Marchant, P. (2005). Evaluating area-wide crime-reduction measures. Significance, 2(2), 62–65.
Abstract: When we look around an imperfect world, we feel an understandable impulse to improve matters. We may therefore decide to intervene by prescribing medical treatment or by introducing crime reduction measures. But how do we know that what we do is likely to work? In medicine the standard answer is to do a trial; not surprisingly the same is true in crime reduction. But, says Paul Marchant, the lessons learned from medical trials have not been implemented in the latter field.
Marchant, P. (2019). Do brighter, whiter street lights improve road safety? Significance, 16(5), 8–9.
Abstract: Would a billion‐dollar investment in improved street lighting make Australian roads safer at night? Paul Marchant finds the evidence wanting
Marchant, P. R. (2006). Investigating whether a crime reduction measure works. Radical Statistics, 91.
Abstract: Crime is a serious business. It causes great distress and fear. It costs a lot
to deal with its consequences. In these regards crime shares much with
the problem of ill-health and disease. The application of sound science and
statistics has allowed great strides to be made in dealing with problems of
ill health. Medical statistics is one of the recognised, established
disciplines involved in researching healthcare.
The parallels between research in crime reduction and in healthcare do
appear to differ in terms of quality. Although there is still room for
considerable improvement in researching health-care, an investigation
into the underpinning of statistical methods used indicates that the
problems are substantially worse in the study of crime. The consideration
given to statistics in crime studies seems rather flimsy, yet important
claims are made which are statistical at source and may affect policy, and
so can have considerable costs attached. Therefore, for example, it is
important to know whether the underlying crime level has really changed,
rather than just being the result of perhaps sampling variation or some
artefact giving rise to statistical bias or systematic error. This is necessary
when trying to determine whether a Crime Reduction Intervention (CRI)
has actually worked.
I started examining the scientific basis of the claim for the effectiveness for
one particular CRI, basically because I was concerned about negative side
effects and I thought the claim implausible. I remain concerned and
unconvinced. The statistical issues and concerns I raise apply also to
investigating other CRIs and to existing published analyses.
This piece extends work presented in Marchant (2006); earlier work on the
statistical issues involved can be found in Marchant (2005a, b; 2004).
Marchant, P. R. (2010). What is the contribution of street lighting to keeping us safe? An investigation into a policy. Radical Statistics, (102), 32–42.
Abstract: Lighting of roads is said to be of benefit beyond giving the ability to be
able to see in the dark. It is claimed for example that lighting reduces
crime and traffic accidents by a considerable amount and it is
therefore necessary to have it for these reasons. My view remains that
this claim lacks evidence of a sufficiently high standard to warrant
using public safety as an argument. On the other hand there are
reasons why having a lot of light at night might be a bad thing. This
work continues a previous talk and article for Radical Statistics
My initial interest in this area was sparked through my interest in
astronomy because light pollution makes it hard to appreciate the
wonders of the night sky. It seemed to me that the belief that lighting
reduces crime was questionableâ¦. I then embarked on investigating
the crime reduction claim and found it suspect, as detailed in the
2006 Radical Statistics article. (See also Marchant 2004, 2005, 2007,
Marchant, P. R. (2011). Have new street lighting schemes reduced crime in London? Radical Statistics, (104), 39–48.
Abstract: Crime counts published by the Home Office for the Metropolitan Police
Crime and Disorder Reduction Partnership areas have been collated
across the years 2003-2009. The crime counts over time have been
modelled taking into account the âmultilevelâ (years within areas)
nature of the data. The key variable of interest, as a predictor of
within-area change of crime, is the proportion of a Core Investment
Period of new Private Finance Initiative street lighting which had been
completed up to the given time point as a predictor of within area
change of crime. The final model gave a 95% confidence interval for
the multiplier by which the number of crimes is increased of (0.87,
1.11), for a fully implemented lighting programme, consistent with