Exploring the Interactive Associations between Urban Built Environment Features and the Distribution of Offender Residences with a GeoDetector Model
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Dependent Variable
2.3. Selection of Independent Variable
3. Research Questions and Methodology
3.1. Research Questions and Hypotheses
3.2. Methodology
3.2.1. Preprocessing of Data
3.2.2. Kernel Density Estimation
3.2.3. Geographical Detectors Model
4. Results and Discussion
4.1. Portrait of Offenders
4.2. Spatial Distribution of Offender Residences
4.3. Results of GeoDetector Model
4.3.1. Factor Detection
4.3.2. Interaction Detection
4.4. Summary
4.5. Limitations and Future Research
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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FID | Residential Address | Date of Arrest | Longitude | Latitude |
---|---|---|---|---|
1 | No. 1, xx Road, A District, B City | 5 January 2020 | 11x.xx | 3x.xx |
2 | No. 2, yy Road, C District, B City | 16 March 2020 | 12x.xx | 2x.xx |
… | … | … | … |
Abbreviations | Attributes | Source | |
---|---|---|---|
Dependent variable | Count of offender residences | Bureau of Public Security, 2020 | |
Independent variables | Village | Area of urban village land use | Land Use Bureau, 2020 |
Factory | The density of factories (factories manufacturing clothing, belts, shoes, and cosmetics) | Baidu Maps, 2020 | |
Hotel | The density of hotels (hotels, guesthouses, and home stays) | Baidu Maps, 2020 | |
Agriculture | The density of agricultural facilities (agricultural tools shops, machinery repair shops, agricultural product processing shops, and agricultural labor agency shops) | Baidu Maps, 2020 | |
Warehouse | The density of warehousing (warehouses and logistics transfer stations) | Baidu Maps, 2020 | |
Entertainment | The density of entertainment places (KTVs, bars, net bars, and massage parlors) | Baidu Maps, 2020 | |
IT | The density of IT-related companies | Baidu Maps, 2020 | |
Mall | The density of stores and malls (shopping malls and convenience stores) | Baidu Maps, 2020 | |
Business | The density of business-leasing service companies (enterprise management services, legal services, consulting and investigation, advertising industry, and professional intermediary services) | Baidu Maps, 2020 | |
Bank | Density of banks | Baidu Maps, 2020 | |
POP | Population density | Mobile communication operator, 2020 |
Description | Interaction | Degree of Interaction Associations |
---|---|---|
Enhance, nonlinear | Very strong (interaction associations were larger than the sum of individual associations) | |
Independent | Independent (interaction associations equaled the sum of individual associations) | |
Enhance, bivariate | Strong (interaction associations were larger than the MAX of individual associations and smaller than the sum of individual associations) | |
Enhance/weaken, univariate | Weak (interaction associations were larger than the MIN of individual associations and smaller than the MAX of individual associations) | |
Weaken, nonlinear | Very weak (interaction associations were smaller than the MIN of individual associations) |
Independent Variables | q Value (95% CI) |
---|---|
Factory | 0.43 *** (0.4016–0.4525) |
Village | 0.38 *** (0.3590–0.4114) |
Hotel | 0.34 *** (0.3087–0.3624) |
Agriculture | 0.26 *** (0.2297–0.2834) |
Warehouse | 0.24 *** (0.2152–0.2686) |
Entertainment | 0.24 *** (0.2111–0.2643) |
IT | 0.23 *** (0.2016–0.2546) |
POP | 0.23 *** (0.2008–0.2538) |
Mall | 0.22 *** (0.1957–0.2484) |
Business | 0.21 ** (0.1856–0.2380) |
Bank | 0.16 ** (0.1342–0.1832) |
Village | Bank | Agriculture | Mall | Entertainment | IT | Warehouse | Factory | Hotel | Business | POP | |
---|---|---|---|---|---|---|---|---|---|---|---|
Village | 0.39 | ||||||||||
Bank | 0.52 | 0.16 | |||||||||
Agriculture | 0.51 | 0.34 | 0.26 | ||||||||
Mall | 0.56 | 0.27 | 0.39 | 0.22 | |||||||
Entertainment | 0.58 | 0.27 | 0.39 | 0.26 | 0.24 | ||||||
IT | 0.55 | 0.28 | 0.37 | 0.26 | 0.28 | 0.23 | |||||
Warehouse | 0.51 | 0.33 | 0.45 | 0.33 | 0.35 | 0.34 | 0.24 | ||||
Factory | 0.56 | 0.48 | 0.53 | 0.48 | 0.50 | 0.48 | 0.49 | 0.43 | |||
Hotel | 0.65 | 0.37 | 0.46 | 0.36 | 0.36 | 0.37 | 0.43 | 0.52 | 0.34 | ||
Business | 0.57 | 0.27 | 0.37 | 0.24 | 0.27 | 0.27 | 0.33 | 0.48 | 0.35 | 0.21 | |
POP | 0.55 | 0.28 | 0.37 | 0.29 | 0.30 | 0.30 | 0.35 | 0.48 | 0.37 | 0.28 | 0.23 |
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Wan, T.; Shi, B. Exploring the Interactive Associations between Urban Built Environment Features and the Distribution of Offender Residences with a GeoDetector Model. ISPRS Int. J. Geo-Inf. 2022, 11, 369. https://doi.org/10.3390/ijgi11070369
Wan T, Shi B. Exploring the Interactive Associations between Urban Built Environment Features and the Distribution of Offender Residences with a GeoDetector Model. ISPRS International Journal of Geo-Information. 2022; 11(7):369. https://doi.org/10.3390/ijgi11070369
Chicago/Turabian StyleWan, Tao, and Buhai Shi. 2022. "Exploring the Interactive Associations between Urban Built Environment Features and the Distribution of Offender Residences with a GeoDetector Model" ISPRS International Journal of Geo-Information 11, no. 7: 369. https://doi.org/10.3390/ijgi11070369
APA StyleWan, T., & Shi, B. (2022). Exploring the Interactive Associations between Urban Built Environment Features and the Distribution of Offender Residences with a GeoDetector Model. ISPRS International Journal of Geo-Information, 11(7), 369. https://doi.org/10.3390/ijgi11070369