基于多尺度时空图融合的警情预测模型

doi:10.11871/jfdc.issn.2096-742X.2026.02.012

数据与计算发展前沿 ›› 2026, Vol. 8 ›› Issue (2): 154-170.

CSTR: 32002.14.jfdc.CN10-1649/TP.2026.02.012

doi: 10.11871/jfdc.issn.2096-742X.2026.02.012

基于多尺度时空图融合的警情预测模型

琚子政¹(),陈鹏^2,^*(),隋晋光³,朱隆昇¹

¹ 中国人民公安大学，信息网络安全学院，北京 100038
² 中国人民公安大学，安全防范技术与风险评估公安部重点实验室，北京 100038
³ 中国人民公安大学，犯罪学院，北京 100038

收稿日期:2025-08-07 出版日期:2026-04-20 发布日期:2026-04-23
通讯作者: *陈鹏（E-mail: chenpeng@ppsuc.edu.cn）
作者简介:琚子政，中国人民公安大学，硕士研究生，研究方向为犯罪地理和警务数据分析。
本文主要工作为开展实验和论文撰写。
JU Zizheng, a Master’s candidate at the People’s Public Security University of China, focuses his research on crime geography and policing data analytics.
His primary contributions to this paper include designing and conducting the experiments, as well as drafting the manuscript.
E-mail: 1339884609@qq.com|陈鹏，中国人民公安大学，博士，教授，研究方向为数据警务技术、犯罪地理、时空热点分析等。
本文主要工作为定制研究计划。
CHEN Peng is a Professor and Ph.D. at the People’s Public Security University of China. His research interests encompass data policing technology, crime geography, and spatiotemporal hotspot analysis.
In this paper, he is responsible for designing the research plan.
E-mail: chenpeng@ppsuc.edu.cn
基金资助:
中国人民公安大学基本科研业务费项目(2024JKF04);高等学校学科创新引智基地项目(B20087)

A Police Incident Prediction Model Based on Multi-Scale Spatio-Temporal Graph Fusion

JU Zizheng¹(),CHEN Peng^2,^*(),SUI Jinguang³,ZHU Longsheng¹

¹ College of Information Technology and Cyber Security, People’s Public Security University of China, Beijing 100038, China
² Key Laboratory of Security Technology & Risk Assessment, People’s Public Security University of China, Beijing 100038, China
³ School of Criminology, People’s Public Security University of China, Beijing 100038, China

Received:2025-08-07 Online:2026-04-20 Published:2026-04-23

摘要/Abstract

摘要：

【目的】 精准的警情时空预测是优化警力资源配置和提升治安防控效能的关键支撑。【应用背景】现有研究方法普遍存在的尺度单一化建模局限、尺度特征适配不足的问题，为此本研究提出一种基于多尺度时空图融合的警情预测模型。【方法】 该模型通过构建行政区划与路网双尺度空间关联体系，实现警情时空分布异质性的多层次特征提取与动态融合。具体而言，本研究首先设计自适应邻接矩阵生成机制，以整合行政区宏观布局与路网微观拓扑之间的跨尺度空间关联；其次，通过基于多头注意力机制的层次化图卷积网络，实现不同尺度警情特征的动态交互与协同学习；最后，将提取的时空特征与多源城市数据融合，预测警情的时空分布。【结果】 本文以北京市朝阳区为例进行了预测实验。实验结果显示，所提模型有效克服了传统单尺度下空间异质性特征捕获不全的问题，其F1值和RMSE指标分别达到0.7729和0.4008。相较于基线模型，F1值提升了1.3%，RMSE下降了4.8%，效果最优。【结论】 本研究为多源时空数据融合提供了新的技术路径，对智慧警务建设和社会治安精准防控提供了重要的决策支持工具。

关键词: 110警情, 时空预测, 多尺度时空建模, 图注意力网络, 道路网络建模, 多模态特征融合

Abstract:

[Objective] Accurate spatio-temporal prediction of police incidents is crucial for optimizing resource allocation and enhancing crime prevention capabilities. [Application Background] Current approaches often suffer from limitations such as single-scale modeling and inadequate adaptation to multi-scale characteristics. To address these issues, this study proposes a novel police incident prediction model based on a Multi-Scale Spatio-Temporal Graph Fusion Network (MS-STGFN). [Methods] The model constructs a dual-scale spatial correlation system incorporating both administrative divisions and road networks, enabling multi-level feature extraction and dynamic fusion of spatio-temporal heterogeneities in incident distributions. Specifically, an adaptive adjacency matrix generation mechanism is designed to integrate cross-scale spatial correlations between macro administrative layouts and micro road network topologies. Subsequently, a hierarchical graph convolutional network incorporating a multi-head attention mechanism facilitates dynamic interaction and collaborative learning of incident features across different scales. Finally, the extracted spatio-temporal features are fused with multi-source urban data to predict the occurrence and distribution of incidents. [Results] Experimental studies conducted in Chaoyang District, Beijing, demonstrate that the proposed model effectively overcomes the limitations of traditional single-scale models in fully capturing spatial heterogeneity. It achieves an F1-score of 0.7729 and an RMSE of 0.4008, outperforming baseline models with a 1.3% improvement in F1-score and a 4.8% reduction in RMSE. [Conclusions] This study provides a new technical pathway for multi-source spatio-temporal data fusion and offers a significant decision-support tool for the development of smart policing and precise crime prevention strategies.

Key words: 110 police incidents, spatio-temporal prediction, multi-scale spatio-temporal modeling, graph attention network, road network modeling, multi-modal feature fusion

琚子政, 陈鹏, 隋晋光, 朱隆昇. 基于多尺度时空图融合的警情预测模型[J]. 数据与计算发展前沿, 2026, 8(2): 154-170.

