So it's been a little while since I posted. From my last post: "...but hopefully I'm back into my apartment soon and I'll have more time to spend with the site." This did not turn out to be true. Explanations for my absence in photographic form after the jump.
Inspired by Garrett Miller's (relatively) recent blog post, Mapping Moves, I decided to do something else with all my NikePlus data and create a map of my runs. Unfortunately for me, the Nike data is more difficult to liberate than from other sources. Fortunately for me, and anyone else trying to do this, there are some pre-existing tools to solve this issue.
Like many pedestrian city-goers, I think about route efficiency a lot: when to cross, which streets are the least crowded, when to take diagonals, etc. I also happen to frequent a bar that is somewhat of an edge case when I try to decide on which route to take. For the first time, I'm going to apply a little rigor to this problem [1].
I've made some more updates to my KenPom visualization and used it to capture a few observations (perhaps very obvious ones) from the last year in college basketball.
I'm taking a quick break from my KenPom data visualization work this week (though I have made some updates; work in progress on the projects page) to talk about another project I've been working on. I'm an avid runner and have an upcoming race where I'm a little worried about the elevation changes so I thought bringing in some programming could help.
Though I've put up a few smaller exercises before, I finally have a draft version of the first full data visualization that I've done the majority of the work on [1]. It's based on a metric I created last week using kenpom data.
This week, I've been spending my free time trying to read everything that Mike Bostock, creator of the JavaScript visualization library D3.js, has ever written.