In crime science, understanding the dynamics and interactions between
crime events is crucial for comprehending the underlying factors that drive
their occurrences. Nonetheless, gaining access to detailed spatiotemporal crime
records from law enforcement faces
significant challenges due to confidentiality concerns. In response to these
challenges, this paper introduces an innovative analytical tool named “stppSim,”
designed to synthesize fine-grained spatiotemporal point records while
safeguarding the privacy of individual locations. By utilizing the open-source
R platform, this tool ensures easy accessibility for researchers, facilitating
download, re-use, and potential advancements in various research domains beyond
crime science.
References
[1]
Crawford, A. and Evans, K. (2017) Crime Prevention and Community Safety. In: Liebling, A., Maruna, S. and McAra, L., Eds., The Oxford Handbook of Criminology, Oxford University Press, Oxford, 797-824. https://doi.org/10.1093/he/9780198719441.003.0036
[2]
Andresen, M.A. and Malleson, N. (2015) Intra-Week Spatial-Temporal Patterns of Crime. Crime Science, 4, Article No. 12. https://doi.org/10.1186/s40163-015-0024-7
[3]
Johnson, S.D. (2010) A Brief History of the Analysis of Crime Concentration. European Journal of Applied Mathematics, 21, 349-370. https://doi.org/10.1017/S0956792510000082
[4]
Bakardjiev, D.K. (2015) Officer Body-Worn Cameras-Capturing Objective Evidence with Quality Technology and Focused Policies. Jurimetrics, 56, 79-112.
[5]
O’Connor, C.D., Ng, J., Hill, D. and Frederick T. (2022) Thinking about Police Data: Analysts’ Perceptions of Data Quality in Canadian Policing. The Police Journal, 95, 637-656. https://doi.org/10.1177/0032258X211021461
[6]
Telep, C.W., Mitchell, R.J. and Weisburd, D. (2014) How Much Time Should the Police Spend at Crime Hot Spots? Answers from a Police Agency Directed Randomized Field Trial in Sacramento, California. Justice Quarterly, 31, 905-933. https://doi.org/10.1080/07418825.2012.710645
[7]
Leigh, J., Dunnett, S. and Jackson, L. (2019) Predictive Police Patrolling to Target Hotspots and Cover Response Demand. Annals of Operations Research, 283, 395-410. https://doi.org/10.1007/s10479-017-2528-x
[8]
Lin, R. (2015) Police Body Worn Cameras and Privacy: Retaining Benefits While Reducing Public Concerns. Duke Law & Technology Review, 14, 346.
[9]
Poullet, Y. (2004) The Fight against Crime and/or the Protection of Privacy: A Thorny Debate! International Review of Law, Computers & Technology, 18, 251-273. https://doi.org/10.1080/1360086042000223535
[10]
Sherman, J.E. and Fetters, T.L. (2007) Confidentiality Concerns with Mapping Survey Data in Reproductive Health Research. Studies in Family Planning, 38, 309-321. https://doi.org/10.1111/j.1728-4465.2007.00143.x
[11]
Wiggins, L. (2002) Using Geographic Information Systems Technology in the Collection, Analysis, and Presentation of Cancer Registry Data: A Handbook of Basic Practices. North American Association of Central Cancer Registries, Springfield IL, 33-34.
[12]
Zhang, Z., Zhang, H., Zhao, L., Chen, T., Arik, S.Ö. and Pfister, T. (2022) Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual Understanding. Proceedings of the AAAI Conference on Artificial Intelligence. 36, 3417-3425. https://doi.org/10.1609/aaai.v36i3.20252
[13]
Jeffery, C., Ozonoff, A. and Pagano, M. (2014) The Effect of Spatial Aggregation on Performance When Mapping a Risk of Disease. International Journal of Health Geographics, 13, Article No. 9. https://doi.org/10.1186/1476-072X-13-9
[14]
Hornberger, Z.T., Cox, B.A. and Hill, R.R. (2019) Effects of Aggregation Methodology on Uncertain Spatiotemporal Data. arXiv: 1910.05125.
