Tag Archives: location
In honor of today being the last day of the existence of Moves, the app from which so many Quantified Self projects drew their location data, I thought I’d post this artwork by Sabastian Meier and Katrin Glinka, who constructed city models based on connecting Moves data with their memories.
For discussion of the demise of Moves, exporting, and alternatives, see this topic in the QS Forum: Moves shutting down? Oh, no! But note, today is the last day.
We are happy to welcome this guest post on a community tool by Bastian Greshake Tzovaras. Bastian is the director of research at the Open Humans project. He can be found online at @gedankenstuecke. -Steven
I’ve built a Twitter analysis web application that’s open to everyone to use and learn from. Often the best data for learning something about yourself are data you’ve already collected; sometimes without even being explicitly aware of collecting it. Social media activity, for example. We often send off Facebook posts or tweets with very little thought about the metadata that we generate in doing so. Where was I when I made that post? What time was it? What type of content did it contain? Did I retweet or reply to another person’s post? And, of course, what did my post contain?
This data can be extremely powerful – for others. The language you use in your Tweets can be used to predict your age as well as your income. Twitter uses the data to gather information about your likes, dislikes, and possessions – among other topics. But what if you want to learn about yourself with your own Twitter data?
The tool I created allows anybody to explore their own Twitter archive in detail. First, you’ll want to request your archive from Twitter. It will contain all the tweets you have ever sent, with not only the text but all the metadata as well. To look at these metadata, go to my small web application called TwArχiv (pronounced tw-archive), which allows you to upload your data and explore it using interactive graphs.
For instance, you can see how the nature of the tweets you send change over time. Are you replying more to people than you used to or is it all just retweets by now? For my own data it seems that finishing up my PhD work had quite an impact, starting in late 2016. With less procrastination I wrote fewer unprompted tweets. Instead, replying to people became more central to my Twitter experience.
There is also plenty of research on gender bias in social media usage and whose voices are being amplified, with men being overwhelmingly favored. TwArχiv allows one to do some soul searching on this. It tries to predict the gender of the people you interact with based on their first names and shows you whether your reply and retweet behaviour is gender-balanced.
My own graphs show that I had (and have) a good way to go here. Especially 2010 is wildly off when it comes to the gender representation in my Twitter interactions. What happened during that time? I was politically active in the German Pirate Party, which was infamous for being a “boys club”.
If you have geolocation enabled on your tweets, you can get an idea of where you tweet. With a fully zoomable map, TwArχiv allows you to explore the globe on all scales to see the broader picture as well as street-level tweet distributions. As a first attempt of seeing movement patterns, you can also get a time-stamped version of the map that highlights locations one tweet at a time.
If you want to give a try with your own archive, you can head to TwArχiv.org. The data storage is handled by Open Humans and by default your archive and the resulting visualizations will be private. (You can choose to make them public, though, to share them with your friends and followers – mine are here!).
A note: The Twitter archive does not contain any direct messages but only your tweets, so if you have a public Twitter account the archive is basically all your “public Twitter interactions”.
If you have ideas on how to extend the functionality of TwArχiv or you want to code your own Twitter archive analysis, you could even get funding to do so: The Open Humans’ mini-grants of USD 5,000 for projects that will enrich the Open Humans ecosystem are a perfect fit for this kind of data visualization and analysis.
We hope you enjoy this week’s selection of links, show&tell posts, and visualizations!
Hacking Your Brain by The Economist. Increasing performance and cognitive functioning, reducing depression, improving memory – if you could use a simple tool to get all these done, would you? What if that device was delivering electrical current to your brain? That’s the promise of transcranial direct current stimulation.
Talking Next-Gen Diabetes Tools with Dexcom Leaders by Mike Hoskins. Wonderful interview here with Terry Gregg (chairman) and Kevin Sayer (CEO) of Dexcom. Particular focus is given to their reaction and ideas regarding the open source Nightscout project.
Scientists threatened by demands to share data by Victoria Schelsinger. An older article (2013) about the shift towards open data and data sharing in academic science and it’s potential impact and possible pitfalls.
”’I think the public thinks that we’re all learning from everyone else’s work. That’s not true, and furthermore, it’s not true in ways that are even worse than you might think.’” – Heather Piwowar
Changing Representation of Self-Tracking by Deborah Lupton. It’s always great to hear that Deborah has released new writing. Her thoughtful analysis about self-tracking, data as culture, and data as object is consistently fantastic. Great addition to her growing body of work here.
