I recently wrote about visualizing my Foursquare check-in history and it inspired me to map my entire Google location history data – about 1.2 million GPS coordinates from my Android phone between 2012 and 2016. I used Python and its pandas, matplotlib, and basemap libraries. The Python code is available in this notebook in this GitHub repo, and it’s simple to re-use to visualize your own location history.
Just download your JSON file from Google then run the code. First I load the JSON file and parse the latitude, longitude, and timestamp with pandas. Then I map my worldwide data set:
Continue reading Mapping Your Google Location History with Python
Last.fm is a web site that tracks your music listening history across devices (computer, phone, iPod, etc) and services (Spotify, iTunes, Google Play, etc). I’ve been using Last.fm for nearly 10 years now, and my tracked listening history goes back even further when you consider all my pre-existing iTunes play counts that I scrobbled (ie, submitted to my Last.fm database) when I joined Last.fm.
Using Python, pandas, matplotlib, and leaflet, I downloaded my listening history from Last.fm’s API, analyzed and visualized the data, downloaded full artist details from the Musicbrainz API, then geocoded and mapped all the artists I’ve played. All of my code used to do this is available in this GitHub repo, and is easy to re-purpose for exploring your own Last.fm history. All you need is an API key.
First I visualized my most-played artists, above. Across the dataset, I have 279,769 scrobbles (aka, song plays). I’ve listened to 26,761 different artists and 66,377 different songs across 38,026 different albums from when I first started using iTunes circa 2005 through the present day. This includes pretty close to every song I’ve played on anything other than vinyl during that time. Continue reading Analyzing Last.fm Listening History
I started using Foursquare at the end of 2012 and kept with it even after it became the pointless muck that is Swarm. Since I’ve now got 4 years of location history (ie, check-ins) data, I decided to visualize and map it with Python, matplotlib, and basemap. The code is available in this GitHub repo. It’s easy to re-purpose to visualize your own check-in history: you just need to plug in your Foursquare OAuth token then run the notebook.
First the notebook downloads all my check-ins from the Foursquare API. Then I mapped all of them, using matplotlib basemap.
Continue reading Visualize Foursquare Location History
Google Takeout lets you download an archive of your data from various Google products. I downloaded my Gmail archive as an mbox file and visualized all of my personal Gmail account traffic since signing up back in July 2004. This analysis excludes work and school email traffic (as well as my other Gmail account for signing up for web sites and services), as I have separate dedicated email accounts for each. It also excludes the Hangouts/chats that Google includes in your mbox archive. So, this analysis just covers personal communication.
This also demonstrates working with time series in Python and pandas. All of my code is on GitHub as an IPython notebook. You can re-purpose it for your own inbox – just download your Gmail archive then run my code.
Continue reading Visualizing a Gmail Inbox
Also check out this follow-up analysis of stadium attendance.
The 2016 college football championship game between Clemson and Alabama was held at University of Phoenix Stadium, where the NFL’s Arizona Cardinals play. Interestingly, this NFL (ironic, given its name) stadium is considerably smaller than the home stadiums of either Clemson or Alabama. In fact every NFL stadium is considerably smaller than the largest college stadiums. Outside of North Korea, the 8 largest stadiums in the world are college football stadiums, and the 15 largest college football stadiums are larger than any NFL stadium.
Americans are obsessed with college football, but how much is too much? Today most athletic departments are subsidized by their schools. Public universities increased their annual football spending by $1.8 billion between 2009-2013 while racking up huge debts to finance stadiums with little chance of profit. This interactive map shows each NCAA Division I college football team’s home stadium: collectively they seat 8.5 million people. Click any point for details about stadium capacity and year built:
Continue reading America’s College Football Stadiums
The U.N. world population prospects data set depicts the U.N.’s projections for every country’s population, decade by decade through 2100. The 2015 revision was recently released, and I analyzed, visualized, and mapped the data (methodology and code described below).
The world population is expected to grow from about 7.3 billion people today to 11.2 billion in 2100. While the populations of Eastern Europe, Taiwan, and Japan are projected to decline significantly over the 21st century, the U.N. projects Africa’s population to grow by an incredible 3.2 billion people. This map depicts each country’s projected percentage change in population from 2015 to 2100:
Continue reading World Population Projections
The fall semester begins next week at UC Berkeley. For the third year in a row, Paul Waddell and I will be teaching CP255: Urban Informatics and Visualization.
This masters-level course trains students to analyze urban data, develop indicators, conduct spatial analyses, create data visualizations, and build interactive web maps. To do this, we use the Python programming language, open source analysis and visualization tools, and public data.
This course is designed to provide future city planners with a toolkit of technical skills for quantitative problem solving. We don’t require any prior programming experience – we teach this from the ground up – but we do expect prior knowledge of basic statistics and GIS.
Our teaching materials, including IPython Notebooks, tutorials, and guides are available in this GitHub repo, updated as the semester progresses.
Continue reading Urban Informatics and Visualization at UC Berkeley
How big is Greenland? It’s huge, right? At 836,109 square miles in size, Greenland is the largest island and the 12th largest country on Earth. With only 56,000 people living in that enormous area (80% of which is covered by the world’s only extant ice sheet outside of Antarctica), it is also the least densely populated country on Earth.
You can get a sense of how large Greenland is when you look at a map of the world:
It’s huge! Greenland is bigger than the entire continent of Africa! Or is it? The map above uses the common Mercator projection to project the 3-D surface of the Earth onto a 2-D surface suitable for a paper map or an image on your computer screen. But it’s not easy to project the curved surface of a sphere onto a rectangular plane. Compromises must be made. In the case of the Mercator projection, the compromise is that objects’ sizes become increasingly distorted the further they are from the equator. At the poles, the scale and distortion become infinite.
Continue reading Map Projections That Lie
Download/cite the paper here!
In a previous post, I discussed chaos, fractals, and strange attractors. I also showed how to visualize them with static 3-D plots. Here, I’ll demonstrate how to create these animated visualizations using Python and matplotlib. All of my source code is available in this GitHub repo. By the end, we’ll produce animated data visualizations like this, in pure Python:
Continue reading Animated 3-D Plots in Python
Download/cite the paper here!
In a previous post, I discussed chaos theory, fractals, and strange attractors – and their implications for knowledge and prediction of systems. I also briefly touched on how phase diagrams (or Poincaré plots) can help us visualize system attractors and differentiate chaotic behavior from true randomness.
In this post (adapted from this paper), I provide more detail on constructing and interpreting phase diagrams. These methods are particularly useful for discovering deterministic chaos in otherwise random-appearing time series data, as they visualize strange attractors. I’m using Python for all of these visualizations and the source code is available in this GitHub repo.
Continue reading Visualizing Chaos and Randomness