

Investigating Crime Pattern Stability at Micro-Temporal Intervals: Implications...
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Investigating Crime Pattern Stability at Micro-Temporal Intervals: Implications for Crime Analysis and Hotspot Policing Strategies
First Published February 25, 2021
Research Article
Abstract
Studies of crime hotspot forecasts use various metrics to describe different characteristics of prediction patterns. However, few investigations consider how the stability of crime hotspot, estimated at relatively short temporal intervals, can impact hotspot policing efforts. In response, using address-level incident location data that were collected from six law enforcement agencies in the United States, the current study examines the daily stability of crime hotspots that were estimated over a 1-year period. Results suggest that micro-temporal stability patterns in crime hotspot forecasts are dependent on crime type, jurisdiction, and the interaction between these two factors. Implications for crime analysis and future research are discussed.
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References
Google Scholar
Adepeju, M., Rosser, G., Cheng, T. (2016). Novel evaluation metrics for sparse spatio-temporal point process hotspot predictions: A crime case study. International Journal of Geographical Information Science, 30(11), 2133–2154. https://doi.org/10.1080/13658816.2016.1159684
Google Scholar
Andresen, M. A. (2009). Testing for similarity in area-based spatial patterns: A nonparametric Monte Carlo approach. Applied Geography, 29, 333–345.
Google Scholar | Crossref
Andresen, M. A. (2011). The ambient population and crime analysis. The Professional Geographer, 63(2), 193–212. https://doi.org/10.1080/00330124.2010.547151
Google Scholar
Andresen, M. A., Linning, S. J., Malleson, N. (2017). Crime at places and spatial concentrations: Exploring the spatial stability of property crime in Vancouver BC, 2003-2013. Journal of Quantitative Criminology, 33(2), 255–275.
Google Scholar | Crossref
Anselin, L. (1995). Local indicators of spatial association–LISA. Geographical Analysis, 27(2), 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x
Google Scholar
Bailey, T., Gatrell, A. (1995). Interactive spatial data analysis. Longman Scientific & Technical.
Google Scholar
Ball, G. H., Hall, D. J. (1970). A clustering technique for summarizing multivariate data. Behavioral Science, 12, 153–155.
Google Scholar | Crossref
Bender, E. (1962). Area-perimeter relations for two dimensional lattices. The American Mathematical Monthly, 69, 742–774.
Google Scholar | Crossref
Boba, R. S. (2016). Crime analysis with crime mapping (4th ed.). Sage.
Google Scholar
Bowers, K. J., Johnson, S. D., Pease, K. (2004). Prospective hot-spotting: The future of crime mapping? British Journal of Criminology, 44(5), 641–658. https://doi.org/10.1093/bjc/azh036
Google Scholar
Braga, A. A., Andresen, M. A., Lawton, B. (2017). The law of crime concentration at places: Editors’ introduction. Journal of Quantitative Criminology, 33, 421–426. https://doi.org/10.1007/s10940-017-9342-0
Google Scholar
Brantingham, P. J., Brantingham, P. L. (1984). Patterns in crime. Prentice Hall.
Google Scholar
Brantingham, P. J., Brantingham, P. L. (1991). Environmental criminology. Waveland Press.
Google Scholar
Brantingham, P. J., Brantingham, P. L., Andresen, M. A. (2016). The geometry of crime and crime pattern theory. In Wortley, R., Townsley, M. (Eds.), Environmental criminology and crime analysis (2nd ed., pp. 119–136). Routledge.
Google Scholar
Caplan, J. M., Kennedy, L. W. (2010). Risk terrain modeling manual: Theoretical framework and technical steps of spatial risk assessment. Rutgers Center on Public Security.
Google Scholar
Caplan, J. M., Kennedy, L. W., Miller, J. (2011). Risk terrain modeling: Brokering criminological theory and GIS methods for crime forecasting. Justice Quarterly, 28(2), 360–381. https://doi.org/10.1080/07418825.2010.486037
Google Scholar
Chainey, S. (2013). Examining the influence of cell size and bandwidth size on kernel density estimation crime hotspot maps for predicting spatial patterns of crime. Bulletin of the Geographical Society of Liege, 60, 7–19.
