Outdoor Lighting and Crime - Amper

crime.) Regardless of what the conversion factor might be between the satellite data and the
actual mean city illuminances, the crime change represented by the slope of the regression
line in Figure 13 seems improbably large to ascribe wholly to an effect of light over the
lighting range covered. For theproportion of crime reduction in dark conditions to be
realistic, the relatively steep slope could apply over less than one log unit of light reduction,
and the observed curve would need to flatten out substantially at lower levels.
Looking at the issue in another way, the upper and lower light energy loss values are 11.4 and
5.07. Eight successive lighting reduction treatments of 11.4/5.07 would reduce the ambient
light by 2.8 log units. If the ambient light level is accepted as low enough to have crime
reduce in the short term to say 25% of its initial value, this would require the crime rate to
reduce by just 16% per treatment, about a quarter of the reduction observed. Possible
explanations for the steep slope include:
• the slope indicates a genuinely steep part of the underlying curve, which must be
generally flatter elsewhere;
• the slope has been exaggerated by some non-lighting factor that has affected the
brighter and dimmer cities differentially;
• observed crime levels have not stabilised after recent lighting changes, bearing in
mind the substantial and discontinuous growth in outdoor lighting;
• the lighting peaks at the centres of the brighter cities have been underestimated
because of sensor saturation or insufficient spatial resolution; and
• the large range in light levels within individual cities is masked, possibly
inappropriately, by the use of the arithmetic mean as a single quantity to characterise
the lighting.
There seems to be no way of testing for these possibilities with the information at hand. In
the meantime, the lighting, commerce and crime hypothesis is supported by the direction of
the result for English cities but the regression equation appears to be unreliably optimistic for
predicting the magnitude of the crime reduction expected with reduced lighting at night.
Regardless of the problems, the observed result is certainly of opposite sign to that implied by
the results of the Farrington and Welsh (2002a,b) meta-analysis. This is further independent
evidence that the meta-analysis result does not reflect the real-world relationship between
lighting and crime.
It remains to check the USA results. Here the ranges in light energy values are greater: a
factor of 9.80 for Figures 9 and 10, or 25.0 if the data are not corrected to no-snow
conditions. 69 Curve E is not readily applicable in the case of Figure 10 because the crime
measure there is relative rather than absolute. Figure 9 is therefore tested instead.
The reduction in crime for one light energy reduction of 9.8 along the regression line in
Figure 9 is a factor of 0.595. Repeating this two more times would represent a lighting
change of nearly three log units and a crime reduction to 21%. This does not appear to be an
69 Compared with the light data range in Figure 13 for English cities, the greater range for
USA city light data in Figure 9, although corrected to no-snow conditions, possibly results
from factors such as having a larger sample number, a broader geographic extent, and greater
autonomy for the US states in terms of how they light their cities.
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