This is my first attempt to use open data for data visualization in web presentation and for a mobile app. The idea was to cross-pollinate promotion, but it didn’t go so well – more on this later.
According to data provided by state police, highway authority and local traffic wardens, there occurred a little less than a million traffic violations between start of 2012 and September 2014. Given that there are 1,300,000 registered vehicles and 1,400,000 active driving licenses in the country, this is a lot. A big majority of them are parking and toll tickets.
In the main article, there are a lot of images and charts. For example, I analyzed data for major towns in Slovenia to get the streets with the highest number of issued traffic tickets. Here’s an example for Ljubljana:
I had temporal data for each issued ticket, so I could also show on which streets you are more likely to be ticketed in the morning, midday or evening. On the image below, morning is blue, midday is yellow, and evening is red.
This is, however, only the beginning. Here are questions I tried to answer:
- Are traffic wardens and traffic police just another type of tax collectors for the state and counties?
- Do traffic wardens really issue more tickets now than in the past, or is that just my perception?
- Which zones in bigger towns are especially risky, should you forget to pay the parking?
- Are traffic wardens more active in specific time intervals?
- Does the police lay speed traps in locations with most traffic accidents? What about DUI checking?
- How does temperature influence the number of issued traffic tickets?
- Does the moon influence the number of issued traffic tickets? If so, which types?
- Where and when are drivers most at risk of encountering other drunk drivers?
- Where does the highway authority check for toll, and when to hit the road if one does not want to pay it?
- How can we drive safer using open data?
Be sure to read the main article to see all the visualizations and interactive maps. There are also videos, for example this one, showing how the ticketing territory expanded through time in Ljubljana:
Some other highlights:
The big finding was a sharp increase of number of parking tickets issued in Ljubljana by the end of 2013, which coincides with publishing of debt that the county has run into:
There’s an interactive map showing the quadrants with most DUI tickets and their distribution by day of week and month in year:
Mobile app for Android
I also wrote an Android mobile app (get it on Google Play if you are interested) that locates the user and shows locations of violations of selected type on the map, as well as a threat assessment, should she want to break the law. Here’s the description on Google Play:
The app helps the user find out where and when were traffic tickets issued in Slovenia, thus facilitating safer driving.
Ticket database is limited to territory of Republic of Slovenia.
Choose between these issued citations to show in app:
– driving while using a cellphone
– ignoring safety belt laws
– unpaid toll
and traffic accidents.
The app will locate you, fetch data about traffic citations issued in your vicinity, and show them on map. To see citations, that were issued somewhere else, click on map. Additionally available is summary of threat level, derived from statistical data, collected by government agencies.
Locating the user and showing dots on map wasn’t really a challenge, but I wanted to show a realistic threat assessment, based on location and time. To do that, I wrote an API method that calculates the number of tickets issued on the same day of week in the same hour interval and then draws a simple gauge.
Let’s say, for example, that you find yourself in the center of Ljubljana on Monday at noon, don’t have the money for parking fee, and you really only want to take a box to a friend who lives there. You’ll be gone for ten minutes only, so should you risk not paying the parking fee?
The app finds out the total number of tickets issued on Mondays in the three-hour period between noon and 3 PM, then graphically shows the threat level along with some distributions, something like this:
It works pretty well, and I use it sometimes, although I admit that its use cases may be marginal for majority of population. It does get ten new installs a day, although I don’t know how long this trend will continue.
I did send out press reviews and mounted a moderate campaign on Twitter (here’s the app’s account), but it amounted to precious little. Maybe the timing was bad – I launched it during Christmas holidays, when Internet usage is low. Or this type of app just isn’t so interesting.
I’m currently working on analysis of parking tickets for New York City, maybe that will be more interesting. There were, after all, more than nine million tickets issued there, and data is much richer.