In this study, we investigated crime events with repeat and near-repeat analysis for Turkey’s Trabzon city’s crime data after standardization process on raw crime data. First, a new crime geodatabase model was created. All types of recorded crime data for events between the years 2010 and 2014 were standardized, generalized, and Geo-referenced. We gave certain locations to crime events with geocoding techniques. Then, we created density maps of crime events with Kernel method in Geographic Information Systems (GIS). Repeat and near-repeat methods were tested on Burglary crime type in this geodatabase. Studies focused to applying prediction analysis besides showing current situation. These predictive analyses may be applied for all the security, intelligence, or defense departments at local, national, or international levels.
AndersonT. K. (2009). Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis & Prevention, 41, 359-364.
2.
BlumsteinA.CohenJ.RothJ.VisherC. (1986). Criminal careers and “career criminals” (Vol. 2). Washington, DC: National Academies Press.
3.
BobaR. (2001). Introductory guide to crime analysis and mapping. Washington, DC: Office of Community Oriented Policing Services, U.S. Department of Justice.
4.
BowersK. J.JohnsonS. D. (2003). Measuring the geographical displacement and diffusion of benefit effects of crime prevention activity. Journal of Quantitative Criminology, 19, 275-301.
5.
BowersK. J.JohnsonS. D.PeaseK. (2004). Prospective hot-spotting: The future of crime mapping?The British Journal of Criminology, 44, 641-658.
6.
CaplanJ. M.KennedyL. W.PizaE. L. (2013). Joint utility of event-dependent and environmental crime analysis techniques for violent crime forecasting. Crime & Delinquency, 59, 243-270.
7.
ChaineyS. P.da SilvaB. F. A. (2016). Examining the extent of repeat and near repeat victimisation of domestic burglaries in Belo Horizonte, Brazil. Crime Science: An Interdisciplinary Journal, 5, Article 1.
8.
CohenL. E.FelsonM. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 44, 588-608.
9.
FaganA. A.MazerolleP. (2011). Repeat offending and repeat victimization: Assessing similarities and differences in psychosocial risk factors. Crime & Delinquency, 57, 732-755.
10.
FarrellG. (1993). Repeat criminal victimsation. Manchester, UK: University of Manchester.
11.
FarrellG.EllingworthD.PeaseK. (1996). High crime rates, repeat victimization, and routine activities. In BennettT. (Ed.), Preventing crime and disorder (pp. 276-296). Cambridge, UK: Institute of Criminology.
12.
FarrellG.PeaseK. (2017). Preventing repeat and near repeat crime concentrations. In TilleyN.SidebottomA. (Eds.), The handbook of crime prevention and community safety (2nd ed.). Leeds: Routledge.
13.
FengJ.DongY.SongL. (2016). A spatio-temporal analysis of urban crime in Beijing: Based on data for property crime. Urban Studies, 53, 3223-3245.
14.
GlasnerP.LeitnerM. (2017). Evaluating the impact the weekday has on near-repeat victimization: A spatio-temporal analysis of street robberies in the city of Vienna, Austria. ISPRS International Journal of Geo-Information, 6, Article 3.
15.
GrubesicT. H.MackE. A. (2008). Spatio-temporal interaction of urban crime. Journal of Quantitative Criminology, 24, 285-306.
16.
GuerryA. (1833). Essay on the Moral Statistics of France. Paris, France: Crochard.
17.
HabermanC. P.RatcliffeJ. H. (2012). The predictive policing challenges of near repeat armed street robberies. Policing: A Journal of Policy and Practice, 6, 151-166.
18.
JohnsonS. D.BernascoW.BowersK. J.ElffersH.RatcliffeJ.RengertG.TownsleyM. (2007). Space–time patterns of risk: A cross national assessment of residential burglary victimization. Journal of Quantitative Criminology, 23, 201-219.
19.
JohnsonS. D.BowersK. J. (2004a). The burglary as clue to the future. European Journal of Criminology, 1, 237-255.
20.
JohnsonS. D.BowersK. J. (2004b). The stability of space-time clusters of burglary. The British Journal of Criminology, 44, 55-65.
21.
JohnsonS. D.BowersK. J.HirschfieldA. (1997). New insights into the spatial and temporal distribution of repeat victimization. The British Journal of Criminology, 37, 224-241.
22.
