- freely available
ISPRS Int. J. Geo-Inf. 2018, 7(9), 357; https://doi.org/10.3390/ijgi7090357
2. Literature Review
- Do vehicle thefts occur more frequently in specific areas and during specific hours?
- What is the relationship between physical or social factors and vehicle theft patterns?
3. Data and Methods
3.1. Research Area
3.2. Data Source
3.3.1. Temporal analysis
3.3.2. Spatial analysis
- Concentration tendency analysis can display central and dispersion tendencies of the crimes by using a standard deviational ellipse. The mean center of a standard deviational ellipse was the average X- and Y-coordinate of all the features in the study area. The size of the ellipse suggested the degree of dispersion. The larger the size, the more dispersed the crimes were. The axis of the ellipse indicated the orientation of the dispersion.
- Spatial autocorrelation analysis can help find hotspots and cold spots of the crimes. The analysis units for the spatial autocorrelation were produced by the overlay of the block layer, which was defined by roads, and the layer of administrative boundaries of village or neighborhood committees, which took both the division of roads and administration into consideration. Then, the cases were spatially joined to the layer of the analysis units, and the number of cases in each unit was calculated. This, in turn, was used as the value of the unit’s centroid in the autocorrelation analysis.Two spatial autocorrelation indicators were used, the global indicator and the local indicator. The global indicator (Moran’s I) measured the autocorrelation of crimes in the entire research area , and the local indicator (Gi*) measured the autocorrelation between one analysis unit and the neighboring units . Moran’s I varies between −1 and 1. Proximity to 1 means positive spatial correlation. Proximity to −1 indicates negative spatial correlation. Proximity to 0 reflects random distribution. If the Gi* of a unit is close to 0, the values of events are randomly distributed around the unit. A positive Gi* means that high values cluster around the unit, forming a “hotspot”, and a negative Gi* means that low values cluster around the unit, forming a “cold spot”. The larger the absolute value of Gi*, the more clustered the values are.Conceptualization of the spatial relationship of the spatial autocorrelation is a fixed distance band, which was set to 1000 m. The definition of this distance band was based on the standard in Chinese urban planning and the current urban environments. In China, the environments of areas enclosed by main roads are more similar than the environments outside of these areas. Under Chinese urban planning standards, the distance between the intersections of main roads is usually approximately 1000 m.
- Hierarchical nearest-neighbor clustering in CrimeStat III was used to produce the nearest deviational ellipses and explore the clustering of crimes at different spatial levels. CrimeStat III is a Windows-based, spatial statistics crime analysis program that interfaces with most desktop GIS programs. It provides supplemental statistical tools to aid law enforcement agencies and criminal justice researchers in their crime mapping efforts . It employs a random nearest-neighbor distance (random NN) that is the expected nearest-neighbor distance if the distribution is spatially random. The default minimum number of points per cluster is 10. The length of the long ellipse axis is one standard deviation. Using the selected criteria, CrimeStat III constructs the first-order clusters (NNH1) of the points. Then, these are treated as “points” and clustered to form second-order clusters (NNH2). This process is repeated until no further clustering can be conducted. We obtained clusters of three orders: NNH1, NNH2, and NNH3. For clusters, the later the order, the more macro the spatial scale of the cluster will be.
- Frequency statistics were used in the analysis of temporal and spatial distributions of crime in different land use zones in the PNA.
- To examine the relationship between the spatial distribution of vehicle thefts and environmental factors, a negative binomial regression model was carried out using the number of vehicle thefts in the area under the police station’s jurisdiction as the dependent variable and the environmental factors, including physical and non-physical factors, as the independent variables. Negative binomial regression fits a model for a nonnegative, count-dependent variable. In this model, the count variable was generated by a Poisson-like process, except that the variation was allowed to be greater than that of a true Poisson. This extra variation is referred to as over-dispersion . Because the number of vehicle thefts is a discrete and non-negative integer, the Poisson regression model was an appropriate choice for processing the data. However, the variance and mean analysis of the number of vehicle thefts indicated that these data were likely to be over-dispersed. For example, the variance of the number of NMVT had a mean of approximately 170, while, for MVT, the mean was about 54. These analyses suggest that using the negative binomial regression model was an appropriate alternative. Before the negative binomial regression was conducted, these independent factors were screened and some of them that were highly correlated were excluded. The factors that were finally selected were permanent population size, floating population size, number of commercial places (representing the potential targets of thefts), number of bus stops (representing the number of transients, as there is no permanent population register in Lujiazui Police Station, yet this area has many commuters everyday who are also potential targets of theft), number of road intersections (representing the number of possible escape routes), area of building bases, density of road intersections (representing the degree of convenience for escaping), and density of building bases . The stepwise estimation tool of negative binomial regression in Stata/SE 12.0 was applied to process these data.
4.1. Temporal Distribution of Vehicle Thefts
4.1.1. Monthly Distribution
4.1.2. Hourly Distribution
4.2. Spatial Distributions of Vehicle Thefts
4.2.1. Concentration Tendency Analysis
4.2.2. Hotspots and Cold Spots
4.2.3. Crime Distribution Based on Land Uses
4.3. Relationship between the Spatial Distribution of Vehicle Thefts and Environmental Factors
Conflicts of Interest
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|Size of permanent population||0.07904||4.47|
|Density of floating population||0.00017||3.76|
|Density of road intersections||0.03315||1.79|
|Size of the built up area||0.10622||5.15|
|α (dispersion coefficient)||0.24559||4.53|
|Number of observations||41|
|Restricted log-likelihood (constant only)||−259.85|
|Log-likelihood at convergence||−238.64|
|Size of permanent population||0.17098||5.08|
|Density of floating population||0.00015||3.15|
|Number of commercial places||−0.00218||−1.87|
|Size of the built up area||0.11878||5.04|
|α (dispersion coefficient)||0.26965||4.19|
|Number of observations||41|
|Restricted log-likelihood (constant only)||−218.64|
|Log-likelihood at convergence||−196.91|
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