数据	离散选择模型	是否增加非时空静态类数据	增加非时空静态类数据可以提升预测精度^[80]	Song等^[80]	2019
案件：侵财类案件数据时空静态类：警务对策数据、土地利用数据、人口普查数据、经济普查数据空间静态时间动态类：流动人口数据	BP神经网络模型 GA-BP神经网络模型	模型间对比	改进的GA-BP模型预测效果更佳^[87]	Li等^[87]	2017
案件：单类犯罪数据、公共服务投诉数据空间静态时间动态类：气象数据时空动态类：浮动车数据、POI签到数据	TCP CSI ARMA LASSO 线性回归模型时空多任务学习模型	模型间对比	TCP能取得最优预测效果^[83]	Zhao等^[83]	2017
案件：警情数据时空静态类：人口网格数据空间静态时间动态类：基站信令数据时空动态类：浮动车数据、地铁刷卡数据	负二项回归模型	是否增加非时空静态类数据	增加非时空静态类数据可以提升预测精度^[81]	Song等^[81]	2018
案件：多种类犯罪案件数据时空静态类：人口统计数据空间静态时间动态类：场馆使用数据时空动态类：浮动车数据、地铁刷卡数据	随机森林模型梯度提升模型 Extra-Tree模型	不同数据对结果作用效果对比;模型间对比	不同类型案件对增加非时空静态类数据的敏感度不同,但都能提升预测效果;不同犯罪类型预测并没有一个完全最优的模型^[82]	Kadar等^[82]	2018

表5

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方法类型	输入变量	主要模型	提出时间
基于案件时空位置信息的犯罪时空预测	案件时空位置信息（主要为空间栅格数据）	犯罪临近重复模型核密度估计模型自激点震荡模型机器学习模型	20世纪90年代
基于时空环境要素的犯罪时空预测	案件时空位置信息,时空环境背景要素（分为空间栅格数据和网络数据）	风险地形建模贝叶斯预测模型离散选择模型张量模型机器学习模型图传播模型时空搜索窗模型	20世纪90年代
融合行为轨迹和时空环境要素的犯罪时空预测	案件时空位置信息,时空环境背景要素,主体行为轨迹（主要为空间栅格数据）	离散选择模型机器学习模型	2015年前后

方法	预测效果	提出/使用者	研究时间/年
逻辑回归与神经网络结合的模型	神经网络模型相比逻辑回归模型有更高的直接命中率但收效甚微,二者结合的模型能平衡预测精度和直接命中率^[58]	Rummens等^[58]	2017
DeepCrime	DeepCrime模型在多种预测场景下均表现出了相比支持向量机（SVR）、ARIMA、逻辑回归、多层感知机、TriMine、GRU、Wide&Deep等7种模型更佳的预测效果^[59]	Huang等^[59]	2018
GSRNN时空预测模型	GSRNN时空预测模型兼顾了时空关联性,相比仅基于LSTM的神经网络模型具有更好的预测效果^[60]	Wang等^[60]	2018
基于随机森林的模型	加入环境协变量的模型相比未加入的模型预测效果更佳^[29]	Liu等^[29]	2019
基于LSTM的时空预测模型	基于LSTM的神经网络时空预测模型相比ARIMA和随机森林模型预测效果更佳^[61]	Pan^[61]	2019

方法	预测效果	提出/使用者	研究时间/年
基于网络的K邻近法（Network K-function）	基于网络的K邻近法相比基于平面的方法更加符合实际案件时空分布^[75]	Lu等^[75]	2007
基于网络的入室盗窃模型	基于网络的模型明显优于非网络模型^[73]	Davies等^[73]	2013
基于网络的时空核密度估计（NTKDE）	基于网络的模型在预测精度及效果上明显强于基于平面的模型^[72]	Rosser等^[72]	2017
NTKDE 门控局部扩散网络模型（GLDNet）	门控局部扩散网络模型预测效果优于基于网络的时空核密度估计模型^[72,74,76]	Zhang等^[72,74,76]	2019

方法	预测效果	提出/使用者	研究时间/年
基于网络的犯罪时空分析（NT-STAC）基于网络的空间扫描统计（NT-SaTScan）	其他条件相同情况下,基于网络的方法总体强于基于平面的方法^[77]	Shiode等^[77]	2011
基于网络的空间搜索窗犯罪时空分析模型（S_NT-STAC）基于网络的时空搜索窗犯罪时空分析模型（ST_NT-STAC）	其他条件相同情况下,预测效果上,基于时空的模型优于基于空间的模型^[78]	Shiode等^[78]	2013
时间搜索窗犯罪分析模型（TAC）基于网络的犯罪空间搜索窗分析模型（SAC_NT）基于网络的犯罪时空搜索窗分析模型（STAC_NT）	其他条件相同情况下,基于网络的方法总体强于基于平面的方法;其他条件相同情况下,在预测效果上,基于时空的模型优于基于空间的模型优于基于时间的模型^[79]	Shiode等^[79]	2015

犯罪时空预测方法研究综述与展望

Overview and Prospect for Spatial-Temporal Prediction of Crime

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