JohnsonS. D.BowersK. J.BirksD. J.PeaseK. (2009). Predictive mapping of crime by promap: Accuracy, units of analysis, and the environmental backcloth. In WeisburdD.BernascoW.BruinsmaG. (Eds.), Putting crime in its place: Units of analysis in geographic criminology (pp. 171-198). New York, NY: Springer.
23.
JohnstonR. J.GregoryD.PrattG.WattsM. (2000). The Dictionary of Human Geography (4th ed.). Oxford, UK: Blackwell Publishers.
24.
MorganF. (2000). Repeat burglary in a Perth suburb: Indicator of short-term or long-term risk. Repeat victimization: Crime prevention studies, 12, 83–118.
25.
O’SheaT. C.NichollsK. (2003). Crime analysis in America: Findings and recommendations. Washington, DC: U.S. Department of Justice, Office of Community Oriented Policing Services.
26.
PeaseK. (1998). Repeat victimization: Taking stock (Crime Detection and Prevention Series Paper 90). London, England: The Home Office: Police Research Group.
27.
QueteletA. J. (1835). Man and the Development of his Faculties or Essay of Social Physics. Paris, France: Bachelier.
28.
RatcliffeJ. H. (2002). Aoristic signatures and the spatio-temporal analysis of high volume crime patterns. Journal of Quantitative Criminology, 18, 23-43.
29.
RatcliffeJ. H. (2004). The hotspot matrix: A framework for the spatio-temporal targeting of crime reduction. Police Practice and Research, 5, 5-23.
30.
RatcliffeJ. H. (2005). Detecting spatial movement of intra-region crime patterns over time. Journal of Quantitative Criminology, 21, 103-123.
31.
RatcliffeJ. H. (2009). Near repeat calculator (Version 1.3). Philadelphia, PA: Temple University.
32.
RatcliffeJ. H.RengertG. F. (2008). Near-repeat patterns in Philadelphia shootings. Security Journal, 21, 58-76.
33.
RothR. E.RossK. S.FinchB. G.LuoW.MacEachrenA. M. (2013). Spatiotemporal crime analysis in U.S. law enforcement agencies: Current practices and unmet needs. Government Information Quarterly, 30, 226-240.
34.
ShermanL. W.GartinP. R.BuergerM. E.ShermanL. W.GartinP. R.BuergerM. E. (1989). Hot spots of predatory crime: Routine activities and the criminology of place. Criminology, 27, 27-56.
35.
ShortM. B.D’OrsognaM. R.BrantinghamP. J.TitaG. E. (2009). Measuring and modeling repeat and near-repeat burglary effects. Journal of Quantitative Criminology, 25, 325-339.
36.
SkoganW. G. (1996). The decade’s most important criminological insight (National Institute of Justice: Research in Action). Washington, DC: U.S. Department of Justice.
37.
SturupJ.RostamiA.GerellM.SandholmA. (2017). Near-repeat shootings in contemporary Sweden 2011 to 2015. Security Journal, 1-20.
38.
TownsleyM.HomelR.ChaselingJ.TownsleyM.HomelR.ChaselingJ. (2000). Repeat burglary victimisation: Spatial and temporal patterns. Australian & New Zealand Journal of Criminology, 33, 37-63.
39.
TownsleyM.HomelR.ChaselingJ.TownsleyM.HomelR.ChaselingJ. (2003). Infectious burglaries: A test of the near repeat hypothesis. The British Journal of Criminology, 43, 615-633.
40.
WellsW.LingW.XinyueY. (2012). Patterns of near-repeat gun assaults in Houston. Journal of Research in Crime and Delinquency, 49, 186-212.
41.
WuL.XuX.YeX.ZhuX. (2015). Repeat and near-repeat burglaries and offender involvement in a large Chinese city. Cartography and Geographic Information Science, 42, 178-189.
42.
WyantB. R.TaylorR. B.RatcliffeJ. H.WoodJ. (2012). Deterrence, firearm arrests, and subsequent shootings: A micro-level spatio-temporal analysis. Justice Quarterly, 29, 524-545.
43.
YoustinT. J.NoblesM. R.WardJ. T.CookC. L. (2011). Assessing the generalizability of the near repeat phenomenon. Criminal Justice and Behavior, 38, 1042-1063.
44.
ZhangY.ZhaoJ.RenL.HooverL. (2015). Space–time clustering of crime events and neighborhood characteristics in Houston. Criminal Justice Review, 40, 340-360.
