Abstract
Most types of crime are known to exhibit seasonal oscillations, yet the annual variations in the amplitude of this seasonality and their causes are still uncertain. Using a large collection of data from the Houston and Los Angeles Metropolitan areas, we extract and study the seasonal variations in aggravated assault, break in and theft from vehicles, burglary, grand theft auto, rape, robbery, theft, and vandalism for many years from the raw daily data. Our approach allows us to see various long term and seasonal trends and aberrations in crime rates that have not been reported before. We then apply an ecologically motivated stochastic differential equation to reproduce the data. Our model relies only on social interaction terms, and not on any exigent factors, to reproduce both the seasonality, and the seasonal aberrations observed in our data set. Furthermore, the stochasticity in the system is sufficient to reproduce the variations seen in the seasonal oscillations from year to year. Researchers should be very careful about trying to correlate these oscillations with external factors.
Highlights
Rising temperatures are inspiring various cities and nations to examine the expected results of global warming on social behavior, and generally the ability of mankind to thrive on a hotter planet
While we believe variations in crime rates will be affected by the weather, our goal is to show that variations in the amplitude of the seasonal oscillation in crime from year to year
An underlying belief in the criminology community is that the seasonal oscillations are often driven by an external force, such as temperature or rainfall
Summary
Rising temperatures are inspiring various cities and nations to examine the expected results of global warming on social behavior, and generally the ability of mankind to thrive on a hotter planet. As an example Thriving in a Hotter LA, a UCLA sponsored program in response to the President’s request for solving Grand Challenges, strives to pro-actively stem the negative effects of climate change through social change in Los Angeles. While there is evidence that large aberrations in temperature can lead to a measurable change in human hostility [1], the correlation between crime rates and seasonal temperature variations, believed to be important in the criminology literature [2], is poorly understood. Considering one of the most noticeable consequences of global warming is a shift in weather patterns, either locally warming or cooling, there is a need to understand how crime rates may change in response to the new weather patterns. By applying a nonparametric analysis to daily crime data collected from Los Angeles, controlled by the LAPD and available in the S1 File, and Houston [3], we are able to extract and decouple the seasonal oscillations in crime rates from the long term trends and higher order
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