Analysis of scale differences and spatial differentiation in the impact of the urban built environment on theft crime: A case study of Chicago, USA

doi:10.3969/j.issn.1004-9479.2026.02.20240529

World Regional Studies ›› 2026, Vol. 35 ›› Issue (2): 18-32.DOI: 10.3969/j.issn.1004-9479.2026.02.20240529

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Analysis of scale differences and spatial differentiation in the impact of the urban built environment on theft crime: A case study of Chicago, USA

Wei LI¹^,²^,³(), Yue DENG¹^,²^,³, Chao JIANG⁴, Wei LONG¹^,²^,³, Yupeng LUAN¹^,²^,³, Rixing HE¹^,²^,³()

^1.College of Resources Environment and Tourism, Ministry of Education, Capital Normal University, Beijing 100048, China
^2.Key Laboratory of Land Subsidence Mechanism and Mitigation, Ministry of Education, Capital Normal University, Beijing 100048, China
^3.Key Laboratory of 3D Data Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China
^4.College of Urban Economics and Public Administration, Capital University of Economics and Business, Beijing 100070, China

Received:2024-07-07 Revised:2024-12-10 Online:2026-02-15 Published:2026-02-27
Contact: Rixing HE

城市建成环境对盗窃犯罪影响的尺度差异及空间分异研究——以美国芝加哥为例

李薇¹^,²^,³(), 邓悦¹^,²^,³, 姜超⁴, 龙伟¹^,²^,³, 栾雨芃¹^,²^,³, 贺日兴¹^,²^,³()

^1.首都师范大学，资源环境与旅游学院，北京 100048
^2.首都师范大学，地面沉降机理与防控教育部重点实验室，北京 100048
^3.首都师范大学，三维数据获取与应用教育部重点实验室，北京 100048
^4.首都经济贸易大学城市经济与公共管理学院，北京 100070

通讯作者: 贺日兴
作者简介:李薇（1999—），女，硕士研究生，研究方向为犯罪地理和犯罪预测，E-mail：2220902139@cnu.edu.cn。
基金资助:
国家自然科学基金项目(42371251);国家自然科学基金项目(42001159);公安部科技强警基础工作专项(2021JC35)

Abstract

Abstract:

The impact of various factors on crime differs in scale and is closely related to the spatial heterogeneity effect. The research takes Chicago, USA as the study area and focuses on the spatial scales and spatial differentiation of the impact of urban built environment factors on theft crime by employing Multiscale Geographically Weighted Regression (MGWR). The results show that: ① Compared to classical OLS regression and traditional GWR models, MGWR not only offers higher model accuracy but also quantitatively demonstrates the differing scales of impact that various urban built environment factors have on theft crimes. ② The scales of impact that different urban built environment factors have on theft crimes show significant variations. Restaurants, schools, and shopping centres have the smallest scales of impact, which are considered local-scale variables; banks, bus stops, grocery stores, and bars are also local-scale variables, with their scales of impact increasing in that order; parks have the largest scale of impact, which is a global-scale variable; ③ The influence of urban built environment factors on theft crimes exhibits varying degrees of spatial heterogeneity. The presence of restaurants, shopping centres, bus stops, bars, and schools tends to promote theft crimes, but there are differences across regions; banks and grocery stores show both positive and negative effects on theft crimes depending on the region, while parks have a globally homogeneous inhibitory effect on theft crimes. The city's land use patterns affect the distribution of different built environment facilities, leading to diverse spatial distribution characteristics of the local-scale variables' impact on theft crimes. The research has shifted from previous global analysis focusing on variables or local analysis under a single scale to the analysis of heterogeneity of multiple variables across different spatial scales, which has positive significance in promoting the application of MGWR model in the field of geography of crime, and can provide a decision-making basis for Police and law enforcement agencies to plan different precise crime prevention strategies according to the differentiated impact space of influencing factors.

Key words: Multiscale Geographically Weighted Regression(MGWR), theft, scale effect, spatial heterogeneity, Chicago

