April 8, 2012

Statistical crimes double near liquor stories[updated]

Stuff has the  headline “Crime doubles close to liquor outlets”, based on an analysis from the University of Canterbury.  Now, can we think of possible non-headline explanations for this?  Indeed we can. As the story admits, near the end

The areas with the most serious violent crime had more Maori and young males, over-represented in crime statistics, and the highest population densities.

and

The three spikes with the highest numbers of liquor outlets were Auckland central (447 alcohol licences), Wellington central (423) and Christchurch central (394), all of which had high crime rates.

These numbers raise the question of what sort of alcohol licenses were included.  I’d be surprised if there were 447 liquor stores in Auckland Central, but if you include pubs and licensed restaurants the numbers look more plausible. If so, we’re not talking about liquor stores at all.  The fact that the three CBD areas (all places with bans on alcohol consumption in the street)  top the list also suggests that there’s a problem with denominators: since many of the people in the CBD don’t live there, rates of crime per 1000 population  will tend to be inflated.

What is really infuriating is that the researchers actually did a better version of the analysis, but we don’t get to see it. In the last paragraph of the story, we get

Day said the correlation was weaker, but still held, when those factors were statistically removed from the equation.

So why don’t we get told the numbers that at least have a chance of meaning something, rather than the “crime doubles”?

Updated to add:  A commenter on a later post gave a link to the published paper,  and the adjustment brings relative rates of 2.4, 2.0, and 2.4 for any license, on-license and off-license, respectively, to 1.5, 1.6, and 1.4.   Also, without adjustment there is a much higher rate in for the areas closest to off-license stores, but after adjustment the elevated rate is constant out to 5km, which seems much less plausible.

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Thomas Lumley (@tslumley) is Professor of Biostatistics at the University of Auckland. His research interests include semiparametric models, survey sampling, statistical computing, foundations of statistics, and whatever methodological problems his medical collaborators come up with. He also blogs at Biased and Inefficient See all posts by Thomas Lumley »

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