The Near Repeat Calculator

What are near repeats?

The near-repeat phenomenon was first discussed and published in 2003 by Townsley and colleagues (Townsley, Homel, & Chaseling, 2003), and soon after by Bowers and Johnson (Bowers & Johnson, 2004; Shane D. Johnson & Bowers, 2004). It is the realization that when a location in the target of a crime (such as a burglary for example), there are two interesting features that are common. First, the same location is at an elevated risk of further burglary for a certain amount of time. This is repeat victimization. Second, nearby locations to the first burglary are also at a heightened chance of being a crime target for a limited number of weeks. This second feature is the near repeat phenomenon. It is the risk to nearby locations after an initial crime event. 


In the nearly two decades since the near repeat hypothesis was suggested, the phenomenon has be identified in a variety of crime types. Not just burglary (S.D. Johnson et al., 2007; Moreto, Piza, & Caplan, 2014), but also street robberies (Haberman & Ratcliffe, 2012), vehicle theft (Piza & Carter, 2018), shootings (Ratcliffe & Rengert, 2008), and even insurgent attacks on coalition forces in Iraq (Townsley, Johnson, & Ratcliffe, 2008). It seems to be a fairly ubiquitous feature of spatial crime patterns. 


Detecting the near repeat phenomenon


There are a number of free software options for detecting the near repeat phenomenon. All of them use the Knox test to compare the spatio-temporal relationship in space and time between all events. The pattern expected under the null hypothesis (where no near repeat pattern exists) is estimated by simulating the absence of a pattern. This usually done by randomizing the date between events, using a process called Monte Carlo simulation. If the last couple of sentences had your head spinning, don’t worry – there are free software programs that can do all of the hard work for you. These can be explore as a Python script, and R package, and a standalone executable program that can be downloaded to your computer.

 
The NearRepeat R package


In 2019 Dr Wouter Steenbeek published the R package ‘NearRepeat’. The program, as well as support materials can be accessed here. The website also include information about installing the program into R and details regarding the parameter choices. It requires some understanding of the R statistical language. 


The NearRepeat Python script


In collaboration with Dr Steenbeek, Dr. Toby Davies published a similar Python script to undertake the same function. The code as well as useful documentation demonstrating the implementation can be found at https://github.com/tobydavies/NearRepeat


The Near Repeat Calculator


The Near Repeat Calculator was developed over a decade ago as a standalone executable program to estimate the near repeat hypothesis. In the development of their programs, Drs. Steenbeek and Davies identified discrepancies in the Near Repeat Calculator that existed in version 1.3 and earlier. These discrepancies are detailed here. The current version corrected the discrepancies and adjusted some other aspects of the software so that the Near Repeat Calculator and its output is more in line with the R and Python scripts, enabling analysts to validate findings across platforms.

 
The Near Repeat Calculator can be downloaded here, with version 2.0 being the current iteration. 


Other resources 


Dr. Spencer Chainey has a nice page with information about repeat and near repeat victimization at the Jill Dando Institute.

 


References cited


Bowers, K. J., & Johnson, S. D. (2004). Who commits near repeats? A test of the boost explanation. Western Criminology Review, 5(3), 12-24. 
Haberman, C. P., & Ratcliffe, J. H. (2012). The predictive policing challenges of near repeat armed street robberies. Policing: A Journal of Policy and Practice, 6(2), 151-166. 
Johnson, S. D., Bernasco, W., Bowers, K. J., Elffers, H., Ratcliffe, J. H., Rengert, G. F., & Townsley, M. (2007). Space-time patterns of risk: A cross national assessment of residential burglary victimization. Journal of Quantitative Criminology, 23(3), 201-219. 
Johnson, S. D., & Bowers, K. J. (2004). The stability of space-time clusters of burglary. British Journal of Criminology, 44(1), 55-65. 
Moreto, W. D., Piza, E. L., & Caplan, J. M. (2014). “A plague on both your houses?”: Risks, repeats and reconsiderations of urban residential burglary. Justice Quarterly, 31(6), 1102-1126. 
Piza, E. L., & Carter, J. G. (2018). Predicting initiator and near repeat events in spatiotemporal crime patterns: An analysis of residential burglary and motor vehicle theft. Justice Quarterly, 35(5), 842-870. 
Ratcliffe, J. H., & Rengert, G. F. (2008). Near repeat patterns in Philadelphia shootings. Security Journal, 21(1-2), 58-76. 
Townsley, M., Homel, R., & Chaseling, J. (2003). Infectious burglaries: A test of the near repeat hypothesis. British Journal of Criminology, 43(3), 615-633. 
Townsley, M., Johnson, S. D., & Ratcliffe, J. H. (2008). Space time dynamics of insurgent activity in Iraq. Security Journal, 21(3), 139-146. 

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