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The purpose of this paper is to examine the extent and variation in the estimates to which crime can be prevented using patterns of repeats and near repeats, and whether hotspot analysis complements these patterns.

Crime data for four study areas in New Zealand are used to examine differences in the extent of burglary repeat and near repeat victimisation. Hotspots of burglary are also created to determine the extent to which burglary repeats and near repeats spatially intersect hotspots.

The extent of repeats and near repeats varies, meaning there is variation in the estimated prevention benefits that repeat and near repeat patterns offer. In addition, at least half of the burglaries repeats and near repeats were not located within hotspots.

The use of other techniques for examining crime concentration could be used to improve the research observations.

By showing that levels of repeats and near repeats vary, the extent to which these observations coincide in hotspots offers practitioners a better means of determining whether repeat and near repeat patterns are reliable for informing crime prediction and crime prevention activities.

The paper is the first known research study that explicitly measures the variation in the extent of repeats and near repeats and the spatial intersection of these patterns within crime hotspots. The results suggest that rather than considering the use of repeat and near repeat patterns as a superior method for predicting and preventing crime, value remains in using hotspot analysis for determining where crime is likely to occur, particularly when hotspot analysis emphasises other locations for resource targeting.

Initiator crimes, also known as near-repeat crimes, occur in places with known risk factors and vulnerabilities based on prior crime-related experiences or information. Consequently, the environment in which initiator crimes occur might be different from more general crime environments. This study aimed to analyse the differences between the environments of initiator crimes and general crimes, confirming the need for predicting initiator crimes.

We compared predictive models using data corresponding to initiator crimes and all residential burglaries without considering repetitive crime patterns as dependent variables. Using random forest and gradient boosting, representative ensemble models and predictive models were compared utilising various environmental factor data. Subsequently, we evaluated the performance of each predictive model to derive feature importance and partial dependence based on a highly predictive model.

By analysing environmental factors affecting overall residential burglary and initiator crimes, we observed notable differences in high-importance variables. Further analysis of the partial dependence of total residential burglary and initiator crimes based on these variables revealed distinct impacts on each crime. Moreover, initiator crimes took place in environments consistent with well-known theories in the field of environmental criminology.

Our findings indicate the possibility that results that do not appear through the existing theft crime prediction method will be identified in the initiator crime prediction model. Emphasising the importance of investigating the environments in which initiator crimes occur, this study underscores the potential of artificial intelligence (AI)-based approaches in creating a safe urban environment. By effectively preventing potential crimes, AI-driven prediction of initiator crimes can significantly contribute to enhancing urban safety.

The purpose of this paper is to generate information about the contours of police responsiveness, focussing on how quickly and precisely police make firearm arrests after a shooting incident.

Using a modified version of the Knox close pair method, a spatio-temporal clustering technique, over 11,000 shooting incidents and firearm arrests between 2004 and 2007 in Philadelphia, PA were analyzed.

Police are responding quickly and in a geographically targeted fashion to shootings. Across Philadelphia elevated patterns of firearm arrests were approximately two and a half times greater than would be expected if shootings and firearm arrests lacked a spatio-temporal association. Greater than expected patterns of firearm arrests persisted for roughly one-fourth of a mile and for about one week from the shooting incident but the strength of these associations waned over space and time. The pattern of police response varied slightly across different police divisions.

The current method uncovered spatio-temporal patterning and determined when these patterns were significantly different from what would be expected if the events were completely independent. Specific events and processes surrounding each event are not known.

Findings can help inform the knowledge about police behavior in terms of how police produce arrests.

The patterns observed here provide more micro-level detail than has been revealed in previous studies regarding police responsiveness to firearm violence while also introducing a more integrated spatially and temporally specific framework.

The purpose of this paper is to examine the effects of user-defined parameters settings (e.g. interpolation method, grid cell size, and bandwidth) on the predictive accuracy of crime hotspot maps produced from kernel density estimation (KDE).

