It’s National Beer Day this Saturday, April 7th! I’m celebrating by posting this Bicycle to Breweries Map of Bellingham, my home city.
National Beer Day is a great excuse to share this map, but I should probably give at least a brief description of the map.
The map shows the location of the city’s breweries and bike shops nearby. I made it using the ArcGIS Maps for Adobe Creative Cloud extension, and its entire dataset comes from the City of Bellingham’s open data. The streets marked with a red dash are all part of Bellingham’s Pedestrian Master Plan, which is an ambitious infrastructure program to improve the City’s walkability.
Bellingham is already wonderful place to walk and bike, rain or shine. The city has great trails, great breweries, and a lot of cyclists, walkers, and runners. With late night sunsets approaching, the season for riding bikes to neighborhood breweries is coming soon. I started this map as a tutorial to show fellow mappers how to make a map using this particular Adobe Illustrator extension, made by Esri (I’m on that team!). One of the features in the extension’s latest updates is the ability to pick from standard layout sizes for print, mobile, and other common media formats. This map uses the 11×17-inch tabloid format.
I am going to file this map into the permanently unfinished category. However, it will certainly help you find the Bellingham breweries, most bike shops, park-and-rides, all traffic lights and trails that were in existence during the time the data was created. It also was a fun exercise in creating a Victorian-esque Adobe Illustrator symbol set.
If you visit ESPN’s website, you can find the team rosters for all the NCAA Basketball teams that made it to the NCAA tournament. On that site, ESPN lists the players’ number, position, year, and hometown, among a few other statistics. A couple of my colleagues, Nick Brueggemann and Gregory Brunner, did some mapping with this data. I asked them if I could take their dataset and crunch it a little further. There is a ton more that could be done with this information. And maybe I will someday, but here’s what I have done for now to spatially visualize the NCAA D1 Basketball Tournament team roster data:
I decided to map the women and men athletes’ hometowns as graduated symbols, where hometown sizes are based upon the amount of athletes that come from there. The men and women hometown maps are separated in order to more easily identify the different locations. These maps started out with world city points and labels, but I removed them for the sake of decluttering the visualization. I was able to identify the lat long of the athletes’ hometowns using ArcGIS. I then calculated the distance from each player’s hometown to the city that their team is located; here is a really handy blog post that someone wrote eleven years ago; I often refer to that post when calculating distances between lat long coordinates. Calculating the distance of each athlete’s hometown to their team city allowed me to identify the average recruit distance per team, along with the farthest (and closest) recruit distance. Other than the maps themselves, I haven’t done any visualization for teams on the lower end of the average recruit distance. (Maybe in few days ?) There are a lot of players whose hometown is also the the same as their team’s city.
*Check out our What’s Your Vote Worth? map to see this effect.
Tired of falling down into web maps? Zooming, zooming, and zooming in from outer space all the way down to the Earth’s surface got you feeling sad?
Here’s another idea: demand that geographic features come up to you. Web maps typically require you to zoom in and may give the sensation of “falling down” or “diving in” towards the Earth’s surface. Why not make this everyday interaction become more user-focused?
Just click—no need to fly down closer to the Earth—and the visualization will do the heavy lifting of bringing up the geographical feature directly to you.
Check out the code and live demo for this D3 block.