Explore the Correlation between Environmental Factors and the Spatial Distribution of Property Crime
Abstract
:1. Introduction
2. Materials and Methods
2.1. The Case Study
2.2. Data Sources and Preprocessing
2.3. Methodology
2.3.1. Average Nearest Neighbor
2.3.2. Kernel Density Estimation
2.3.3. Pearson Correlation
2.3.4. Bayesian Linear Regression
2.3.5. Best Subset Selection
2.3.6. Geo-Detector
- (1)
- Factor detector: The factor detector q value measures the SSH of a variable Y, or the determinant power of an explanatory variable X of Y; The calculation is;
- (2)
- Interaction detector: The interaction detector identifies the interactions between factors. By judging whether the joint action of two factors will increase or weaken the explanatory power of the variable (Y) or whether the effects of these factors on (Y) are independent of each other from Table 2. The method of judgment is to calculate the q value of two different driving factors on variable Y: q(Xi) and q(Xj), then calculates the interaction result of the q value of two different driving factors (q(Xi ∩ Xj): The new polygon distribution formed by the tangent of the two layers of the superimposed variables Xi and Xj), and finally compare the calculated results of q(Xi), q(Xj) and q(Xi ∩ Xj);
- (3)
- Ecological detection: The ecological detector identifies the difference of the impacts between two explanatory variables. As the test index, F is defined as:
3. Results and Analysis
3.1. The Analysis of Spatial Distribution Heterogeneity of Property Crime
3.2. Bayesian Linear Regression Analysis
3.2.1. Multi-Collinearity Test and Pearson Analysis
3.2.2. Regression Model with the Optimal Combination of Independent Variables
3.2.3. Analysis of Independent Factors on Crime Distribution
3.3. The Analysis of Geo-Detector Results
3.3.1. The Dominant Factors on Property Crime
3.3.2. The Differences between Factors
3.3.3. The Interaction of Factors on Property Crime
3.3.4. Influence of Factors on the Stability of System Interpretation
4. Discussion
5. Conclusions
- (1)
- The distribution pattern of property crime cases in the main urban area of Lanzhou was not non-random; it had spatial heterogeneity, more specifically, spatial agglomeration. Half of the crime cases were concentrated in 4.33% of the area, and even up to 72.22% of crime cases were highly concentrated in 8.60% of the study area. Shop density, hotel density, entertainment density and house price were the four most powerful drivers of the spatial distribution of property crimes in the main urban area of Lanzhou, and the regions with high values of these four factors seemed more attractive to property crime. The attractive targets, which are from the concentration of wealth and the lack of capable guardianship, contribute to the offenders’ access to suitable, insufficiently guarded targets, whose spatial heterogeneity leads to the spatial distribution of property crime;
- (2)
- The distance to police stations, distance to main roads, and distance to bus stops had a weakly negative effect on property crime. The light intensity in the study area had a positive effect on crime committed. The relationship between the light intensity and crime has different correlation explanations in temporal projection and spatial projection. Factors that inhibit human activity, such as severe cold, will reduce the correlation coefficient between the night light intensity and the crime. We can make a prediction that the correlation coefficient between the night light intensity and the crime of cities in the cold zone should be lower than that in the temperate zone or the tropics;
- (3)
- There was a normal distribution curve between the number of property crimes and the PE ratio of the community. With the increase of PE ratio, the number of property crimes first increased and then decreased, and the peak PE ratio was 5.25. Gated communities were designed with stronger guardianship, which effectively deterred the potential offenders and reduced property crime. For property offenders, they were less likely to choose areas with stronger guardianship;
