Advising caution in studying seasonal oscillations in crime rates


Kun Dong, Yunbai Cao, Beatrice Siercke, Matthew Wilber, Scott G. McCalla


PLoS One


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.



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