The influence of variations in parameter settings on prospective KDE maps is examined across two types of interpersonal violence (e.g. aggravated assault and robbery) and two types of property crime (e.g. commercial burglary and motor vehicle theft).

Results show that interpolation method has a considerable effect on predictive accuracy, grid cell size has little to no effect, and bandwidth as some effect.

The current study advances the knowledge and understanding of prospective hotspot crime mapping as it answers the calls by Chainey et al. (2008) and others to further investigate the methods used to predict crime.

Purpose: This study explores the perspectives of data experts (DXs) and refugees on the algorithms used by law enforcement officers and focuses on emerging insecurities. The authors take police risk-scoring algorithms (PRSA) as a proxy to examine perceptions on algorithms that make/assist sensitive decisions affecting people’s lives.

Methodology/approach: In-depth interviews were conducted with DXs (24) in Estonia and refugees (19) in Estonia and Turkey. Using projective techniques, the interviewees were provided a simple definition of PRSA and a photo to encourage them to share their perspectives. The authors applied thematic analysis to the data combining manual and computer-aided techniques using the Maxqda software.

Findings: The study revealed that the perspectives on PRSA may change depending on the individual’s position relative to the double security paradox surrounding refugees. The use of algorithms for a sensitive matter such as security raises concerns about potential social outcomes, intentions of authorities and fairness of the algorithms. The algorithms are perceived to construct further social borders in society and justify extant ideas about marginalized groups.

Research limitations: The study made use of a small population sample and aimed at exploring perspectives of refugees and DXs by taking PRSA as the case without targeting representativeness.

Originality/value: The study is based on a double security paradox where refugees who escape their homelands due to security concerns are also considered to be national security threats. DXs, on the other hand, represent a group that takes an active role in decisions about who is at risk and who is risky. The study provides insights on two groups of people who are engaged with algorithms in different ways.

Since the early 1970s, production planning systems have evolved from material requirements planning (MRP) through manufacturing resource planning (MRPII) into enterprise resource planning (ERP) with simultaneous development of related control systems such as theory of constraints (epitomised by OPT), just‐in‐time (JIT), etc. One key area for all manufacturing companies is the planning and control function. There is a wide range of generic proprietary software available that aims to meet a company’s planning and scheduling requirements. The difficulty experienced by many companies is not only in examining available software, but also in understanding the match between business needs and the capabilities of that software. This paper first sets out some common manufacturing classification systems, then attempts to map them against accepted paradigms for production planning and control approaches. Analysis confirms the need for a more rigorous approach to software selection, and the need for a complete understanding of the drivers of the production control process before this can be achieved. The paper goes on to discuss a method for mapping these drivers, with the aim being to create a series of reference models for production planning and scheduling.

The purpose of this paper is to use an evaluation of a micro-place-based hot-spot policing implementation to highlight the potential issues raised by data quality standards in the recording and measurement of crime data and police officer movements.

The study focusses on an area of London (UK) which used a predictive algorithm to designate micro-place patrol zones for each police shift over a two-month period. Police officer movements are measured using GPS data from officer-worn radios. Descriptive statistics regarding the crime data commonly used to evaluate this type of implementation are presented, and simple analyses are presented to examine the effects of officer patrol duration (dosage) on crime in micro-place hot-spots.

The results suggest that patrols of 10-20 minutes in a given police shift have a significant impact on reducing crime; however, patrols of less than about 10 minutes and more than about 20 minutes are ineffective at deterring crime.

Due to the sparseness of officer GPS data, their paths have to be interpolated which could introduce error to the estimated patrol dosages. Similarly, errors and uncertainty in recorded crime data could have substantial impact on the designation of micro-place interventions and evaluations of their effectiveness.

This study is one of the first to use officer GPS data to estimate patrol dosage and places particular emphasis on the issue of data quality when evaluating micro-place interventions.