Outdoor Lighting and Crime - Amper

5.2.3.7 Summary and discussion of results for USA cities
The material in this Section 5.2.3 indicates clearly that there is a factor of over 10 times in the
amount of light energy loss per unit area between the 21 studied cities and up to 25 times in
the amount of light energy loss per person. 63 No significant correlation was found between
UCR Index crime rate and light energy loss per unit area. When the crime measure used was
the Morgan Quitno score, a survey-weighted derivative of UCR data, equivocal positive
associations were found with light energy loss per unit area. Strong positive associations
were found between UCR crime rate and light energy loss per person, and even stronger
between Morgan Quitno score and light energy loss per person. In other words, cities with
large light energy losses tend to have high crime. City population size did not contribute to
this result: any effect was in the opposite direction.
It is important to note the shortcomings in this analysis as they degrade the reliability of the
results to some unknown extent:
• the shape and geographical location of the area involved in each light energy loss
measurement was not described by Isobe and Hamamura;
• the areas of individual cities used in determining light energy loss per unit area could
not readily be checked against the areas implied by the population counts in the UCR
lists;
• uncertainties about matching of the areas involved in the crime and lighting data are
exacerbated in the case of New York City because of the cryptic nature of the satellite
data table entry;
• the reflectance corrections for snow cover were calculated estimates rather than on-site
observational measures; and
• the crime recording period was about a year after the satellite measures, although this
might be a near-optimal delay duration and in the right direction for indirect effects of
lighting according to the lighting, commerce and crime hypothesis.
Section 4.2.2.4 mentioned the prospect of using observations of Curve E data points to predict
overall crime as a function of the amount of ambient artificial light in a place or collection of
places at night. The result has been achieved in this Section 5.2.3 using satellite observations
and UCR crime data instead, which means that the inverse process might now be used to
calculate the form of Curve E, or Curve F as the case may be. There are close relationships
between Curves E/F and the regression lines that approximate the underlying ‘real’ curves in
Figures 7, 9 and 10. The differences largely concern scaling and the axis variables. The
positive slopes of the regression lines in Figures 9 and 10 indicate that the slope of a
63 NASA (2000) is a popular account of urbanisation research involving satellites. It
describes how urban and peri-urban boundaries are drawn on the basis of processed pixel
intensities derived from DMSP OLS nighttime data. The technique, which includes ground
truth checks, appears to depend on a more-or-less constant relationship between population
density and light energy loss per unit area at each type of boundary, although detection of
small-scale spatial variations in light energy loss is also part of the process. Urban areas are
estimated to have about ten times the population density of peri-urban areas. Further
professional work on the topic might well provide welcome unplanned assistance in data
collection and refinement for lighting and crime work.
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