摘要：

本文选取美国芝加哥市为研究区，基于多尺度地理加权回归模型（MGWR），针对城市建成环境因素对盗窃犯罪影响的作用尺度和空间分异进行了深入探究。结果发现：①相较于经典的OLS回归和传统的GWR模型，MGWR不仅具有更高的模型拟合精度，还能实现对不同城市建成环境因素对盗窃犯罪的差异化作用尺度的定量分析。②不同城市建成环境因素对盗窃犯罪影响的作用尺度存在明显差异。餐馆、学校和购物中心对盗窃犯罪的作用尺度最小，属于局部尺度变量；银行、公交站、杂货店和酒吧同样属于局部尺度变量，其作用尺度依次增大；相比之下，公园的作用尺度最大，表现出全局均质性特征。③建成环境因素对盗窃犯罪的影响存在不同程度的空间异质性。餐馆、购物中心、公交站、酒吧和学校的存在会促进盗窃犯罪的发生，但在不同区域存在差异；其中，银行和杂货店在不同区域对盗窃犯罪表现为正负两种影响效果，公园对盗窃犯罪具有全局均质的抑制作用。城市的土地利用方式影响不同建成环境设施的分布，进而导致局部尺度变量对盗窃犯罪的影响呈现出多样化的空间分布特征。研究从以往关注变量的全局分析或单一尺度下的局部分析，向综合多变量在不同空间尺度上的异质性分析拓展，对促进MGWR模型在犯罪地理学领域的应用具有积极意义，同时可为警务执法部门根据影响因素的差异化作用空间，制定分类精准化的犯罪防控策略提供决策依据。

关键词: 多尺度地理加权回归（MGWR）, 盗窃犯罪, 尺度效应, 空间分异, 芝加哥

Wei LI, Yue DENG, Chao JIANG, Wei LONG, Yupeng LUAN, Rixing HE. Analysis of scale differences and spatial differentiation in the impact of the urban built environment on theft crime: A case study of Chicago, USA[J]. World Regional Studies, 2026, 35(2): 18-32.

李薇, 邓悦, 姜超, 龙伟, 栾雨芃, 贺日兴. 城市建成环境对盗窃犯罪影响的尺度差异及空间分异研究——以美国芝加哥为例[J]. 世界地理研究, 2026, 35(2): 18-32.

Figures/Tables 9

References 56

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序号	变量名称	VIF
1	银行	1.369
2	公交站点	1.226
3	公园	1.029
4	餐馆	2.626
5	杂货店	1.333
6	学校	1.033
7	酒吧	1.647
8	酒店	1.457
9	购物中心	1.168

序号	变量名称	VIF
1	银行	1.369
2	公交站点	1.226
3	公园	1.029
4	餐馆	2.626
5	杂货店	1.333
6	学校	1.033
7	酒吧	1.647
8	酒店	1.457
9	购物中心	1.168

模型	AICc	R²	Adj.R²	Adj.R²提升量（提升百分比）
OLS	60 391.122	0.449	0.448
GWR	57 981.421	0.618	0.610	0.162（36.16%）
MGWR	10 798.434	0.759	0.739	0.291（64.96%）；0.129（21.15%）

模型	AICc	R²	Adj.R²	Adj.R²提升量（提升百分比）
OLS	60 391.122	0.449	0.448
GWR	57 981.421	0.618	0.610	0.162（36.16%）
MGWR	10 798.434	0.759	0.739	0.291（64.96%）；0.129（21.15%）

参数指标	OLS	GWR	MGWR
Moran’s I	0.21	0.11	－0.02
z-score	26.10	13.35	－1.50
P值	0.00^*	0.00^*	0.13

Analysis of scale differences and spatial differentiation in the impact of the urban built environment on theft crime: A case study of Chicago, USA

城市建成环境对盗窃犯罪影响的尺度差异及空间分异研究——以美国芝加哥为例

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References 56

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变量	GWR 带宽	MGWR 带宽	MGWR带宽占样本总量百分比
常数项^*	8 331	2 435	4.36%
学校^*	8 331	2 135	3.95%
餐馆^*	8 331	2 135	3.95%
购物中心^*	8 331	2 135	3.95%
银行^*	8 331	3 567	6.60%
公交站^*	8 331	4 614	8.53%
杂货店^*	8 331	7 092	13.12%
酒吧^*	8 331	17 330	32.06%
酒店	8 331	6 145	11.37%
公园	8 331	54 208	100%

解释变量	平均值	标准差	最小值	中值	最大值	系数变化范围
截距	－0.016	0.145	－0.285	－0.046	0.725	－0.285~0.725
银行^*	0.043	0.074	－0.237	0.030	0.522	－0.237~0.522
公交站点^*	0.075	0.052	－0.022	0.064	0.306	－0.022~0.306
购物中心^*	0.097	0.223	－0.936	0.077	1.361	－0.936~1.361
餐馆^*	0.195	0.192	－0.184	0.151	1.998	－0.184~1.998
杂货店^*	0.012	0.043	－0.111	0.008	0.170	－0.111~0.170
学校^*	0.030	0.207	－0.329	0.011	2.547	－0.329~2.547
酒吧^*	0.016	0.027	－0.035	0.023	0.059	－0.035~0.059
酒店	0.142	0.228	－0.041	0.061	1.150	－0.041~1.150
公园	-0.006	0.001	－0.008	－0.006	－0.003	－0.008~-0.003

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