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Using citizen notification to interrupt near-repeat residential burglary patterns: the micro-level near-repeat experiment

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Test the efficacy of swift resident notification for preventing subsequent burglaries within near-repeat high-risk zones (NR-HRZ).


The experiment was conducted in Baltimore County, Maryland and Redlands, California. As residential burglaries came to the attention of the police, a trickle randomization process was used to assign each micro-level NR-HRZ (measured 800 ft, 244 m from the burglary location) and associated buffer (400 ft, 122 m) to treatment or control. Treatment was performed by uniform agency volunteers and consisted of swift notification to residents in the area of increased risk of burglary victimization. Treatment and control zones were compared for differences in the mean count of residential burglary using independent samples t tests. Two surveys were administered to gauge the impact of the program: one targeted residents and one targeted at the treatment providers.


There was limited evidence that the treatment reduced follow-on burglaries. The effectiveness of the intervention varied depending on the post-intervention time period being considered. The results of the community survey suggested that: (1) the most frequent crime prevention actions taken by residents were relatively low-cost and low-effort and (2) notification did not increase resident fear of burglary. The treatment provider survey found that the program was effective at increasing the level of engagement between volunteers and the agency and had positive impacts on community perception.


This research demonstrated that law enforcement volunteers can be used to undertake programs that have positive impacts on police-community relations. Limitations included low near-repeat counts, delays in discovering/reporting burglary, and staffing constraints.

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  1. This publication appeared in a Dutch journal. We are citing the summary published in English. Original citation is Peeters, M.P., Van der Kemp, J., Beijers, G., & Elffers, H. (2012) Het effect van intensief surveilleren vlak bij en vlak na een eerdere inbraak [The effect of intensive surveillance close by and immediately after a previous burglary]. Tijdschrift voor Criminologie, 54(4), 335–348.

  2. We thank one of our anonymous reviewers for this insightful observation.

  3. “If You See Something, Say Something” is a Department of Homeland Security initiative.

  4. “Is That Your Bag?” is a Metro Transit Police Department, Washington, DC, initiative.

  5. For more information on WeTip, see

  6. The intervention was originally scheduled to end after 3–4 months. During the planning phase, BCoPD realized that it would be able to allocate enough volunteers for only one treatment per day. This was because volunteers were already used for other tasks in BCoPD. This treatment limit reduced the rate of accrual for treatment and control sites and the experiment had to be extended multiple times. The research and practitioner teams agreed that when we reached the number of sites necessary to achieve the power to detect a large effect, we would end the experiment.

  7. This decision meant that burglaries occurring in the inverse buffer area were not considered for inclusion in the study as originators because their associated NR-HRZ would fall outside the study area. However, subsequent burglaries that occurred within an NR-HRZ that overlapped the inverse buffer area were counted as outcomes. This element of the design may have contributed to the low numbers of burglaries observed during the study period because the inverse buffer area has a relatively high rate of burglary. We did not use an internal buffer in Redlands because only 1.87% (374 out of 20,042 addresses) fell within 400 ft (122 m) of the boundary.

  8. Measurement of Manhattan distance within the NRC “simply adds the difference between the x coordinates of two points to the difference between the y coordinates of two points. It is the same as traveling from point to point first horizontally and then vertically.” (Ratcliffe 2007, p. 9).

    Parameters used in NRC were as follows:

    Iterations requested = 19

    Spatial bandwidth = 400

    Number of spatial bands = 5

    Temporal bandwidth = 7

    Number of temporal bands = 5

  9. Because of the differences in burglary volume, we used 2013 residential burglary data in Baltimore County and January 2012–May 2014 residential burglary data in Redlands.

  10. The NR-HRZ intervention tool was developed by Azavea in R, an open-source software program. Because it is open-source and free, we were able to install it at no cost to the agencies. For more information about Azavea, see For access to the custom software, please see [redacted for peer review].

  11. A Mersenne twister random number generator was used to generate a random sequence of 1000 numbers before the start of the experiment. Assignment to treatment or control conditions was dependent on whether the randomly assigned number was odd or even.

  12. An a priori power analysis to compute required sample size was conducted with G*power 3.0 software (Faul et al. 2007) using the given parameters: a one-tailed test an alpha of 0.05 and power (1 − β) of 0.95. Effect sizes of 0.2, 0.5, and 0.8 were tested and returned sample sizes of 1084, 176, and 70, respectively.

  13. In NR literature, the originator event is the typical term attached to an event that starts a chain of NR crimes. In other words, the first event in a series is the originating event for that series.

