writes code and visualizes data
in Montréal, Québec, Canada.
I have recently read some though-provoking articles that discussed data visualization by analogy to photography. I really like this analogy, both from a process perspective – photography and data visualization – and a people perspective – photographers and data visualizers. Anyone who takes a picture with a camera is a photographer in that moment, and anyone who makes a chart, diagram or map based on data is a data visualizer while they’re doing that. Both photographers and data visualizers produce images of information emanating from their subjects, to make a point, to record, to inform, to delight. Photographers choose the lighting of their subject and framing of their shots, then use cameras to capture their image. Data visualizers choose the data they use about their subject and the mapping of data attributes to visual attributes, then use algorithms to produce graphics. Both can post-process their images to exert even finer control over their products.
Last November 5th was Municipal Election Day in Montreal and I’m proud to say I was one of the hundreds of volunteers who got out the vote to elect Valérie Plante as Montreal’s first female mayor and the leader of Projet Montréal. However unlike most volunteers who were making phone calls, going door to door or driving electors to polling stations, I was at the campaign headquarters in front of my computer writing SQL queries and interpreting data from a real-time web dashboard I’d built the week before. In this post I’ll explain some of what I learned through this experience about get-out-the-vote (GOTV) efforts, and a bit more about the small role I played.
Many people reacted to my my interactive map of Montreal election results with requests for tables of hard numbers, and I’m happy to oblige! I grabbed the official election results from the Montreal open data portal and aggregated them by district to produce an easy-to-use CSV file. I also created a page that preloads a PivotTable.js instance with the data, for interactive data exploration fun!
The 2017 edition of my interactive map of Montreal election results is now available, and I’m so pleased about the results it shows! In 2013 I made a map a couple of months after the election and it was considered so unusual it was talked about on the radio. But times have changed: this time the data was available within days, and within hours of that, news outlets had similar maps on their websites. I still like mine better though because it shows data from all 103 races, rather than just the mayoralty. The 2013 map is still around, for reference.
My friend Mark Weiss recently started a podcast called Using Reflection and I was pleased to be interviewed as a guest on his 6th episode. We had a great chat about datavis and engineering ethics, among other topics.
In an agile software development project, the role of the product owner comes with the responsibily of managing the product backlog. Most popular definitions of the backlog are quite broad, encouraging product owners to include in it every feature request, bugfix, idea related to the product etc. I have found it more helpful, however, to distinguish between backlog items on the one hand (i.e. changes that as a product owner I intend to be made to the product) and feedback items on the other (i.e. bug reports, feature requests, ideas etc.)
I recently did a guest lecture (in French!) at the Université de Montréal in the context of the École d’été en Architecture de l’information (Summer program for Information Architecture).
As part of my second collaboration with data journalist Roberto Rocha, I made an interactive map for his recent piece on where and when Car2Go vehicles park in Montreal (shorter english version). Earlier in the year, Roberto told me about people in certain neighbourhoods complaining about Car2Go vehicles causing parking problems. He and I hit upon the idea of querying Car2Go’s API every few minutes to find out where all their available cars were parked in Montreal, to take a look at some real data on this issue. I’m a huge fan and user of car-sharing services and in my neighbourhood of Rosemont I feel they prevent parking problems by enabling lower car ownership. As my map makes clear, however, this is not the case in areas like the Mile End. In any case, the CBC articles do a great job of reporting on the situation, and I wanted to share some of the thinking and code that went into making the map.