Year-over-year Crime Stat Reporting with ISO Week Date

Does your organization report a weekly crime stat? Is part of that report a measure of how the week compares to the same week the year before? If so, it’s important that you understand how ISO week dates work so that your report offers an accurate comparison between this year and prior years. In this post I am going to first discuss what an ISO week date is and then I am going to explain how it helps create better crime stat reports.

First, what is an ISO week date? Hopefully you’re familiar with the International Organization for Standardization (ISO). They publish a lot of standards and ISO 8601 is the standard that deals with a calendar system that gives each week of the year a number. For example, this article was written on October 21, 2012, which, in ISO date week notation, is written 2012 W42 4. Breaking the date down: the first number is the year, the second number is the week of the year (42 in this case) and the third number is the day of the week (the 4th day is a Thursday because the system states that Monday is the first day of the week). Most of the time a year has 52 weeks, sometimes it has 53 weeks to handle leap years. According to Wikipedia the system is most often used in government and business for keeping fiscal years.

This is pretty straightforward to understand but the tricky parts comes when determining the first week of the year as the first week introduces slight discrepancies between the ISO system and the traditional Gregorian calendar that people are used too. To wit: ISO 8601 defines the first week as the week with the year’s first Thursday in it. Using 2012 as an example, the first Thursday of 2012 was January 5th and, recalling that the ISO week begins on Monday, this means that the first day of the ISO year, that is 2012 W01 1, was January 2nd, 2012. But what about January 1st, 2012? It was actually the last part of week 52 of 2011 or 2011 W52 7. Yes, I know, it’s weird, but I think its utility outweighs its weirdness.

If you’ve made it this far in the post you’re probably thinking: “This seems overly complicated, why should this concern someone reporting crime stats?” A valid question. Consider a weekly crime report that covers October 8th through October 14th from 0000 hours to midnight. That’s a whole week and conveniently it is also week 41 of 2012. Now let’s say that for comparison our theoretical crime report also tabulates the stats for October 8th through October 14th for 2011. Is there a problem with this? Does it matter that the 2011 numbers run from October 8th (a Saturday in 2011) to October 14th (a Friday in 2011) instead of Monday to Sunday like it does in 2012? Is it enough that we capture one of each weekday in a weekly crime report? Is it relevant that the Saturday from 2011 is part of ISO week 40 while the Saturday from 2012 is from week 41?

I think this last point is key. Calls for service have a definite seasonal trend which means that for any particular day of the week, Saturdays for example, the calls for service will vary according to the week of the year. This means that, historically speaking, the number of calls for service on the Saturday in week 41 are likely going to be consistently different than the number of calls for service on the Saturday in week 40 and the same goes for every other day of the week. Basically, days are not created equal and that means that if you want to compare apples to apples for crime reports you should really compare equivalent time periods. The easiest way to do this is, you guessed it, by using ISO week dates and comparing week 41 from 2012 to the week 41 from 2011. In our example, compare October 8th through October 15th 2012 to October 10th to October 16th 2011.

But is this reasonable? Why is the week the unit of analysis here? In non-leap years October 8th through 14th is always the 281st through 287th day of the year, isn’t there consistency in that that gets thrown out if we adopt the weekly approach? Perhaps but I don’t think day-of-year consistency is more useful than the weekly one. Most analysts recognize that calls for service have a distinct weekly pattern—there are more calls for service on Friday and Saturday then other days of the week—that is imposed upon the larger seasonal trend. By adopting the ISO week date system we can align reporting with this natural frequency found in the crime data and by locking into the frequency we can exploit it to make comparisons easier.

So what’s the bottom line? Because calls for service show a seasonal trend you should endeavor to compare the same weeks when performing a year over year analysis. And because calls for service show a weekly trend it makes sense to lock your reporting to a standard week that makes comparisons easy and straightforward.  The best way to accomplish both of these goals is to adopt the established ISO week date system for weekly crime reports.

