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
A guide to setting up the Python scientific stack, well-suited for geospatial analysis, on a Raspberry Pi 3. The whole process takes just a few minutes.
The Raspberry Pi 3 was announced two weeks ago and presents a substantial step up in computational power over its predecessors. It can serve as a functional Wi-Fi connected Linux desktop computer, albeit underpowered. However it’s perfectly capable of running the Python scientific computing stack including Jupyter, pandas, matplotlib, scipy, scikit-learn, and OSMnx.
Despite (or because of?) its low power, it’s ideal for low-overhead and repetitive tasks that researchers and engineers often face, including geocoding, web scraping, scheduled API calls, or recurring statistical or spatial analyses (with small-ish data sets). It’s also a great way to set up a simple server or experiment with Linux. This guide is aimed at newcomers to the world of Raspberry Pi and Linux, but who have an interest in setting up a Python environment on these $35 credit card sized computers. We’ll run through everything you need to do to get started (if your Pi is already up and running, skip steps 1 and 2). Continue reading Scientific Python for Raspberry Pi
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
Which U.S. cities are the most expensive for rental housing? Where are rents rising the fastest? The American Community Survey (ACS) recently released its latest batch of 1-year data and I analyzed, mapped, and visualized it. My methodology is below, and my code and data are in this GitHub repo.
This interactive map shows median rents across the U.S. for every metro/micropolitan area. Click any one for details on population, rent, and change over time. Click “switch” to re-draw the map to visualize how median rents have risen since 2010:
Continue reading The Landscape of U.S. Rents
I like to do my data wrangling and analysis work in Python, using the pandas library. I also use Python for much of my data visualization and simple mapping. But for interactive web maps, I usually use Leaflet. There isn’t dead-simple way to dump a pandas DataFrame with geographic data to something you can load with Leaflet. You could use GeoPandas to convert your DataFrame then dump it to GeoJSON, but that isn’t a very lightweight solution.
So, I wrote a simple reusable function to export any pandas DataFrame to GeoJSON:
Continue reading Exporting Python Data to GeoJSON
I am presenting at the 2015 Conference on Complex Systems tomorrow in Tempe, Arizona. My paper is on methods for assessing the complexity of urban design. If you’re attending the conference, come on by!
Here’s the abstract:
Continue reading Urban Design and Complexity