Near-repeat victimisation (NRV) is a phenomenon whereby the proximity to a recently victimised person or property item increases the risk of victimisation of spatially near or similar targets. An example might be the house next door to or several doors from a previously burgled property being targeted soon after. This heightened risk is often identified to be near in space-time, and therefore also decays with space-time.
NRV is explained by two theories, boost account and optimal forager,
Boost account suggests that future victimisation is boosted by the initial event - the offender was successful and got away, so why not try it again? Their previous success and knowledge gained can be applied to similar targets they are familiar with nearby. A typical example is uniform housing styles and layouts (high homogeneity in residential developments makes it easier to identify suitable targets)
Optimal foraging theory considers the offender as the optimal forager, likening them to foraging animals. “As a forager, an animal makes a trade-off between the energy value of the food that is immediately available and the effort that will be expended in reaching a better food source. The better food has to be good enough to offset the energy required to travel and attain it. The quality of food in over-grazed areas diminishes until it re-grows. This is akin to a repeatedly burgled property, where the value of the items taken from this property declines until these items have been replaced. Once an area has been grazed out (i.e. skimmed of the best theft opportunities), the forager moves on.” see JDI Briefs: Predictive mapping
Whilst much of the literature on NRV considers household burglary, its application has been used across a broad range of problems including sex crimes, armed robberies, shootings, street robbery and vehicle crime.
Why might you want to identify NRV patterns?
Inform crime prevention initiatives (i.e. warning near neighbours at heightened risk of burglary)
Follow-up and investigation within defined areas (i.e. viewing potential routes possibly travelled by offenders between events, locating physical evidence to increase the likelihood of solvability - CCTV, suspect descriptions, ANPR or vehicle details etc)
Identifying possible future targets for disrupting offenders (i.e. commercial burglary or commercial robbery NRV, forecasting/predicting patterns in shootings where visible policing can be targeted)
Below you can find links to a brief slide deck showing a variety of methods for practising near-repeat analysis, and a walked-through example using NearRepeat and ptools in R.
Options for conducting near-repeat analysis slide deck with notes included
Open-source software details
GitHub - ptools, Andrew Wheeler
GitHub - NearRepeat, Wouter Steenbeek
Identifying near repeat crime strings in R or Python, Andrew Wheeler
Selection of studies
All burglaries are not the same: Predicting near-repeat burglaries in cities using modus operandi
Near-repeat victimisation of sex crimes and threat incidents against women and girls in Tokyo
The predictive policing challenges of near-repeat armed street robberies
Understanding Crime: Analyzing the Geography of Crime, Spencer Chainey - for the techniques discussed above, more detailed guidance can be found in Chapter 5: Risky facilities, and repeat and near-repeat victimisation