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Do casinos cause crime
- 1. Do Casinos Cause Crime? An ARIMA Analysis Adam Jacobs Department of Sociology University of Wisconsin
- 13. Total Crime and Theft
- 14. Non-theft crimes
- 17. Seasonal Differencing – Theft
- 18. Seasonal Differencing – Non-Theft
- 21. Autocorrelation graph (correlogram) for larceny
- 22. Theft and Non-Theft have different trends, though both are clearly seasonal
Notes de l'éditeur
- Obviously there are still strong moral/religious objections to gambling; Utah does not (and probably never will) have gambling, and Protestant groups in the South are still very active against casino development
- Autoregression just means that previous values largely determine current values; moving average means that previous values are averaged together with the current one for a stable series
- ARIMA is a univariate analysis, although we can also add independent variables as controls
- Although it turns out the same model will apply to both types of crime
- Differencing and seasonally adjusting the data gets us to the first requirement of ARIMA – a stationary series that is just white noise or a “random walk.” To figure out the shape of our model, we need to look at the autocorrelation functions.
- So crime levels exhibit clear seasonality, with peaks in june and july, and they seem to grow over time. It’s not clear if this is a one-time jump to a new level or a gradual growth over time. Because theft is such a large portion of the overall crime count, let’s subtract it out and look at non-theft crimes.
- With theft removed, the data still looks fairly seasonal, and there still appears to be a jump after about month 50.
- I’ve separated crime into larceny (which has a 90+ correlation with total crime) and all non-theft offenses
- Differencing data is just subtracting out the prior term in the time series. So it’s now centered around zero, with the values representing the deviation from the last month. There’s still some seasonality, so we’ll adjust for that too.
- Now we have a nice stationary series. This is called a “random walk” where the months values are just scattered randomly around zero with no clear pattern. Notice that there aren’t values for the first year, because we’re now subtracting out the previous year’s value. So the value for this July subtracts out last July and this June.
- Differencing and seasonally adjusting the data gets us to the first requirement of ARIMA – a stationary series that is just white noise or a “random walk.” To figure out the shape of our model, we need to look at the autocorrelation functions.
- This graph shows the autocorrelations for the seasonally differenced larceny data; this suggests a moving average
- the effects of casinos don’t appear instantly and it takes a while for the industry to get off the ground.
- We have a large positive interruption term, a growth of more than 250 larcenies per year with the interruption.
- Obviously there’s no effect here. I decomposed some particular crimes like auto theft with similar results – they simply followed overall trends, with no break with casino introduction.
- This is an in-between value, suggesting a fairly rapid ascent to the new level; deltas are between 0 and 1 with 0 indicating instantaneous change.
- Is this surprising? Clearly from the graph you can see an increase; but ARIMA offers us a way to a) test the significance of this jump b) specify the dimensions of the change (in this case rather abrupt and permanent) c) test model for different types of offenses.
- Although the overall population of atlantic city doesn’t change much during this time, and actually drops through the 80s, surely the number of people passing through is rising gradually.