I’ve recently participated in a couple of hackathons to keep brushed up on my coding skills. In both instances, the hackathons were motivated by working with open data, which I also find interesting. Open data is usually datasets that government has sitting in a database somewhere that may be useful for apps, programs or visualization that they do not necessarily have time to do themselves. Since the people of the country technically “own” the data, there has been a movement lately to release it to the people to use. In both hackathons, I worked with my friend (and media and design expert) – [Carlos Saavedra](https://web.archive.org/web/20140517043126/http://carlossaavedra.com/).
The first was the “Open Data Guelph” hackathon which was put on by the City of Guelph. You can see the data sets available at the [Guelph Open Data website](http://guelph.ca/opendata). Since we were given data like bus routes, heritage site locations, parks, bicycle paths and elevation / topographic data our motivation was “build you own adventure” to discover the city of Guelph. The easiest data to work with was the park data since it was csv and followed a regular easy to read format. There were also some data sets which used XML. This was also quite easy to handle in PHP because there are built-in functions to parse XML. The tricky data was ESRI shapefiles. There were a few PHP libraries available to read these types of files, but given the time constraints (24 hours) of the hackathon, we didn’t end up getting to use this data. In the end, we ended up getting the bus data sets, and the park data sets working.
You can try our app here: [myCity Guelph Adventure Game](https://web.archive.org/web/20140512053343/http://lab.jasonernst.com/final/loginPage.php).
The second was the “CODE ([Canadian Open Data Experience](https://web.archive.org/web/20170804025913/http://open.canada.ca/en/code-2014-event))” competition by the Government of Canada. This competition had much more data, and the sets were very large compared to the Guelph data. For instance, one of the sets for labour force data was 65 megabytes and had over 500,000 lines of records to parse. For this app, we decided given the datasets we had available, we would create an app for immigrants to help them find a good city to live in. It is well known in Canada that the typical choice is Toronto, however maybe there are places where people may have a better fit given certain preferences. We used criteria like climate, tolerance for crime, housing prices, desired income level, type of job industry the person is looking for, and whether they were part of a particular visible minority to determine which city may be best for them. Here’s a quick demo video of the "[newRoots app](http://www.youtube.com/watch?v=HJg3ee-JVG8)". You can try the app yourself at the [newRoots app](https://web.archive.org/web/20140512053343/http://lab.jasonernst.com/code/) page.
You can also follow our team’s twitter account – @2electricsheep