Testing the Significance of Day-of-Week Analysis with the Chi-Square Statistical Test

A common weapon in the crime analyst arsenal is the day-of-week analysis. One problem with the technique is that many people accept the results uncritically and assume that if, for example, there have been several more incidents on Friday than any other day then Friday is the best day for a focused action. The problem is that day-of-week analysis is seldom accompanied by a confidence interval or evaluated to find out if the results are statistically significant. Without performing the statistical follow-up it’s impossible to know if the results actually represent anything other than randomness.

In order to evaluate statistical significance for something like a day-of-week analysis we need to use the Chi-Square Statistical Test. The chi-square test is what’s known as a non-parametric test which is a fancy way to say that it will work with nominal data (that is categories) and that the hypothesis being measured does not require normal distribution or any variance assumptions (a bonus for testing things like a day-of-week breakdown). The test works by performing a goodness-of-fit analysis between the observed data and an expected distribution. The point is to determine if the differences between the observed and expected are caused by something other than chance.

To make things more concrete let me introduce the actual data I will be evaluating. The following image lists some (fictional) motor vehicle collision (MVC) counts over the course of several weeks. Looking at the data with an uncritical eye might lead some to assume that Fridays are the worst day for collisions and that we should do a traffic blitz on that day. The purpose of the exercise is to see if that’s a valid assumption.


So this is our “observed data” but what about the “expected distribution”? In order to answer we need to identify another property of the statistical significance test: the null hypothesis. In any statistical test we need two hypotheses: the research hypothesis and the null hypothesis. The research hypothesis is what we’re hoping to show, in this case, that the day of week means something for MVCs. The null hypothesis is the opposite and in our example it means that the day of the week doesn’t factor into when collisions take place. Since the null hypothesis states that the day doesn’t matter then we should expect the same number of collisions each day. If the total number of collisions is 140 then the expected value for each day is 140/7 = 20. With this new info have a look at the following image that lists both the observed and theoretical distributions.

The chi-square calculation is pretty straight forward: subtract each observed value from its expected value, square it, and then divide by the expected value. So for Sunday the value would be (14-20)^2/20 = 1.8., for Monday it’s: (16-20)^2/20 = 0.8 and so on for each of the 7 days. Then you sum up each of the calculated values to arrive at a total score (8.45 in this case), as in the image below.


The next step is to check the score against what’s known as a chi-square critical value table, which is pre-computed tables of critical values for different confidence intervals and degrees of freedom. The tables are all over the Internet but I used this one for this example. I wrote above about confidence intervals, and I’m going to pick the 95% interval just because, which means that my alpha value (look at the table) is 0.05 (i.e. 1 – 0.95). The second value is degrees of freedom which is a measure of how many elements are being tested, minus one. For this example, 7 days in the week, minus one, means we have 6 degrees of freedom (df). Knowing these two pieces of information we can look up the chi-square critical value. Go down to the row for df=6 and across to the column for alpha=0.05 and the number is 12.592.

12.592 is greater than 8.45. That means we cannot reject the null hypothesis. That means we can’t say that the observed MVCs across the days of the week is due to anything other than chance. It means that we can’t say that any particular day is better than another to do a traffic blitz because, statistically, the observed data could have happened by chance. This is a troubling truth if we’ve been telling officers to focus on Fridays.

So this post is kind of a bummer if you’re used to directing officers based on day-of-week analysis. A lot of officers don’t want to hear about statistical significance (well, actually, none, I don’t know any who would want to hear about it) but the hard truth is that math is telling us that what we think is a pattern (e.g. more MVCs on Fridays) is often not distinguishable from chance. On the bright side, when we do get to reject the null hypothesis we are able to objectively demonstrate that, in fact, yes, something is going on that warrants attention. And of course the chi-square test is applicable to all sorts of categorical data sets, many of which demonstrate statistical significance, so hopefully you can add it to your toolbox for use in the future.

Important Bonus Math:
There are a few caveats when working with chi-square. First, it’s a data hungry method, the more values you have, the better your test will be. Second, it is skewed to rejecting the null hypothesis. I don’t know exactly why, that’s what a smart stat guy wrote, but it means that if you are forced to accept the null hypothesis, you’re really in bad shape. Third, don’t run the test if you have an expected distribution with less than 5 in each of the cells. This means, don’t let the expected value be less than 5 for each of days of the week, that’s too little data (see the first caveat) and your results won’t be good.