  Objectives: To evaluate the relationship between population size and number
of crimes in cities across twelve countries and assess the impact of per capita
measurements on crime analyses, depending on offense type.
  Methods: We use data on burglaries and thefts at the city level and evaluate
the relationship between crime numbers and population size using probabilistic
scaling analysis. We estimate the growth exponent of each offense type and use
Kendall rank correlation to assess the impact of a linear growth assumption
(i.e., per-capita analysis) on cities rankings.
  Result: In nine out of eleven countries, theft increases superlinearly with
population size; in two of them, it increases linearly. In eight out of ten
countries, burglary increases linearly with population size; in two of them, it
increases superlinearly. In nonlinear scenarios, using per capita rates to rank
cities produces substantially different rankings from rankings adjusted for
population size.
  Conclusions: Comparing cities using per capita crime rates (e.g., crime per
100,000 people per year) assumes that crime increases linearly with population
size. Our findings indicate, however, that this assumption is unfounded,
implying that one should be cautious when using per capita rankings. When crime
increases nonlinearly with population, per capita rates do not remove
population effects. The contrasting crime growth of burglary and theft also
suggests that different crime dynamics at the local level lead to different
macro-level features in cities.
