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Much has been written about intimate partner homicide (IPH), but empirical examinations have been less rigorous and mostly descriptive in nature. The purpose of this paper is to provide an exploration of the characteristics of fatal intimate partner violence (IPV) cases.

A direct comparison of fatal IPHs with both a matched sample of non-fatal IPV cases and a random selection of non-fatal IPV cases is made on a number of offence, offender, victim characteristics and risk-relevant variables.

Despite assertions that domestic homicide is different than domestic violence, in general, few notable differences emerged among the groups. Prior domestic incidents differed between the matched fatal and non-fatal cases, where a greater proportion of the homicide perpetrators had a prior domestic incident. Other differences that were found revealed that more non-fatal perpetrators had substance abuse problems, younger victims and been unemployed at the time of the offence. However, differences were minimal when fatal and non-fatal IPV perpetrators were matched on demographic features and criminal history.

This study highlights that there may be few features that distinguish IPH and non-fatal violence. Rather than be distracted with searching for risk factors predictive of fatality, we should evaluate IPV risk using broad-based approaches to determine risk for reoffending and overall severity of reoffending.

The purpose of this paper is to illustrate how COVID-19 lockdowns in the USA impacted traffic safety.

The authors explored the role of vehicle, user and built environment factors on traffic fatalities in the USA, comparing results during COVID-19 lockdowns (March 19th through April 30th, 2020) to results for the same time period during the five preceding years. The authors accomplished this through proportional comparisons and negative binomial regression models.

While traffic levels were 30%–50% below normal during the COVID-19 lockdowns, all traffic fatalities decreased by 18.3%, pedestrian fatalities decreased by 19.0% and bicyclist fatalities increased by 3.6%. Fatal COVID-19 crashes were more likely single-vehicle crashes involving fixed objects or rollovers. COVID-19 traffic fatalities were most common on arterial roadways and in lower density suburban built environments. Findings suggest the importance of vulnerable road users, speed management and holistic built environment policy when pursuing safety on the streets.

The findings have road safety implications not only for future pandemics and other similar events where we would expect decreases in motor vehicle volumes (such as natural disasters and economic downturns) but also for cities that are pursuing mode shift away from personal automobiles and toward alternative modes of transportation.

The previous articles in this series have concentrated on fatal accidents. This was done for two reasons. First a fatal accident is a more significant consequence of lack of safety than is a non‐fatal one. Second, the data available about fatal accidents are generally more complete and reliable. There are however other measures of risk which can usefully be considered in looking at the picture of safety as a whole.

The purpose of this paper is to demonstrate how the use of data mining (DM) analysis can be used to evaluate how well cameras that monitor red‐light‐signal controlled intersections improve traffic safety by reducing fatalities.

The paper demonstrates several different data modeling techniques – decision trees, neural networks, market‐basket analysis and K‐means models. Decision trees build rule sets that can abet future decision making. Neural networks try to predict future outcomes by looking at the effects of historical inputs. Market‐basket analysis shows the strength of the relationships between variables. K‐means models weigh the impact of homogenous clusters on target variables. All of these models are demonstrated using real data gathered by the Department of Transportation from fatal accidents at red‐light‐signal controlled intersections in Maryland and Washington, DC from the year 2000 through 2003.

The results of the DM analysis will show predictable relationships between the demographic data of drivers and fatal accidents; the type of collision and fatal accidents and between the time of day and fatal accidents.

The limitations of missing or incomplete data sets are addressed in this paper.

This paper can act as a guide to follow for red light camera program managers or local municipalities to conduct their own analysis.

This paper builds upon prior research in DM and also extends the body of research that examines the effectiveness of red camera programs as they mature.

The fatal conceit is the assumption that the world can be shaped according to human desires. This chapter argues that the logic of the fatal conceit can be applied to foreign interventions which go beyond the limits of what can be rationally constructed by reason alone. In suffering from the fatal conceit, these interventions are characterized by: (1) the realization that intentions do not equal results, (2) a reliance on top-down planning, (3) the view of development as a technological issue, (4) a reliance on bureaucracy over markets, and (5) the primacy of collectivism over individualism. These characteristics explain why interventions extending beyond the limits of what can be rationally constructed tend to fail.