[15]
Geiger, E.F., Heron, S.F., Hernández, W.J., et al. (2021) Optimal Spatiotemporal Scales to Aggregate Satellite Ocean Color Data for Nearshore Reefs and Tropical Coastal Waters: Two Case Studies. Frontiers in Marine Science, 8, Article 643302. https://doi.org/10.3389/fmars.2021.643302
[16]
Karlson, R.H., Cornell, H.V. and Hughes, T.P. (2007) Aggregation Influences Coral Species Richness at Multiple Spatial Scales. Ecology, 88, 170-177. https://doi.org/10.1890/0012-9658(2007)88[170:AICSRA]2.0.CO;2
[17]
McGlinn, D.J., Engel, T., Blowes, S.A., et al. (2021) A Multiscale Framework for Disentangling the Roles of Evenness, Density, and Aggregation on Diversity Gradients. Ecology, 102, e03233. https://doi.org/10.1002/ecy.3233
[18]
Liaw, S.T., Taggart, J., Dennis, S. and Yeo, A. (2011) Data Quality and Fitness for Purpose of Routinely Collected Data—A General Practice Case Study from an Electronic Practice-Based Research Network (ePBRN). AMIA Annual Symposium Proceedings, 2011, 785-794.
[19]
Errington, A., Einbeck, J., Cumming, J., Rössler, U. and Endesfelder, D. (2021) The Effect of Data Aggregation on Dispersion Estimates in Count Data Models. The International Journal of Biostatistics, 18, 183-202. https://doi.org/10.1515/ijb-2020-0079
[20]
Buil-Gil, D., Medina, J. and Shlomo, N. (2021) Measuring the Dark Figure of Crime in Geographic Areas: Small Area Estimation from the Crime Survey for England and Wales. The British Journal of Criminology, 1, 364-388. https://doi.org/10.1093/bjc/azaa067
[21]
Fox, J.C. and Lundman, R.J. (1974) Problems and Strategies in Gaining Research Access in Police Organizations. Criminology, 12, 52-69. https://doi.org/10.1111/j.1745-9125.1974.tb00620.x
[22]
Gottschalk, P. (2006) Knowledge Management Systems in Law Enforcement: Technologies and Techniques. IGI Global, Hershey. https://doi.org/10.4018/978-1-59904-307-4
[23]
Ashby, D.I., Irving, B.L. and Longley, P.A. (2007) Police Reform and the New Public Management Paradigm: Matching Technology to the Rhetoric. Environment and Planning C: Government and Policy, 25, 159-175. https://doi.org/10.1068/c0556
[24]
Brunton-Smith, I., Buil-Gil, D., Pina-Sánchez, J., Cernat, A. and Moretti, A. (2023) Using Synthetic Crime Data to Understand Patterns of Police Under-Counting at the Local Level. https://www.crimrxiv.com/pub/2j7s2j6z
[25]
Devia, N. and Weber, R. (2013) Generating Crime Data Using Agent-Based Simulation. Computers, Environment and Urban Systems, 42, 26-41. https://doi.org/10.1016/j.compenvurbsys.2013.09.001
[26]
Rosés, R., Kadar, C. and Malleson, N. (2021) A Data-Driven Agent-Based Simulation to Predict Crime Patterns in an Urban Environment. Computers, Environment and Urban Systems, 89, Article ID: 101660. https://doi.org/10.1016/j.compenvurbsys.2021.101660
[27]
Malleson, N., Heppenstall, A., See, L. and Evans, A. (2013) Using an Agent-Based Crime Simulation to Predict the Effects of Urban Regeneration on Individual Household Burglary Risk. Environment and Planning B: Planning and Design, 40, 405-426. https://doi.org/10.1068/b38057
[28]
Farrell, G. and Pease, K. (2001) Repeat Victimization. Criminal Justice Press, Monsey, New York.
