Outdoor Lighting and Crime - Amper

eliably positive slope with data uncorrected for snow cover (slope = 0.662, r 2 = 0.368, t =
2.644, 19 df, p < 0.05) (not shown). A weighted combination representing a year would
certainly retain statistical significance.
This finding is of far-reaching importance: USA cities with higher values of Morgan Quitno
crime scores among the 21 have reliably more upward light energy loss per person. As with
the finding for Figure 9, it supports the existence of coupled growth of lighting and crime.
Slightly smaller positive correlation coefficients result when the ordinates are rankings, ie
ordinal data. This is not surprising given that the ranking is out of 315 and the ranks have a
close monotonic relationship with the scores. An even higher correlation coefficient, r =
0.797, is obtained when the plot (not shown) is for Morgan Quitno scores against log10 (nosnow
light energy loss per person): r 2 = 0.635, t = 3.473, 19 df, p < 0.01. This indicates that
the relationship between the crime score and light energy loss per person is non-linear.
Another way of interpreting this result is that the crime score increased with the perceived
amount of light per person.
The evidence for the lighting and crime connection appears to be even stronger than is
indicated by Figure 10 and the preceding paragraph when the circumstances are considered in
more detail. Firstly, most of the cities listed by Isobe and Hamamura (1998) appear to have
been selected for measurement on the basis of being relatively bright and well defined in
spatial extent. The Isobe and Hamamura maps of light energy loss distribution show many
discrete areas fainter than most of those included in their listed results. Likewise, many cities
and towns showing as patches of light are identified in a map of central England by Cinzano
(2000a), and only the brightest of these are listed by Isobe and Hamamura. Therefore it is
likely that the 21 US cities listed are generally brighter than random samples of 21 of the 315
US cities ranked in Morgan Quitno (2000) would be. Of the 21 cities, all but one have a
positive crime score and a rank number indicating less safety than for the median rank. If
more cities could be added to the 21 listed to even out the number of positive and negative
crime scores, or to even out the numbers of faint and bright cities, the additional data points
would tend to cluster about the lower left quarter of Figure 9. The slope would then appear
likely to be greater than for the present sample. The statistical significance could well be
larger in this case, and larger again if the light energy loss data have been log-transformed.
It is useful to draw a comparison between the present results and those of Goodman (2002),
who modeled crime with a single 13-variable equation using data from 90 USA midsize
cities. He was able to account for about 75% of the observed variance in crime. Here, the
best result accounts for 63% of the variance in crime for 21 USA cities of small to large sizes,
using just one variable, the amount of outdoor artificial light energy going into outer space per
person.
Regardless of whether the lighting, commerce and crime hypothesis is true, the regression
equation for Figure 10 can be used to estimate the effect of changing the light energy loss per
person in a USA city, all else remaining unchanged. This estimate does depend on the
existence of some sort of causal effect of light on crime. A 10% reduction from 100 to 90
kW.h per person per year, say, would reduce the Morgan Quitno crime score from 217.7 to
198.4, a reduction of 8.9%. From the Morgan Quitno (2000) ranking list, this would reduce
the crime rank from over 297 to less than 294 out of 315. A decrement of about three places
in rank would apply for a 10% reduction in light energy loss per person over most of the 315
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