The Role of predictive policing in predicting spatio-temporal crime mapping
DOI:
https://doi.org/10.21638/spbu14.2024.112Аннотация
Since the modern criminology critically reviews the contribution of socio-environmental factors to the crime mapping in terms of their spatio-temporal distribution, a statistical/machine learning technique namely called the risk terrain modelling (RTM) is currently being used as a software solution to diagnose the socio-environmental conditions that lead to crime in a specific area’s geography (Study Area), through geospatial and temporal analyses of crimes by linking them to hotspots and analysing them into big data (criminal data), and as a result, appears the new predicting patterns of risks in geographical area under survey (Survey Area) in future, all that for a sake of a rapid and effective response by the Predictive Police, firstly, for setting the priorities for the deployment of precautionary resources to prevent crime and reduce potential risks in the event of a crime, and secondly, what is required to be done upon arrival as a precautionary step to control the crime with a minimum disadvantage to the police forces. The Risk Terrain Modelling represents a systematic investment for more than a decade in the field of criminal investigation, it is have been founded by Leslie W. Kennedy and Joel M. Caplan at Rutgers University, and it currently testes in over than 45 countries around the world.
Ключевые слова:
predictive policing, near-repeat phenomenon, crime prediction, risk terrain modelling, artificial intelligence, machine learning, agent-based modelling
Скачивания
Данные скачивания пока недоступны.
Библиографические ссылки
References
Brantingham, Patricia, Paul Brantingham. 1995. “Criminality of place: Crime generators and crime attractors”. European Journal on Criminal Policy and Research 3: 1–26. Accessed February 10, 2024. https://www.researchgate.net/publication/321478569_Criminality_of_Place_Crime_Generators_and_CrimeAttractors.
Briz-Redon, Alvaro, Francisco Martinez-Ruiz, Francisco Montes. 2022. “Adjusting the Knox test by accounting for spatio-temporal crime risk heterogeneity to analyse near-repeats”. European Journal of Criminology 19 (4): 586–611. https://doi.org/10.1177%2F1477370820905106
Busso, Matias, Jesse Gregory, Patrick Kline. 2013. “Assessing the incidence and efficiency of a prominent place based policy”. American Economic Review 103 (2): 897–947. Accessed February 10, 2024. https://www.ssc.wisc.edu/~jmgregory/EZ_AER. pdf.
Caplan, Joel M., Leslie W. Kennedy, Jeremy D. Barnum, Eric L. Piza. 2015. “Risk terrain modeling for spatial risk assessment”. John Jay College of Criminal Justice 1: 7–16. Accessed February 10, 2024. https://academicworks.cuny.edu/cgi/viewcontent.cgi?article=1192&context=jj_pubs.
Caplan, Joel M., Leslie W. Kennedy. 2011. “Risk terrain modeling compendium”. Rutgers Center on Public Security. Accessed February 10, 2024. https://www.rutgerscps.org/uploads/2/7/3/7/27370595/riskterrainmodelingcompendium_caplankennedy2011.pdf.
Danaher, John, Michael J. Hogan, Chris Noone, Rónán Kennedy, Anthony Behan, Aisling De Paor, Heike Felzmann, Muki Haklay, Su-Ming Khoo, John Morison, Maria Helen Murphy, Niall O’Brolchain, Burkhard Schafer, Kalpana Shankar. 2017. “Algorithmic governance: Developing a research agenda through the power of collective intelligence”. Big Data & Society. Accessed February 10, 2024. https://journals.sagepub.com/doi/pdf/10.1177/2053951717726554.
Drawve, Grant. 2014. “A metric comparison of predictive hot spot techniques and RTM”. Justice Quarterly 33 (3): 369–397. https://doi.org/10.1080/07418825.2014.904393
Dugato, Marco. 2013. “Assessing the validity of risk terrain modeling in a European city: Preventing robberies in Milan”. IRIS PubliCatt 5 (1): 63–89. Accessed February 10, 2024. https://publicatt.unicatt.it/handle/10807/44530.
Egbert, Simon, Susanne Krasmann. 2019a. “Predictive policing: Not yet, but soon preemptive?” Policing and Society 30 (8): 905–919. https://doi.org/10.1080/10439463.2019.1611821
Egbert, Simon, Susanne Krasmann. 2019b. Predictive policing. Eine ethnographische Studie neuer Technologien zur Vorhersage von Straftaten und ihre Folgen fur die polizeiliche Praxis. Projektabschlussbericht. Hamburg, Universitat Hamburg.
Garnier, Simon, Joel M. Caplan, Leslie W. Kennedy. 2018. “Predicting dynamical crime distribution from environmental and social influences”. Frontiers 4. https://doi.org/10.3389/fams.2018.00013
Kai, Seidensticker, Bode Felix, Stoffel Florian. 2018. “Predictive policing in Germany”. KOPS the Institutional Repository of the University of Konstanz. Accessed February 10, 2024. http://nbn-resolving.de/urn:nbn:de:bsz:352-2-14sbvox1ik0z06.
Kennedy, Leslie W., Joel M. Caplan, Eric L. Piza, Amanda L. Thomas. 2020. “Environmental factors influencing urban homicide clearance rates: A spatial analysis of New York City”. CrimRxiv. https://doi.org/10.21428/cb6ab371.c14d8170
Kennedy, Leslie W., Joel M. Caplan, Eric L. Piza. 2013. “Risk Terrain Modeling Diagnostics Utility User Manual (Version 1.0)”. Newark NJ: Rutgers Center on Public Security. Accessed February 10, 2024. http://www.rutgerscps.org/uploads/2/7/3/7/27370595/rtmdxusermanual_final_caplankennedypiza.pdf.
