Playing with fire data.
The other day I saw a link to NASA active fire data fly by on Twitter. It’s a satellite-derived world wide dataset at 375m resolution, where one (or several) polar orbiting satellites scan earth in the infrared band from which fire and fire intensity is computed. Redding, CA With the Redding fire in the news I decided to take the data for a test drive. And also try out the gganimate package to watch the fire evolve over time.
Neigbourhood typologies within Metro Vancouver.
Some time ago I saw Geoff Boeing’s excellent package to generate Jane Jacobs style street grid images. It’s lots of fun to compare different cities that way. It can be hard to represent one city by one square mile, so I thought it would be neat to use this to compare different parts of Vancouver. Some common themes emerge for the central parts, the more outlying areas display very differnet patterns.
Exploring Isochrones.
Mapzen again upped their game by publishing their Mobility API. This is super exciting for anyone interested in a whole range of mobility questions. A question I have seen is how to adapt that to specific needs. So here is a quick example how to customize walksheds. All we do is set up a quick map that computes the 5 and 10 minute walksheds when the user clicks on the map.
Bike share stats for all.
I keep getting questions about Mobi stats these days. Rather than ansering them one by one I decided to just offer a live view into data generated by our shadow API. I made two simple views, the most recent month of daily bike checkout counts and the most recent week of hourly bike checkout counts. The data issues mentioned in our previous post still apply. For data geeks, here is a link to a very useful paper that compared estimates like I make to real usage data.
Scraping Mobi data.
Vancouver finally has a bikeshare system. And everyone is hoping it will succeed, despite the obstacles BC’s mandatory helmet law poses for the system. So we are eager to find out how things are going with Mobi. To set the background, consider that Seattle’s Pronto is getting less than 1 ride per bike per day after half a year in operation. In comparison, bike shares that are considered ‘successful’ in North America get 3 to 5 rides per bike per day.
Too much data, too little time.
Mountain Doodles is an insomnia driven MountainMath side project, born out of random questions, trying to give partial answers through data.