JU Zizheng, CHEN Peng, SUI Jinguang, ZHU Longsheng. A Police Incident Prediction Model Based on Multi-Scale Spatio-Temporal Graph Fusion[J]. Frontiers of Data and Computing, 2026, 8(2): 154-170, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2026.02.012.

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参考文献 31

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特征类型	变量名	数据类型	取值范围/示例	描述说明
时间特征	month	整数	[1-12]	月份
	day	整数	[1-31]	日
	hour	整数	[0-24]	小时（24小时制）
	weekday	整数	[1-7]	周
气象特征	wind	浮点数	[0.1, …] (m/s)	风速
	temp	浮点数	[-4.7, …] (℃)	温度
	precipitation	整数	{0,1}	降水（0：无，1：有）
	pressure	浮点数	[1022.4, …]	气压
	humidity	整数	[17, …]	湿度
社会特征	is_holiday	整数	{0,1}	节假日标记（0：无，1：有）

特征类别	区域类型	子类别	特征指标
空间特征	道路网络	道路属性	单向通行、层级、桥梁、隧道、道路类型、长度
		人口分布	人口数量/密度
		POI分布	餐饮、景点、公共、企业、购物等13类POI数量/密度
	行政区划	行政区划属性	行政区划类型、面积、区划边界长度、邻接行政区划数量
		人口分布	人口数量/密度
		POI分布	餐饮、景点、企业、购物等13类POI数量/密度
		监管属性	派出所及警务工作站数量/密度
时间特征	道路网络/行政区划	日历信息	月份、日期、星期、节假日、小时
外部特征	道路网络/行政区划	气象数据	风速、温度、气压、湿度、降水

模型类型	模型名称	关键超参数配置
机器学习	随机森林	n_estimators=[100,200]；max_depth=[None,10,20]；class_weight=[“balanced”]
机器学习	XGBoost	n_estimators=[100,200]；learning_rate=[0.01,0.1]；max_depth=[3,5,7]
深度学习	AFM	attention_factor=8；l2_reg_linear=1×10^-5；l2_reg_embedding=1×10^-5
	DCN	dnn_hidden_units=（256,128,64）；dnn_dropout=0.5；l2_reg_embedding=1×10^-4
	NFM	dnn_hidden_units=（256,128,64）；dnn_dropout=0.5；l2_reg_embedding=1×10^-4
	DeepFM	dnn_hidden_units=（256,128,64）；dnn_dropout=0.5；l2_reg_embedding=1×10^-4
图神经网络	DSTGCN	learning_rate=0.001；weight_decay=0.001
	DSTGAT	learning_rate=0.001；weight_decay=0.001
	MS-STGFN	learning_rate=0.001weight_decay=0.001

模型	所有警情		盗窃类警情		纠纷类警情		伤害类警情
模型	F1值	RMSE	F1值	RMSE	F1值	RMSE	F1值	RMSE
RF	0.6572	0.5022	0.6157	0.5233	0.5907	0.5341	0.5998	0.5184
XGBoost	0.6598	0.5003	0.5689	0.5731	0.5398	0.5911	0.5401	0.5927
AFM	0.6788	0.4723	0.6912	0.4681	0.6573	0.4729	0.6744	0.4796
DCN	0.6932	0.4762	0.7025	0.4684	0.6628	0.4738	0.6879	0.4643
NFM	0.6763	0.4603	0.6861	0.4634	0.6684	0.4802	0.6752	0.4768
DeepFM	0.6892	0.4521	0.6983	0.4532	0.6521	0.4569	0.6735	0.4534
DSTGCN	0.7597	0.4351	0.7551	0.4443	0.7342	0.4355	0.7518	0.4249
DSTGAT	0.7631	0.4211	0.7593	0.4355	0.7556	0.4429	0.7644	0.4286
MS-STGFN	0.7729	0.4008	0.7701	0.4156	0.7684	0.4193	0.7754	0.4013

模型	所有警情			盗窃类警情
模型	准确率	F1值	均方根误差	准确率	F1值	均方根误差
消融1	0.7266	0.7303	0.4472	0.7218	0.7243	0.4524
消融2	0.7583	0.7403	0.4305	0.7537	0.7358	0.4372
消融3	0.7502	0.7439	0.4311	0.7459	0.7387	0.4379
消融4	0.7763	0.7635	0.4131	0.7704	0.7586	0.4198
消融5	0.7476	0.7392	0.4401	0.7428	0.7345	0.4453
消融6	0.7689	0.7497	0.4216	0.7642	0.7449	0.4283
消融7	0.7737	0.7658	0.4284	0.7691	0.7608	0.4352
消融8	0.7956	0.7729	0.4008	0.7931	0.7701	0.4156
模型	纠纷类警情			伤害类警情
模型	准确率	F1值	均方根误差	准确率	F1值	均方根误差
消融1	0.7136	0.7164	0.4638	0.7307	0.7328	0.4426
消融2	0.7449	0.7268	0.4483	0.7616	0.7437	0.4274
消融3	0.7368	0.7305	0.4486	0.7538	0.7476	0.4283
消融4	0.7627	0.7496	0.4304	0.7796	0.7658	0.4103
消融5	0.7342	0.7258	0.4567	0.7514	0.7428	0.4369
消融6	0.7553	0.7362	0.4385	0.7729	0.7535	0.4182
消融7	0.7602	0.7519	0.4454	0.7775	0.7696	0.4253
消融8	0.7862	0.7684	0.4193	0.7993	0.7754	0.4013

基于多尺度时空图融合的警情预测模型

A Police Incident Prediction Model Based on Multi-Scale Spatio-Temporal Graph Fusion

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