builds data visualization tools
at Plotly in Montreal, Canada.
I recently did a guest talk at the Arup Montreal office regarding the differences between Software Product Organizations and Professional Services Organizations.
Data visualization uses algorithms to create images from data so humans can understand and respond to that data more effectively. Artificial intelligence development is the quest for algorithms that can “understand” and respond to data the same was as a human can – or better. It might be tempting to think that the relationship between the two is that to the extent that AI development succeeds, datavis will become irrelevant. After all, will we need a speedometer to visualize how fast a car is going when it’s driving itself? Perhaps in some distant future, it might be the case that we delegate so much to AI systems that we lose the desire to understand the world for ourselves, but we are far from that dystopia today. As it stands, despite the name, AI development is still very much a human endeavour and AI developers make heavy use of data visualization, and on the other hand, AI techniques have the potential to transform how data visualization is done.
One of the easiest ways to start visualizing data is to turn a table into a heatmap: every cell gets a colour, the higher the number the brighter the colour. Unfortunately, this is often a fairly unrewarding exercise, yielding graphics that look like plaid or tartan fabric. Part of the problem is that the rows and columns of a dataset often have no natural ordering, such as time, and are instead shown in alphabetical order, or else the dataset is sorted by one of the rows or columns, rather than in an order which makes patterns pop out visually. My goal in this article is to clearly demonstrate this problem and show that there exist neat solutions to this problem using a set of techniques collectively called seriation. I’ll do this by automatically reordering the rows and columns in the following noisy-looking heatmap to make the underlying pattern very clear.
Many a bored long-haul flight passenger has asked themselves why the flight path on the map is curved, and if it wouldn’t be faster to just fly straight there. In fact, airlines try very hard to keep their flight paths as straight as possible. It’s just that the rectangular world maps we are accustomed to looking at project the 3-dimensional earth onto a 2-dimensional surface such that any long straight line not directly along the equator or perpendicular to it will appear curved. Making the equator special in this way makes some sense as a default way to draw maps, because of the way the earth spins on its axis, but we can just as easily choose any other straight line path for this treatment, and doing so gives us an interesting perspective on the world and on maps.
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.