Google Scholar
Chainey, S., Tompson, L., Uhlig, S. (2008). The utility of hotspot mapping for predicting spatial patterns of crime. Security Journal, 21(1), 4–28. https://doi.org/10.1057/palgrave.sj.8350066
Google Scholar
Clarke, R. V (1995). Situational crime prevention. In Tonry, M., Farrington, D. P. (Eds.), Building a safe society: Strategic approaches to crime prevention, vol. 19 of Crime and Justice: A review of research. University Press.
Google Scholar | Crossref
Cohen, L. E., Felson, M. (1979). Social change and crime rate trends: A routine activity approach. American Sociological Review, 44(4), 588–608. https://doi.org/10.2307/2094589
Google Scholar
Drawve, G. (2016). A metric comparison of predictive hot spot techniques and RTM. Justice Quarterly, 33(3), 369–397.
Google Scholar | Crossref | ISI
Environmental Systems Research Institute. (2016). ArcGIS Desktop: Release 10.5. [Computer software]. https://desktop.arcgis.com/en/
Google Scholar
Felson, M., Eckert, M. (2019). Crime and everyday life: A brief introduction (6th ed.). Sage.
Google Scholar | Crossref
Fielding, M., Jones, V. (2011). Disrupting the optimal forager: Predictive risk mapping and domestic burglary reduction in Trafford. Greater Manchester. International Journal of Police Science & Management, 14(1), 30–41.
Google Scholar | SAGE Journals
Getis, A., Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24(3), 189–206. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x
Google Scholar
Groff, E. R., Taniguchi, T. (2018). Micro-level policing for preventing near repeat residential burglary: Final monograph. Police Foundation.
Google Scholar
Hart, T., Zandbergen, P. (2014). Kernel density estimation and hotspot mapping: Examining the influence of interpolation method, grid cell size, and bandwidth on crime forecasting. Policing: An International Journal of Police Strategies & Management, 37(2), 305–323. https://doi.org/10.1108/PIJPSM-04-2013-0039
Google Scholar
Hartigan, J. A. (1975). Clustering algorithms. John Wiley & Sons.
Google Scholar
Jeffery, C. R. (1971). Crime prevention through environmental design. Sage.
Google Scholar
Johnson, S. D. (2017). Crime mapping and spatial analysis. In Wortley, R., Townsley, M. (Eds.), Environmental criminology and crime analysis (2nd ed., pp. 199–223). Routledge.
Google Scholar
Johnson, S. D., Bowers, K. J. (2004a). The burglary as a clue to the future: The beginnings of prospective hot-spotting. The European Journal of Criminology, 1, 237–255.
Google Scholar | SAGE Journals
Johnson, S. D., Bowers, K. J. (2004b). The stability of space-time clusters of burglary. British Journal of Criminology, 44, 55–65.
Google Scholar | Crossref | ISI
Johnson, S. D., Bowers, K. J., Birks, D., Pease, K. (2009). Predictive mapping of crime by ProMap: Accuracy, units of analysis, the environmental backcloth. In Weisburd, D., Bernasco, W., Bruinsma, G. (Eds.), Putting crime in its place: Units of analysis in spatial crime research (pp. 171–198). Springs.
Google Scholar | Crossref
Johnson, S. D., Lab, S. P., Bowers, K. J. (2008). Stable and fluid hotspots of crime: Differentiation and identification. Built Environment, 34(1), 32–45.
Google Scholar | Crossref
Lee, J., Gong, J., Shengwen, L. (2017). Exploring spatiotemporal clusters based on extended kernel estimation methods. International Journal of Geographical Information Science, 31(6), 1154–1177.
