Outdoor Lighting and Crime - Amper

proportional to the amount of outdoor artificial ambient light per person. This variable is
mentioned again in Section 5.2.
It seems unlikely that the picture presented by these correlations would change much in either
direction if data for more of the whole century became available. By inspection, Garstang’s
population-based California skyglow growth data would appear likely to give correlation
coefficients with crime similar to those for the population. Garstang’s data were not included
in the figures or in Table 1 because the observational evidence for exponential growth is so
strong.
Hays (1970, p 565) stated, “There is no guarantee that all psychological relationships of
theoretical or applied interest must be linear in form.” The logarithm of light flux might be a
more suitable variable to investigate for linear correlation, given that the visual system tends
to respond to light in a logarithmic fashion, and given the apparent exponential nature of
existing growth in artificial skyglow. Hays (1970, p 563) indicated that using the logarithm
of an exponential quantity in this way is appropriate. The correlation coefficients of the
various crime and justice data, and population, with log skyglow are therefore included in
Table 1. Simple counts indicate that these log skyglow correlation coefficients with crime
and justice data are most often the highest values across the three columns. The small
numbers and partly overlapping data sets deter a formal significance test but future work
might be better organised to deal with this issue. Not surprisingly, population correlates
better with log skyglow than with skyglow in 4 of the 5 cases in Table 1.
A further caution seems worthwhile about the relative amounts of crime in the countries
studied. The crime data are from police records. Crime surveys in 14 countries indicated
rather different relativities, and also revealed a latitude effect- colder countries tended to have
lower crime rates (Walker, Wilson, Chappell and Weatherburn 1990). However, it is the rate
and direction of change of crime, and of artificial lighting, with time that are of most interest
here, rather than the absolute scales of crime and lighting.
It might seem rather naïve to expect total crime to correlate strongly with lighting, given that
crime was problem long before bright lighting became widely available or used 23 (eg Sharpe
2002). Therefore, it might appear more reasonable to use the increment in crime above some
pre-lighting base crime rate as a variable in looking for lighting and crime correlations.
Subtracting a constant base rate from any of the crime data does not change linear correlation
coefficients, however. Likewise, it is almost immaterial in this context whether total skyglow
or just its artificial component is used. The correlations shown in the table are a reasonable
guide as they stand. The squares of the correlation coefficients have been included to indicate
the proportions of variance in crime and justice data explained by skyglow and population.
Correlation studies rate a mere 1 on the Scientific Methods Score used by Sherman,
Gottfredson, MacKenzie, Eck, Reuter and Bushway (1997), but that is in the context of
experimental hypothesis testing rather than exploratory analysis of complex systems. The
correlation studies used here are quite appropriate for guiding hypothesis formulation.
23 For example, the initial part of European settlement of Australia from 1788 was primarily a
result of transportation of a copious supply of British convicts. (Among the reasons for doing
so at the time, it was thought to be a suitable alternative to execution!)
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