Crime Geographical Displacement: Testing Its Potential Contribution to Crime Prediction

Wang, Zengli; Liu, Lin; Zhou, Hanlin; Lan, Minxuan

doi:10.3390/ijgi8090383

Cited by 12 publications

(14 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the second article included under this research topic, [11] analyze the existence, distribution, and predictive ability of crime displacement in large cities in China, focusing on burglaries. The authors show that, similar to Western cities, the crime displacement phenomenon occurs in response to crime interventions, by using the repeat and near-repeat (RNR) calculator.…”

Section: Crime Risk Diffusion Spillover and Displacementmentioning

confidence: 99%

Urban Crime Mapping and Analysis Using GIS

Ristea

Leitner

2020

IJGI

View full text Add to dashboard Cite

show abstract

Section: Crime Risk Diffusion Spillover and Displacementmentioning

confidence: 99%

Urban Crime Mapping and Analysis Using GIS

Ristea

Leitner

2020

IJGI

View full text Add to dashboard Cite

show abstract

“…One important goal for crime prediction research is to identify crime hotspots. Crimes are not evenly distributed but concentrated within limited areas, which are usually called hotspots [7,10,[19][20][21][22][23][24][25]. KDE is a popular method for crime mapping and hotspot detection [26].…”

Section: Literature Reviewmentioning

confidence: 99%

How Is the Confidentiality of Crime Locations Affected by Parameters in Kernel Density Estimation?

Wang

Liu

Zhou

et al. 2019

IJGI

Self Cite

View full text Add to dashboard Cite

Kernel density estimation (KDE) is widely adopted to show the overall crime distribution and at the same time obscure exact crime locations due to the confidentiality of crime data in many countries. However, the confidential level of crime locational information in the KDE map has not been systematically investigated. This study aims to examine whether a kernel density map could be reverse-transformed to its original map with discrete crime locations. Using the Epanecknikov kernel function, a default setting in ArcGIS for density mapping, the transformation from a density map to a point map was conducted with various combinations of parameters to examine its impact on the deconvolution process (density to point location). Results indicate that if the bandwidth parameter (search radius) in the original convolution process (point to density) was known, the original point map could be fully recovered by a deconvolution process. Conversely, when the parameter was unknown, the deconvolution process would be unable to restore the original point map. Experiments on four different point maps—a random point distribution, a simulated monocentric point distribution, a simulated polycentric point distribution, and a real crime location map—show consistent results. Therefore, it can be concluded that the point location of crime events cannot be restored from crime density maps as long as parameters such as the search radius parameter in the density mapping process remain confidential.

show abstract

“…The geographical detector can detect the influence of a single variable and the interaction of variables, and eliminate the influence of the multicollinearity of variables [40]. The geographical detector model has been widely used in research in various fields, such as health risk assessments [40][41][42][43][44][45][46], crime prediction [47,48], land health assessments [49,50], carbon emission influencing factors [51,52], etc. To the best of our knowledge, the geographical detector model has not been used in the impact of the built environment on the ridership of ride-hailing.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Relative Importance and Interactive Impacts of Explanatory Variables of the Built Environment on Ride-Hailing Ridership by Using the Optimal Parameter-Based Geographical Detector (OPGD) Model

et al. 2023

View full text Add to dashboard Cite

The impact of the built environment on the ridership of ride-hailing results depends on the spatial grid scale. The existing research on the demand model of ride-hailing ignores the modifiable areal unit problem (MAUP). Taking Chengdu as an example, and taking the density of pick-ups and drop-offs as dependent variables, 12 explanatory variables were selected as independent variables according to the “5D” built environment theory. The nugget–sill ratio (NSR) method and optimal parameter-based geographical detector (OPGD) model were used to determine the optimal grid scale for the aggregation of the built environment variables and the ridership of ride-hailing. Based on the optimal grid scale, the optimal data discretization method of the explanatory variables was determined by comparing the results of the geographic detector under different discretization methods (such as the natural break method, k-means clustering method, equidistant method, and quantile method); we utilized the geographic detector model to explore the relative importance and the interactive impacts of the explanatory variables on the ridership of ride-hailing under the optimal grid scale and optimal data discretization method. The results indicated that: (1) the suggested grid scale for the aggregation of the built environment and ride-hailing ridership in Chengdu is 1100 m; (2) the optimal data discretization method is the quantile method; (3) the floor area ratio (FAR), distance from the nearest subway station, and residential POI (point of interest) density resulted in a relatively high importance of the explanatory variable that affects the ridership of ride-hailing; and (4) the interactions of the diversity index of mixed land use ∩ FAR, distance to the nearest subway station ∩ FAR, transportation POI density ∩ FAR, and distance to the central business district (CBD) ∩ FAR made a higher contribution to ride-hailing ridership than the single-factor effect of FAR, which had the highest contribution compared with the other explanatory variables. The proposed grid scale can provide the basis for the partitioning management and scheduling optimization of ride-hailing. In the process of adjusting the ride-hailing demand, the ranking results of the importance and interaction of the built-environment explanatory variables offer valuable references for formulating the priority renewal order and proposing a scientific combination scheme of the built-environment factors.

show abstract

Crime Geographical Displacement: Testing Its Potential Contribution to Crime Prediction

Cited by 12 publications

References 30 publications

Urban Crime Mapping and Analysis Using GIS

Urban Crime Mapping and Analysis Using GIS

How Is the Confidentiality of Crime Locations Affected by Parameters in Kernel Density Estimation?

Exploring the Relative Importance and Interactive Impacts of Explanatory Variables of the Built Environment on Ride-Hailing Ridership by Using the Optimal Parameter-Based Geographical Detector (OPGD) Model

Contact Info

Product

Resources

About