1 |
W. Bernasco and C. Vandeviver, The geography of crime and crime control, Appl. Geogr. 86 (2017), 220-225.
DOI
|
2 |
X. Hu et al., Impact of climate variability and change on crime rates in Tangshan, China, Sci. Total Environ. 609 (2017), 1041-1048.
DOI
|
3 |
D. J. Lemon and R. Partridge, Is weather related to the number of assaults seen at emergency departments?, Injury 48 (2017), 2438-2442.
DOI
|
4 |
X. Zhao and J. Tang, Crime in urban areas: A data mining perspective, available at CoRR http://arxiv.org/abs/1804.08159, preprint, 2018.
|
5 |
M. R. D'Orsogna and M. Perc, Statistical physics of crime: A review, Phys. Life Rev. 12 (2015), 1-21.
DOI
|
6 |
M. A. Andresen, Crime measures and the spatial analysis of criminal activity, Br. J. Criminol. 46 (2005), 258-285.
DOI
|
7 |
D. Vildosola et al., Crime in an affluent city: Applications of risk terrain modeling for residential and vehicle burglary in Coral Gables, Florida, 2004-2016, Appl. Spat. Anal. Policy 13 (2019), 441-459.
DOI
|
8 |
M. A. Andresen, Estimating the probability of local crime clusters: The impact of immediate spatial neighbors, J. Crim. Justice 39 (2011), 394-404.
DOI
|
9 |
L. Anselin, Local Indicators of Spatial Association-LISA, Geogr. Anal. 27 (1995), 93-115.
DOI
|
10 |
C. Cowen, E. Louderback, and S. Roy, The role of land use and walkability in predicting crime patterns: A spatiotemporal analysis of Miami-Dade County neighborhoods, 2007-2015, Secur. J. 32 (2019), 264-286.
DOI
|
11 |
C. Huang et al., Deep-Crime: Attentive hierarchical recurrent networks for crime prediction, in Proc. ACM Int. Conf. Inf. Knowledge Manag. (Torino, Italiy), Oct. 2018, pp. 1423-1432.
|
12 |
M. S. Gerber, Predicting crime using Twitter and kernel density estimation, Decis. Support Syst. 61 (2014), 115-125.
DOI
|
13 |
L. Vomfell, W. K. Hardle, and S. Lessmann, Improving crime count forecasts using Twitter and taxi data, Decis. Support Syst. 113 (2018), 73-85.
DOI
|
14 |
M. L. Williams, P. Burnap, and L. Sloan, Crime sensing with Big Data: The affordances and limitations of using open-source communications to estimate crime patterns, Br. J. Criminol. 57 (2016), 320-340.
DOI
|
15 |
G. Mohler, Marked point process hotspot maps for homicide and gun crime prediction in Chicago, Int. J. Forecast. 30 (2014), 491-497.
DOI
|
16 |
L. G. A. Alves, H. V. Ribeiro, and F. A. Rodrigues, Crime prediction through urban metrics and statistical learning, Phys. A 505 (2018), 435-443.
DOI
|
17 |
J. H. Ratcliffe, Geocoding crime and a first estimate of a minimum acceptable hit rate, Int. J. Geogr. Inf. Sci. 18 (2004), 61-72.
DOI
|
18 |
J. K. Ord and A. Getis, Local spatial autocorrelation statistics: Distributional issues and an application, Geogr. Anal. 27 (1995), 286-306.
DOI
|
19 |
G. N. Kouziokas, The application of artificial intelligence in public administration for forecasting high crime risk transportation areas in urban environment, Transp. Res. Procedia 24 (2017), 467-473.
DOI
|
20 |
A. Getis and J. K. Ord, The analysis of spatial association by use of distance statistics, Geogr. Anal. 24 (1992), 189-206.
DOI
|
21 |
K. Leong and A. Sung, A review of spatio-temporal pattern analysis approaches on crime analysis, Int. e-J. Crim. Sci. 9 (2015), 1-33.
|
22 |
A. Rummens, W. Hardyns, and L. Pauwels, The use of predictive analysis in spatiotemporal crime forecasting: Building and testing a model in an urban context, Appl. Geogr. 86 (2017), 255-261.
DOI
|
23 |
T. Lawson, R. Rogerson, and M. Barnacle, A comparison between the cost effectiveness of CCTV and improved street lighting as a means of crime reduction, Comput. Environ. Urban Syst. 68 (2018), 17-25.
DOI
|
24 |
R. Iqbal et al., An experimental study of classification algorithms for crime prediction, Indian, J. Sci. Technol. 6 (2013), 4219-4225.
|
25 |
Y. Xu et al., The impact of street lights on spatial-temporal patterns of crime in Detroit, Michigan, Cities 79 (2018), 45-52.
DOI
|