Why Pets Are the Future of Fitness Wearables by Annie Lowrey. An interesting take on how the rise of tracking tools for pets may impact pet owners. Reminds me of research conducted by my old colleagues at San Diego State University: Physical activity, weight status, and neighborhood characteristics of dog walkers (Spoiler: Having a dog is associated with being more physically avtive.)
This guy is the Mark Zuckerberg of open-source genetics by Daniela Hernandez. A few weeks ago we highlighted an article by Daniela that focused on the fantastic openSNP project. She’s back with a profile of one of the founders, Bastian Greshake. (Full disclosure: I am openSNP member #610.)
Personal Sleep Monitors: Do They Work? by Christopher Winter. Superb experiment here to try and understand the accuracy of different sleep trackers.
What I’ve learned after 10 years of quantifying myself by Maxim Kotin. The title says is all.
A History of Checkins: Facebook Checkin Stats by Octavian Logigan. Octavian breaks down three years of his location checkin history and describes what he learned through examining seasonal trends, category breakdowns, and travel patterns.
I love the sleep tracker, so I can quantify this kind of information! (I have a 2yo and a 5yo….) by reddit user EclecticBlue. Fun visualization here of Fitbit sleep data. Also, great comments in the thread.
Locals & Tourists by Mapbox & Eric Fischer. I could spend hours exploring this interactive map of tweet locations by “tourists” and “locals”. (Special thanks to Beau Gunderson for point out that Eric also did a similar project with geotagged Flickr photos)
The Impact of Weather on Human Activity by Paul Veugen. The team at Human “1.9M activities in Boston & NYC to see the impact of weather on Human activity.” Make sure to click through for the full visualization.
FCC & FDA moving connected health forward by establishing wireless medical test beds
Nike+ Running Expand Global Partnerships
Will Our Fitness Data Be Used Against Us?
As the “quantified self” industry explodes, who will control the data — us or them?
This Week on QuantifiedSelf.com
Gordon Bell: Every Beat of My Heart
QS15 Conference Preview: Stephen Cartwright on 17 Years of Location Tracking
What’s in My Gut
On June 18-20 we’ll be hosting the QS15 Conference & Expo in San Francisco at the beautiful facilities at the Fort Mason Center. This will be a very special year with two days of inspiring talks, demos, and discussion with your fellow self-trackers and toolmakers, plus a third day dedicated to the Activate public expo. As we start to fill out our program we’ll be highlighting speakers, discussion leaders, sponsors, and attendees here.
Stephen Cartwright has been attending the QS Conferences since 2012, where he first spoke about his ambitious geolocation tracking project. As an associate professor at the School of Art and Design at the University of Illinois at Urbana-Champaign, where he teaches sculpture, digital fabrication, and furniture design, Stephen brings an interesting and welcomed point of view and set of experiences to our show&tell program.
At the QS15 Conference he will be sharing his process and what he’s learned from tracking his location every hour using a GPS for the last 17 years. He will describe how his practice has changed and adapted to new technologies over the years, including how active versus passive tracking techniques have impacted this project.
My tracking informs my life and especially my art, so I will consider my tracking through the lens of my 3D data visualization sculpture. The artistic aspect of my work allows the data visualization to become more than informative graphs, they become new landscapes of data.
We’re excited to have Stephen joining us and asked him a few questions about himself and what he’s looking forward to at the conference.
QS: What is your favorite self-tracking tool (device, service, app, etc)?
Stephen: This is a difficult question, I use different tools for different stages of my work. My practice would be nowhere without a GPS. It took me a long time to replace my Garmin stand-alone GPS but I now use the MotionX GPS app for my iPhone. My requirements for these apps/devices is that the waypoints have to be saved with the date and time attached.
QS: What are you most looking forward to at the conference?
Stephen: The conference is a great place to be among like-minded people and share ideas and inspiration. Although all the attendees have a lot in common everyone comes to self-tracking from a different angle and seeks different outcomes. I love to see how similar practices result in improvements in performance and health, self-help, and even art.
QS: What should people come talk to you about at the conference?
Stephen: Come talk to me about the intersection of art and science, data-visualization, and GPS/location tracking.
QS: What tools, devices, or apps do you want to see at the conference?
Stephen: I am looking for the best smart phone based step and movement tracker.
QS: What topic do you think that Quantified Self community is not talking enough about?