Google Scholar
Lemieux, A. M., Felson, M. (2012). Risk of violent crime victimization during major daily activities. Violence and Victims, 27(5), 635–655. https://doi.org/10.1891/0886-6708.27.5.635
Google Scholar
Levine, N. (2008). The ‘hottest’ part of a hotspot: Comments on “the utility of hotspot mapping for predicting spatial patters of crime.” Security Journal, 21(4), 295–302. https://doi.org/10.1057/sj.2008.5
Google Scholar
Levine, N. (2015). CrimeStat: A spatial statistics program for the analysis of crime incident locations (Version 4.02) [Apparatus and software]. National Institute of Justice.
Google Scholar
McBratney, A. B., deBruijter, J. J. (1992). A continuum approach to soil classification by modified fuzzy k-means with extragrades. Journal of Soil Science, 43, 159–175.
Google Scholar | Crossref
Mohler, G. O., Short, M. B., Brantingham, P. J., Schoenberg, F. P., Tita, G. E. (2011). Self-exciting point process modeling of crime. Journal of the American Statistical Association, 106(493), 100–108. https://doi.org/10.1198/jasa.2011.ap09546
Google Scholar
Newman, O. (1972). Defensible space: Crime prevention through urban design. Macmillan.
Google Scholar
Ord, J. K., Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4), 286–306.
Google Scholar | Crossref | ISI
Pease, K., Farrell, G. (2016). Repeat victimisation. In Wortley, R., Townsley, M. (Eds.), Environmental criminology and crime analysis (2nd ed., pp. 180–198). Routledge.
Google Scholar
Perry, W. L., McInnis, B., Price, C. C., Smith, S. C., Hollywood, J. S. (2013). Predictive policing: The role of crime forecasting in law enforcement operations. Rand Corporation.
Google Scholar | Crossref
Pitney Bowes Software . (2016). MapInfo professional. Pitney Bowes.
Google Scholar
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
Google Scholar
R Core Team . (2013). R: A language and environment for statistical computing. R Foundation for Statistical Computing. http://www.R-project.org/
Google Scholar
Silverman, B. W. (1986). Density estimation for statistics and data analysis. Chapman & Hall.
Google Scholar | Crossref
Spring, J. V., Block, C. R. (1989). STAC user’s manual. Illinois Criminal Justice Information Authority.
Google Scholar
Steenbeek, W., Weisburd, D. L. (2015). Where the action is in crime? An examination of variability of crime across different spatial units in The Hague, 2001–2009. Journal of Quantitative Criminology, 32, 449–469.
Google Scholar | Crossref
Thompson, H. R. (1956). Distribution of distance to nth neighbour in a population of randomly distributed individuals. Ecology, 37, 391–394.
Google Scholar | Crossref | ISI
Townsley, M., Homel, R., Chaseling, J. (2003). Infectious burglaries: A test of the near repeat hypothesis. The British Journal of Criminology, 43(1), 615–633. https://doi.org/10.1093/bjc/43.3.615
Google Scholar
Turner, M. G. (1989). Landscape ecology: The effect of pattern on process. Annual Review of Ecology and Systematics, 20(1), 171–197. https://doi.org/10.1146/annurev.es.20.110189.001131
Google Scholar
Van Patten, I. T., McKeldin-Coner, J., Cox, D. (2009). A microspatial analysis of robbery: Prospective hot spotting in a small city. Crime Mapping: A Journal of Research and Practice, 1(1), 7–32.
Google Scholar
Vandeviver, C., Steenbeek, W. (2019). The (in)stability of residential burglary patterns on street segments: The case of Antwerp, Belgium 2005–2016. Journal of Quantitative Criminology, 35(1), 111–133.
Google Scholar | Crossref
Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58, 236–244.
Google Scholar | Crossref | ISI
Weisburd, D. (2015). The law of crime concentration and the criminology of place. Criminology, 53(2), 133–157. https://doi.org/10.1111/1745-9125.12070
Google Scholar
Weisburd, D., Majmundar, M. K. (2018). Proactive policing: Effects on crime and communities. National Academies Press.
Google Scholar | Crossref
Zambom, A. Z., Dias, R. (2012). A review of kernel density estimation with applications to econometrics. International Econometric Review, 5, 20–42.
Google Scholar
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