- (4)
- The results of the factor interaction indicated that multiple environmental factors might not work in an independent manner; factor interactions showed a two-factor enhancement, and the explanatory power of the factor interaction for property crime was much greater than any single factor. As an important catalyst, shop density had the strongest interaction with other factors. The shop density gradient influenced the degree of interpretation of spatial heterogeneity of property crime. With the continuous increase of shop density, the solution of model factors tended to be unstable, and the fitting accuracy of various factors to crime was improved. Even so, the maximum R2 among the regression functions was only 0.6492, that is, the crime action is a process formed by multiple microscopic roles of people and the environment. For its inherent complexity, it is difficult to simulate or predict the crime event location with high precision.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variables | Code | Units | Year |
---|---|---|---|
Property crime | Y | - | 2014–2016 |
Population density | X1 | person/km2 | 2016 |
Night lighting | X2 | - | 2016 |
Road network density | X3 | m/km2 | 2016 |
Distance to the police stations | X4 | m | 2016 |
Distance to main roads | X5 | m | 2016 |
Distance to bus stops | X6 | m | 2016 |
Average house price | X7 | ¥/m2 | 2016 |
Entertainment density | X8 | - | 2016 |
Hotel density | X9 | - | 2016 |
Shop density | X10 | - | 2016 |
Graphical Representation | Description | Interaction |
---|---|---|
| Nonlinear-weaken | |
| Uni-weaken | |
| Bi-enhance | |
| Independent | |
| Nonlinear-enhance |
Variable Codes | Number of Samples | Minimum | Maximum | Mean | Standard Deviation | VIF | 1/VIF |
---|---|---|---|---|---|---|---|
X1 | 1454 | 0.996 | 19,992.685 | 1668.100 | 2633.275 | 1.507 | 0.664 |
X2 | 1454 | 0 | 0.685 | 0.034 | 0.070 | 1.457 | 0.686 |
X3 | 1454 | 0 | 37,632.758 | 6223.789 | 5638.334 | 2.261 | 0.442 |
X4 | 1454 | 42.391 | 5530.103 | 1500.513 | 1034.103 | 1.756 | 0.569 |
X5 | 1454 | 0.169 | 3613.028 | 724.546 | 692.411 | 2.702 | 0.370 |
X6 | 1454 | 5.357 | 3067.615 | 593.087 | 520.993 | 3.114 | 0.321 |
X7 | 1454 | 0 | 28,635 | 3767.508 | 5769.121 | 2.176 | 0.460 |
X8 | 1454 | 0 | 124 | 1.810 | 5.760 | 1.956 | 0.511 |
X9 | 1454 | 0 | 115 | 2.100 | 7.487 | 1.804 | 0.554 |
X10 | 1454 | 0 | 572.000 | 23.218 | 56.957 | 2.293 | 0.436 |
Y | 1454 | 0 | 63 | 1.518 | 4.427 | - | - |
X1 | X2 | X3 | X4 | X5 | X6 | X7 | X8 | X9 | X10 | |
---|---|---|---|---|---|---|---|---|---|---|
X1 | ||||||||||
X2 | Y | |||||||||
X3 | Y | Y | ||||||||
X4 | N | Y | N | |||||||
X5 | Y | N | Y | Y | ||||||
X6 | N | N | Y | Y | N | |||||
X7 | Y | Y | N | Y | Y | Y | ||||
X8 | Y | Y | Y | Y | Y | Y | Y | |||
X9 | Y | Y | Y | Y | Y | Y | Y | Y | ||
X10 | Y | Y | Y | Y | Y | Y | Y | Y | Y |
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Sun, L.; Zhang, G.; Zhao, D.; Ji, L.; Gu, H.; Sun, L.; Li, X. Explore the Correlation between Environmental Factors and the Spatial Distribution of Property Crime. ISPRS Int. J. Geo-Inf. 2022, 11, 428. https://doi.org/10.3390/ijgi11080428
Sun L, Zhang G, Zhao D, Ji L, Gu H, Sun L, Li X. Explore the Correlation between Environmental Factors and the Spatial Distribution of Property Crime. ISPRS International Journal of Geo-Information. 2022; 11(8):428. https://doi.org/10.3390/ijgi11080428
Chicago/Turabian StyleSun, Lijian, Guozhuang Zhang, Dan Zhao, Ling Ji, Haiyan Gu, Li Sun, and Xia Li. 2022. "Explore the Correlation between Environmental Factors and the Spatial Distribution of Property Crime" ISPRS International Journal of Geo-Information 11, no. 8: 428. https://doi.org/10.3390/ijgi11080428