Kennedy, Leslie W., Joel M. Caplan. 2012. “A theory of risky places”. Rutgers Center on Public Security BRIEF. Accessed February 10, 2024. https://www.rutgerscps.org/uploads/2/7/3/7/27370595/risktheorybrief_web.pdf.
Lazzati, Natalia, Amilcar Menichini. 2014. “Hot spot policing: A study of place-based strategies for crime prevention”. Calhoun: The NPS Institutional Archive DSpace Repository JEL codes: D7 K4 R1. Accessed February 10, 2024. https://calhoun.nps.edu/bitstream/handle/10945/40255/Menichini_Hot_Spot_Policing_A_Study.pdf.
Mante, Claude, Oumar Saikou Kide, Anne-Francoise Yao, Bastien Merigot. 2016. “Fitting the truncated negative binomial distribution to count data. A comparison of estimators with an application to groundfishes from the Mauritanian Exclusive Economic Zone”. Environmental and Ecological Statistics Springer Verlag (Germany) 23 (3): 359–385. Accessed February 10, 2024. https://hal.archives-ouvertes.fr/hal-01292224.
McCue, Colleen. 2007. “Data mining and predictive analysis: Intelligence gathering and crime analysis”. Burlington MA: Butterworth-Heinemann (Elsevier). Accessed February 10, 2024. https://vdoc.pub/documents/data-mining-and-predictive-analysis-intelligence-gathering-and-crime-analysis-45te4tqmglb0.
Pearsall, Beth. 2010. “Predictive policing: The future of law enforcement?” National Institute of Justice Journal 266. Accessed February 10, 2024. https://nij.ojp.gov/topics/articles/predictive-policing-futurelaw-enforcement.
Perry, Walter L., Brian McInnis, Carter C. Price, Susan Smith, John S. Hollywood. 2013. “Predictive policing: the role of crime forecasting in law enforcement operations”. RAND Corporation. Accessed February 10, 2024. https://www.rand.org/pubs/research_reports/RR233.html.
Ratcliffe, Jerry H. 2010. “Crime mapping: Spatial and temporal challenges”. Handbook of Quantitative Criminology 2: 8–17. Accessed February 10, 2024. http://www.cedus.it/files/9780387776491-c1.pdf.
Rubin, Joel. 2010. “Stopping crime before it starts”. Los Angeles Times. Accessed February 10, 2024. https://www.latimes.com/archives/la-xpm-2010-aug-21-la-me-predictcrime-20100427-1-story.html.
Smith, Adam, Benjamin Hofner, Juliet S. Lamb, Jason Osenkowski, Taber Allison, Giancarlo Sadoti, Scott R. McWilliams, Peter Paton. 2018. “Modeling spatiotemporal abundance of mobile wildlife in highly variable environments using boosted GAMLSS hurdle models”. WILEY Ecology and Evolution 9: 2346–2364. Accessed February 10, 2024. https://onlinelibrary.wiley.com/doi/epdf/10.1002/ece3.4738.
Taylor, Ralph B. 2006. “Impact of neighborhood structure crime and physical deterioration on residents and business personnel in Minneapolis, St. Paul 1970–1982 (ICPSR 2371)”. Inter-university Consortium for Political and Social Research, Philadelphia. https://doi.org/10.3886/ICPSR02371.v1
Taylor, Ralph B., Adele V. Harrell. 1996. “Physical environment and crime”. Washington: U. S. Department of Justice Office of Justice Programs National Institute of Justice NCJ 157311. Accessed February 10, 2024. https://www.ojp.gov/pdffiles/physenv.pdf.
Završnik, Aleš. 2019. “Algorithmic justice: Algorithms and big data in criminal justice settings”. European Journal of Criminology 18 (5): 623–642. Accessed February 15, 2024. https://doi.org/10.1177/1477370819876762
Zubi, Zakaria Suliman, Ayman Altaher Mahmmud. 2014. “Using data mining techniques to analyze crime patterns in the Libyan national crime data”. Recent advances in image audio and signal processing 8: 79–85. Accessed February 10, 2024. https://www.researchgate.net/profile/Zakaria-Zubi/publication/259477161_Using_Data_Mining_Techniques_to_Analyze_Crime_Patterns_in_the_Libyan_National_Crime_Data/links/00b4952c07b2cc32d4000000/Using-Data-Mining-Techniques-to-Analyze-Crime-Patterns-in-the-Libyan-National-Crime-Data.pdf.
Загрузки
Опубликован
28.03.2024
Как цитировать
Abdulkareem, F. A. A. (2024). The Role of predictive policing in predicting spatio-temporal crime mapping. Вестник Санкт-Петербургского университета. Право, 15(1), 192–205. https://doi.org/10.21638/spbu14.2024.112
Выпуск
Раздел
Зарубежное и международное право
Лицензия
Статьи журнала «Вестник Санкт-Петербургского университета. Право» находятся в открытом доступе и распространяются в соответствии с условиями Лицензионного Договора с Санкт-Петербургским государственным университетом, который бесплатно предоставляет авторам неограниченное распространение и самостоятельное архивирование.
