Since I’m currently visiting the Montreal area, I figured why not become more familiar with the city and its crime. But why crime? Surely there are nicer things to learn about Montreal than its crime rates? The reason why I’ve picked crime is that police data on crimes is usually publicly accessible, and contains geographic and date information. It’s an easy source of data that you can explore with.
Background on Data
I first discovered this visualization when looking for Montreal crime data. Besides the fact it is only in French, it only allows you to filter by type of category and date. Once I found this visualization though, it was only a hop and a skip to finding the raw data behind it: http://donnees.ville.montreal.qc.ca/dataset/actes-criminels
Also incredibly helpful, is the fact that this page provides some background on methodology and a data dictionary. You can read the full details on the website but essentially, for privacy reasons, they made the location of the incident at a street intersection and exact time grouped into periods of day, evening, and night.
I noticed right away that some information provided in the raw data isn’t captured by the current visualization. Such as time of day and the neighborhood station number that covers the area where the criminal act occurred. Perhaps we can explore this information more.
Building the Visualizations
I decided to use Tableau to build my visualizations. 1) Because it is pretty 2) Can easily publish and share 3) Time investment is lower compare to other options (I didn’t want to spend more than a couple hours on it).
One thing I had hope to do in the Tableau is to have the ability to switch between English and French in the visualization depending on the user. Unfortunately, while you can change the language of the Tableau software, you can’t really have labels/titles shift based on a language selection parameter (https://community.tableau.com/ideas/1649). Apparently the closest you can come to it is hacking XML but it still results in separate workbooks for each language (https://tableauandbehold.com/2015/08/20/full-localization-of-tableau-workbooks/). Obviously the level of workaround is quite high for this particular issue and doesn’t even result in a ideal finished product. Cue the hate part of the hate/love relationship with Tableau
Despite this initial disappointment, I was at least able to easily change the Alias/Titles to the English translation (apologies in advance for any mistranslations, I was relying on google translate). After some dragging/dropping, and playing around with colors, I was able to produce this.
Conclusion
Based on my visualizations, it looks like overall, most crime incidents in Montreal happen during the day (i.e. not evening or night).
This in itself isn’t terribly surprising. Many people commute into Montreal for work so they aren’t staying there during evening/night hours. One might suppose that during the times there are less people in Montreal, there tends to be less incidents. When we look at incidents by category, we begin to see a different story emerge. For example, breaking and entering into a residence, tends to have an equal number of incidents between day hours and evening hours.
Also defying the overall trend, it seems that acts of robbery occur the most often in the evening.
And for incidents like murder, we don’t see a particular pattern at all.
We see another story when we look at criminal acts reported by time (i.e. month). It seems that in the winter months, in general there are less criminal acts registered.
Visually, it looks like January and February are low points in incidents across all categories although the drop is more severe in some categories than in others. If I had to assume why, I would guess this is largely due to how cold it is in Montreal in the winter. People are less likely to hang around outdoors and there is probably also a drop in tourism to the island.
In regards to my last visualization, I won’t go into too much detail other than as you filter between different categories, you will notice that certain station numbers deal with certain incidents more often than others.
Future Directions
Hopefully this was a good crash course on exploring the SPVM data on registered criminal acts, but there is always room for improvement. If I wanted to invest some more time, I could probably put in some different views by station number and add in the geography for those neighborhoods by finding a secondary source. I could also add in data on temperature highs by date from another source.