Here’s a little project I’ve been working on: All of the world’s reservoirs, as seen by the GRanD database. I think it still needs a few tweaks — let me know if anything looks off.
Reservoir capacity is proportional to the size of each dot. Note that capacity is measured as the extra capacity created by a manmade dam. For instance, Lake Victoria in Africa is here because there is a dam on it — but the “capacity” includes only the few feet at the top of the reservoir that can be controlled by the dam.
I’m going to Turkey next month, and interested in the various water challenges facing the country.
Using the GRanD databse, and infogr.am, I made this chart to compare the construction of reservoirs in Turkey versus the U.S.
As the book about my trip is about to be released, I decided to make an interactive map of the trip. Below are some points of interest along my two year bicycle journey — click on the markers and read about parts of the journey.
Working with EcoWest.org, I helped put together this interactive below that shows how the Yosemite Rim Fire has grown over the past few weeks. The Rim Fire is now as large as San Francisco, Oakland, and San Jose put together (about 200 square miles).
Visit EcoWest.org to read Mitch’s great post that puts this fire in perspective.
I’m currently experimenting with Datawrapper as a platform for publishing charts. I’ve downloaded the fire data from Ecowest.org, and copied and pasted it. Datawrapper’s goal is to essentially be an open source version of ManyEyes.
Below is the number of fires bigger than 100,000 acres for every year since 1998. I highlighted the years with 10 or more fires over 100,000 acres.
Also, the total number, area burned, and average size of fires since 1985. You can see that the number of large fires is increasing!
I recently re-edited my film Ten Tips for Biking Eastern Europe to submit to the Banff Film Festival (yes, a reach, but why not?). I had to alter the film because the old version uses Google Earth to show our bike route, and this is, of course, copyrighted. So I had to make a non-copyrighted map.
To make a new map, I took the KML file of my bike route (which I produced by combining all of the GPS files from the route), and using the Python library matplotlib and the toolkit extension basemap, plotted the route on NASA’s “blue marble” image of the earth, which is an image of the entire earth created by combining cloud-free satellite images. According to my sources, I can use this NASA image as my map background as long as I attribute it. (Note: the image used in the map background below is brought to you by NASA).
Pretty, eh? I wish the blue marble background image were a bit higher resolution, but I think it looks good enough.
This took a long time, not because the coding was difficult (I borrowed code from here and here), but because it took forever to get the Python libraries installed correctly. And, when I did get the libraries installed, the blue marble background image, for some reason, showed up backwards. To right everything, I had to change the “backend” rendering of the matplotlib library by installing WxAgg (As with most Python library challenges, I just followed advice from the Internet until I got it to work).
A few months ago, we had a housewarming party and about 30 guests, mostly people roughly my age living in San Francisco, attended. I put a map of the world on the wall, and we asked people to mark each country that they had visited. I then entered the data into excel, used Google Charts Tools Geomap to make the following map of where people I know, who showed up to a party in San Francisco, have traveled.
I think it is absolutely fascinating that not one of the 30 partygoers had been to many places in central Africa, most of the Middle East, or a string of Eastern European countries stretching from the Baltic Sea to the Black Sea. Also, it’s interesting that only one person at the party has visited Russia, and that my friends were just as likely to have visited France and the UK as they were to have visited Mexico.
There’s a lot to say here. I will be revisiting this map.
I recently combined all of the GPS files from our ~1000-mile 26-day bike trip this past summer and uploaded them into Google Earth. The result is a path that you can “fly over” and follow. Google Earth also allowed me to plot an elevation profile of the trip.
Unsurprisingly, our favorite parts are almost exactly correlated with the hilliest sections–the Tatras mountains of Slovakia at the beginning of our journey, and the many mountains of Bosnia at the end.
You can download the KML file from our trip here and plot it yourself on Google Earth. I took a screen capture video of the route, which you can see below.
California is, of course, number one.
Moocirclepack only lets you put an image on each circle, and not text, so I had to use the Python pil library (which was a major pain to install) to turn the state abbreviations into images.
The most interesting thing I saw on this graphic is Puerto Rico (PR). Puerto Rico, with a population of 3.7 million, is more populous than 21 other states. It’s almost strange that it isn’t a state (based on population alone).
I do have some things I don’t like about this visualization. The spatial placement of the circles doesn’t mean anything (Oregon is next to Florida). Secondly, our eyes are not very good at comparing area. For instance, it’s hard to tell, by looking at this graphic, that California has a population 10 times that of Puerto Rico (38 million versus 3.7 million).
This following graphic comes courtesy of WRI, which shows where new coal power plants have been proposed. According to a new report by WRI, there are 1,199 planned around the globe, more than half of which will be built in India and China.