  14. Certified Spanish language translators at the RPD translated the crime prevention materials.

  15. The exact time of a burglary is often unknown. For example, a resident may return home from a day at work and find that their home was burglarized. The reported time of the burglary may be 5 p.m., but the burglary occurred between the time the owner left in the morning and the time they returned. Techniques such as Ratcliffe’s (2000) Aoristic Analysis have been used to address this issue in other contexts but are not appropriate here. Because this is a police intervention, the possibility of treatment can begin only once the agency has been made aware of the event. Thus, we use the time that the event is reported to the police as the benchmark for police action.

  16. Crime analysis was also closed on 6 days over the course of the experiment (October 17, 2014, Monday; November 4, 2017, Tuesday; March 5, 2017, Thursday (snow day); May 25, 2015, Monday; September 7, 2015, Monday; October 12, 2015, Monday; November 26, 2015, Thursday). In addition, the experiment did not run on October 31, 2014 (Halloween) because the auxiliary police were needed to keep order.

  17. The limit on the number of treatments per day was also carried out by the program and the cases that were omitted were not subject to human intervention or decision-making.

  18. The reasons for canceling treatments included volunteer safety (reported house with Ebola in treatment area), jeopardy to an undercover operation (had a GPS on a suspect who was in that area, so they did not want uniformed people in the area; ongoing covert narcotic investigations) and having had an arrest already made on the burglary.

  19. The incentive was used to encourage a high response rate based on a sample of addresses that received the treatment. In a comprehensive meta-analysis, Edwards et al. (2002) found that a monetary incentive for survey completion more than doubled the odds of receiving a response.

  20. As noted previously, we took the statistically conservative approach and included these areas in the outcome analysis as if they were treated as intended.

  21. Given the large number of events with zero events in the 1, 1- to 2-, 1- to 4-, 1- to 8-, and 1- to 12-week follow-on period, it is reasonable to question whether the data meet the normality assumption that underlies t tests. We explored these data further by re-specifying models into binary outcomes where 0 = no follow-on events during that temporal band and 1 = one or more follow-on events in that temporal band. Logistic regression models were used to predict this binary outcome; the only predictor in the model was a dichotomous variable for treatment or control condition. Results did not differ greatly from the t tests presented in Tables 3 and 4. In Baltimore County, the marginally significant difference in the NR-HRZ for the 1- to 2-week time period was slightly more marginal (p = .08 compared with p = .13). In Redlands, the 1, 1- to 2-, and 1- to 4-week buffer area model could not be fitted because all treatment buffers had zero follow-on events. In the Redlands buffer area for the 1- to 8-week temporal band, the p value was reduced slightly (from .04 to .08).

  22. An additional analysis of other property crimes in the treatment and control areas revealed that their numbers were also low. There were no significant differences between treatment and control areas for property crime either. Results available from first author.

  23. There is mixed evidence as to seasonal patterns in residential burglary increases. Several studies found a winter increase in burglary (Baumer and Wright 1996; Farrell and Pease 1994; Peng et al. 2011). Other studies reported lower numbers of residential burglaries in winter (Andresen and Malleson 2013; Lauritsen and White 2014; Linning et al. 2017).

  24. We thank Dr. Shane Johnson for this insightful potential explanation for failing to find meaningful outcome differences between treatment and control areas.


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This research could not have been completed without the support of the Police Foundation and the Baltimore County, Maryland and City of Redlands, California Police Departments. At the Police Foundation, Dr. Karen Amendola, who served as Project Director and Maria Valdovinos who coordinated the citizen survey mail outs and assisted with other tasks. There are too many practitioners to mention all by name but several were indispensable. In Baltimore County, Major Mark Warren, Lt. Chris Kelly, Captain Matthew (Mac) McElwee and Mike Leedy (Crime Analyst) and in Redlands, Chief Chris Catren and Tom Resh (GIS Coordinator) made sure we had the support we needed. The authors would also like to recognize the comments provided by Robert Boruch and their Advisory Board (Kate Bowers, Shane Johnson, Jerry Ratcliffe, and David Weisburd) related to the design and conduct of the experiment. The opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect those of the Department of Justice. Any errors are our own.


This project was supported by Award No. 2012-IJ-CX-0039, awarded by the National Institute of Justice, Office of Justice Programs, US Department of Justice to the Police Foundation.

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Correspondence to Elizabeth Groff.

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Groff, E., Taniguchi, T. Using citizen notification to interrupt near-repeat residential burglary patterns: the micro-level near-repeat experiment. J Exp Criminol 15, 115–149 (2019).

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