[29]
Bowers, K.J. and Johnson, S.D. (2004) Who Commits Near Repeats? A Test of the Boost Explanation. Western Criminology Review, 5, 12-24.
[30]
Farrell, G. (1995) Preventing Repeat Victimization. Crime and Justice, 19, 469-534. https://doi.org/10.1086/449236
[31]
Grove, L.E., Farrell, G., Farrington, D.P. and Johnson, S.D. (2012) Preventing Repeat Victimization: A Systematic Review. Brottsförebyggande rådet/The Swedish National Council for Crime Prevention, Stockholm.
[32]
Weisburd, D. and Braga, A.A. (2006) Advocate Hot Spots Policing as a Model for Police Innovation. In: Weisburd, D. and Braga, A., Eds., Police Innovation: Contrasting Perspectives, Cambridge University Press, Cambridge, 225-244. https://doi.org/10.1017/CBO9780511489334.012
[33]
Halford, E. (2023) Linking Foraging Domestic Burglary: An Analysis of Crimes Committed within Police-Identified Optimal Forager Patches. Journal of Police and Criminal Psychology, 38, 127-140. https://doi.org/10.1007/s11896-022-09497-8
[34]
Fielding, M. and Jones, V. (2012) ‘Disrupting the Optimal Forager’: Predictive Risk Mapping and Domestic Burglary Reduction in Trafford, Greater Manchester. International Journal of Police Science & Management, 14, 30-41. https://doi.org/10.1350/ijps.2012.14.1.260
[35]
Clarke, R.V. and Cornish, D.B. (1985) Modeling Offenders’ Decisions: A Framework for Research and Policy. Crime and Justice, 6, 147-185. https://doi.org/10.1086/449106
[36]
Cohen, L.E. and Felson, M. (1979) Social Change and Crime Rate Trends: A Routine Activity Approach. American Sociological Review, 44, 588-608. https://doi.org/10.2307/2094589
[37]
Brantingham, P.L. and Brantingham, P.J. (1993) Environment, Routine and Situation: Toward a Pattern Theory of Crime. Advances in Criminological Theory, 5, 259-294. https://doi.org/10.4324/9781315128788-12
[38]
Groff, E.R. (2007) Simulation for Theory Testing and Experimentation: An Example Using Routine Activity Theory and Street Robbery. Journal of Quantitative Criminology, 23, 75-103. https://doi.org/10.1007/s10940-006-9021-z
[39]
Groff, E. (2008) Simulating Crime to Inform Theory and Practice. In: Chainey, S. and Tompson, L., Eds., Crime Mapping Case Studies: Practice and Research, Wiley, New York, 133. https://doi.org/10.1002/9780470987193.ch16
[40]
Malleson, N., Heppenstall, A. and Crooks, A. (2018) Place-Based Simulation Modeling: Agent-Based Modeling and Virtual Environments. In: Oxford Research Encyclopedia of Criminology and Criminal Justice, Oxford University Press, Oxford University, UK. https://doi.org/10.1093/acrefore/9780190264079.013.319
[41]
Gerritsen, C. and Elffers, H. (2020) Agent-Based Modelling for Criminological Theory Testing and Development. Routledge, London. https://doi.org/10.4324/9780429277177
[42]
Groff, E.R., Johnson, S.D. and Thornton, A. (2019) State of the Art in Agent-Based Modeling of Urban Crime: An Overview. Journal of Quantitative Criminology, 35, 155-193. https://doi.org/10.1007/s10940-018-9376-y
[43]
Rephann, T.J. and Öhman, M. (1999) Building a Microsimulation Model for Crime in Sweden: Issues and Applications.
[44]
Adepeju, M.O. and Evans, A. (2018) A Dynamic Microsimulation Framework for Generating Synthetic Spatiotemporal Crime Patterns. GISRUK Proceedings, Leeds.