Stephen: I would like to hear more about the relationship between individual trackers and larger data studies. How well do we know ourselves as compared to what can be inferred about us by our data footprint or studies of people in similar circumstances?
Stephen’s session is just one of the many hands-on, up-to-date, expertly moderated sessions we’re planning for the QS15 Global Conference and Exposition.
Bonus Video of Stephen’s Data:
New sensors are peeking into previously invisible or hard to understand human behaviors and information. This has led to many researchers and organizations developing an interest in exploring and learning from the increasing amount of personal self-tracking data being produced by self-trackers. Even though individuals are producing more and more personal data that could possibly provide insights into health and wellness, access to that data remains a hurdle. Over the last few years a few different projects, companies, and research studies have launched to tackle this data access issue. As an introduction to this area, we’ve put together a short list of three interesting projects that involve donating personal data for broader use.
Developed and administed by the WikiLife foundation, the DataDonors platform allows individuals to upload and donate various forms of self-report and Quantified Self data. Data is currently available to the public at no cost in an aggregated format (JSON/CSV). Data types includes physical activity, diet, sleep, mood, and many others.
OpenSNP is an online community of over 1600 individuals who’ve chosen to upload and publicly share their direct-to-consumer genetic testing results ( 23andMe, deCODEme or FamilyTreeDNA) . Genotype and phenotype data is freely available to the public.
Open Paths is an Android and iOS geolocation data collection tool developed by the New York Times R&D Lab. It periodically collects, transmits, and stores your geolocation in a secure database. The data is available to users via an API and data export functions. Additionally, users can grant access to their data to researchers who have submitted projects.
We’ll be expanding this list in the coming weeks with additional companies, projects, and research studies that involve personal self-tracking data donation. If you have one to share comment here or get in touch.
Jamie Aspinall was interested in what his location history could tell him. As a Google Location user, his smartphone is constantly pinging his GPS and sending that data back to his Google profile. Using Google Takeout Jamie was able to download the last four years of his location history, which represented about 600,000 data points. In this talk, presented at the London QS meetup group, Jamie describes his process of using a variety of visualizations and analysis techniques to learn about where he goes, what causes differences in his commute times, and other interesting patterns hidden in location data.
You can also view his presentation here.
Today’s gallery image comes to us from Eric Jain. Eric is the creator of Zenobase a neat data aggregation and tracking system. He’s also been a great contributor to our community at meetups in Seattle, our conferences, and on the forum.
This map shows my outdoor trips in the Pacific Northwest since 2008. Red is driving, yellow is hiking or paddling. The map doesn’t just help me remember past trips, but also helps me decide what areas to explore next. The tracklogs were recorded with a Garmin GPS device, processed with a simple script and uploaded to Google Fusion Tables with additional meta data stored for each trip in my Zenobase account.
This entry comes to us from Bob Troia. Bob runs the excellent Quantified Bob blog where he explores self-tracking and experimentation. Make sure to check out this post where he explains how he created this great visualization of his movement data.
Here’s a cool visualization of approximately 1 month of my location data in and around New York City using Moves and a Processing sketch Nicholas Felton put together. Yellow lines are walking (you’ll see the hot spots where I walk my dog or around my office, blue are cycling (usually to/from the soccer field), and gray are subways/car/taxi. Pretty neat! It shows that I am very much a creature of habit (or I walk the same routes all the time to conserve willpower!
We invite you to take part in this project as we share our favorite personal data visualizations.If you’ve learned something that you are willing to share from seeing your own data in a chart or a graph, please send it along.
How well do you know your city? Your neighborhood? The patterns of our daily lives typically dictate what we see and experience in our local environments. We travel the same roads, go to the same shopping centers, see the same sights. What would happen if we made a conscious effort to experience the entirety of our community? Alastair Tse was a recent transplant to New York City and decided he would attempt to do just that by walking all of Manhattan. In this great update to his previous talk, filmed at the New York QS meetup group, Alastair explains his motivation and how he’s developed his own application to help him track every street he travels while walking. Make sure to check out his wonderful explanation of this project and method for exploring his data on his personal website.
Cristian Monterroza felt like his life was slipping in a direction that he didn’t like, and was inspired to start tracking by the amazing lifelogging project of artist On Kawara. Cristian started out using several different apps, then created his own app to passively record his daily activities, called wrkstrm. In the video below, Cristian shares the insights he gained from six months of building a self-tracking autobiography, and asks us to consider if we are recording the right things. (Filmed by the New York QS meetup group.)