[45]
Malleson, N. and Birkin, M. (2012) Analysis of Crime Patterns through the Integration of an Agent-Based Model and a Population Microsimulation. Computers, Environment and Urban Systems, 36, 551-561. https://doi.org/10.1016/j.compenvurbsys.2012.04.003
[46]
Diggle, P.J., Chetwynd, A.G., Häggkvist, R. and Morris, S.E. (1995) Second-Order Analysis of Space-Time Clustering. Statistical Methods in Medical Research, 4, 124-136. https://doi.org/10.1177/096228029500400203
[47]
Weisburd, D. (2015) The Law of Crime Concentration and the Criminology of Place. Criminology, 53, 133-157. https://doi.org/10.1111/1745-9125.12070
[48]
Bowers, K.J. and Johnson, S.D. (2005) Domestic Burglary Repeats and Space-Time Clusters: The Dimensions of Risk. European Journal of Criminology, 2, 67-92. https://doi.org/10.1177/1477370805048631
[49]
Townsley, M., Homel, R. and Chaseling, J. (2003) Infectious Burglaries. A Test of the Near Repeat Hypothesis. British Journal of Criminology, 43, 615-633. https://doi.org/10.1093/bjc/43.3.615
[50]
Johnson, S.D., Davies, T., Murray, A., Ditta, P., Belur, J. and Bowers, K. (2017) Evaluation of Operation Swordfish: A Near-Repeat Target-Hardening Strategy. Journal of Experimental Criminology, 13, 505-525. https://doi.org/10.1007/s11292-017-9301-7
[51]
Bediroglu, G., Bediroglu, S., Colak, H.E. and Yomralioglu, T. (2018) A Crime Prevention System in Spatiotemporal Principles with Repeat, Near-Repeat Analysis and Crime Density Mapping: Case Study Turkey, Trabzon. Crime & Delinquency, 64, 1820-1835. https://doi.org/10.1177/0011128717750391
[52]
Felson, M. (1987) Routine Activities and Crime Prevention in the Developing Metropolis. Criminology, 25, 911-932. https://doi.org/10.1111/j.1745-9125.1987.tb00825.x
[53]
Leclerc, B. and Reynald, D. (2017) When Scripts and Guardianship Unite: A Script Model to Facilitate Intervention of Capable Guardians in Public Settings. Security Journal, 30, 793-806. https://doi.org/10.1057/sj.2015.8
[54]
Langton, S.H. (2019) Offender Residential Concentrations: A Longitudinal Study in Birmingham, England.
[55]
Savage, J. and Windsor, C. (2018) Sex Offender Residence Restrictions and Sex Crimes against Children: A Comprehensive Review. Aggression and Violent Behavior, 43, 13-25. https://doi.org/10.1016/j.avb.2018.08.002
[56]
R Core Team (2022) A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/
[57]
Quaglietta, L. and Porto, M. (2019) SiMRiv: An R Package for Mechanistic Simulation of Individual, Spatially-Explicit Multistate Movements in Rivers, Heterogeneous and Homogeneous Spaces Incorporating Landscape Bias. Movement Ecology, 7, Article No. 11. https://doi.org/10.1186/s40462-019-0154-8
[58]
Hijmans, R.J., Van Etten, J., Cheng, J., Mattiuzzi, M., et al. (2015) Package ‘Raster’. R Package, 734, 473.
[59]
Steenbeek, W. (2018) Near Repeat. R Package Version 0.1.1. 2018. https://github.com/wsteenbeek/NearRepeat
[60]
Ashby, M.P. (2019) Studying Crime and Place with the Crime Open Database: Social and Behavioural Sciences. Research Data Journal for the Humanities and Social Sciences, 4, 65-80. https://doi.org/10.1163/24523666-00401007
[61]
Ratcliffe, J.H. and McCullagh, M.J. (1998) Aoristic Crime Analysis. International Journal of Geographical Information Science, 12, 751-764. https://doi.org/10.1080/136588198241644
[62]
Ratcliffe, J.H. (2002) Aoristic Signatures and the Spatio-Temporal Analysis of High Volume Crime Patterns. Journal of Quantitative Criminology, 18, 23-43. https://doi.org/10.1023/A:1013240828824
