Tag Archives: location
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.)
Alastair Tse recently moved to New York, and wanted to walk all of the streets of Manhattan! He tried a few different approaches to tracking this that didn’t work, so he decided to make his own app that doesn’t use GPS or drain his phone battery. In the video below, Alastair talks about his adventures in working towards this goal, and the interesting things he learned about himself from the experience. (Filmed by the New York QS Show&Tell meetup group.)
Where are you? A pretty easy question to answer. But, what about, “Where was I?” Not so easy to answer, especially when we start talking about periods of time more than a few days or weeks. Sure, we all have GPS running on our phones now. We can check in with Foursquare/Facebook/Path etc. to keep a log of locations, but that data is fragmented and only represents certain specific locations. What about paths? What would we learn if we knew more about how we traveled about our world?
Aaron Parecki is one of the founders of Geoloqi, a location-based services platform. He has also been tracking his location every 6 seconds for the last four years and he has created some amazing visualizations to better understand his movement:
You may think this is just a boring old map with some travel data layered on top, but what makes this map special is that there is no underlying geospatial data. The lines you see above are Aaron’s actual travel paths from his GPS data. Using this information you can easily see the well traveled roadways by finding the thicker lines. You can even quickly pick out freeways and interstates due to their high speed.
Aaron has a lot more visualizations of his GPS traces, but I’ll leave you with this neat video showing a timelapse of his minute-by-minute movement:
Every few weeks be on the lookout for new posts profiling interesting individuals and their data. If you have an interesting story or link to share leave a comment or contact the author here.
Robby MacDonell doesn’t own a car, so he gets around on public transportation and on foot. He spent one month tracking his use of various modes of transportation, using the app MyTracks. In the video below, Robby talks about how he evaluated the different location-tracking tools, how he built his own custom interactive Google map, what metrics he tracked, and some really interesting, surprising things he learned. Great talk! (Filmed by the Seattle QS Show&Tell meetup group.)
This is the sixth post in the “Toolmaker Talks” series. The QS blog features intrepid self-quantifiers and their stories: what did they do? how did they do it? and what have they learned? In Toolmaker Talks we hear from QS enablers, those observing this QS activity and developing self-quantifying tools: what needs have they observed? what tools have they developed in response? and what have they learned from users’ experiences?
Location tracking apps and geo-tagging are becoming ever more common, and self-trackers have been finding ways to mine the data. The QS Amsterdam meetup group has featured many interesting talks (see Victor van Doorn, Joost Plattel, and Willempje Vrins and Leonieke Verhoog). At the QS Conference in May, Naveen Selvadurai of Foursquare showed how “check-in” data could be analyzed to understand your life. Now, Sam Liang, CEO of Alohar Mobile, and previously architect of Google’s Location Server, wants to make collection and analysis of personal location data much easier.
Q: How do you describe Placeme? What is it?
Liang: Alohar Mobile’s PlaceMe application is a tool to automatically remember all the places I have been to. It generates statistics like when I went there, how much time I spent there, how often I go there, etc. It also classifies the places I visited based on their categories, such as gyms, restaurants, parks, etc. It is available now for Android phones and soon will be available for iPhones. It will also remember the motion activities, such as how often I walk, how fast I walk, how much I drive, how much time I’m stationary. It captures memories for you, and enables you to search your past for quick recall of the places you’ve visited.
For people who are conscious about themselves, Placeme helps them keep track of their activities, and better understand themselves. People are always busy, and often forget to record what they want to log. Therefore, people need a tool to automatically remember things for them. Placeme is such a tool.
Placeme can also be used to understand people’s personal activities and health habits, and help people improve their lives.
Q: What’s the back story? What led to it?
Liang: I have always been curious about how I spend my life everyday. I always wished there was a tool that can journal my life automatically, understand my behavior and habits, then intelligently suggest things to me, which can help me improve my time management and improve my life as a result. As one example, although I often try to change some bad health habits, I almost always fail, because I’m busy working on something all the time, and can’t remember what I should do, and I’ll always regret it afterwards. So I’d love an intelligent personal assistant to help me achieve all of these.
When I was the architect for the Google Location Server, I realized that smartphones today have so many great senses. They can see, touch, and hear, in addition to sensing location and motion. With all these sensor data, the phone can learn so much about the mobile user, and can infer a lot about the user’s habits, interests and can predict future needs. So I wondered why can’t we make mobile phones more intelligent and help people automatically without requiring them to do everything manually. So I founded Alohar Mobile with a couple of friends from Stanford to pursue this dream.
Q: What impact has it had? What have you heard from users?
Liang: I have been running Placeme for Android and its predecessor for over a year. It has given me a lot of interesting insights, such as how much time I spend at work, at home, how much time I spend commuting, how often and how much time I spend playing tennis. For example, I noticed that in the past several weeks, because we are working so hard on our next release of Placeme, my work time has significantly risen, and I didn’t play tennis for 4 weeks! Seeing this data, I decided to go to swim at YMCA in the morning to increase my work-out time. Also, I saw that I spent far more time in office than at home for several weeks, to adjust the balance between family and work, I changed some of my work-hours, so that I can spend a bit more time with my family and I’ll do some additional work at home after the kids go to bed.
It automatically captured all the interesting places I visited during my trip to Alaska last summer and allowed me to easily reminisce about my trip. Interestingly, it also captured my black Friday shopping trips and the data showed me how much time and gas I wasted while driving around to and from the stores and malls, etc. The first screenshot (below) shows the places I spent some time at that day; the second screenshot shows some of the data Placeme automatically calculated from my location data; the third is a pie chart I made myself from that data.
We are still in our early stage, however, we’ve got dozens of enthusiastic beta testers running our application now. Many beta testers told us that they discovered some interesting facts unknown to themselves before, such as how much junk food they are having each week, how much time they actually spend walking, or going to the gym, and how much time is wasted commuting everyday.
Q: What makes it different, sets it apart?
Liang: Placeme has a number of unique features. The most important feature is that, in contrast to some existing applications, Placeme does most of the work automatically. Once the application is installed, it runs in the background, and requires no user assistance. It remembers all the data automatically, and it automatically generates the analytics results (daily, weekly, and historical) and presents them to the user. The user is not required to manually open the application, except when the user would like to see the results.
So Placeme requires little effort from the user, and makes it easier to be adopted.
Also, Placeme uses some intelligent power management algorithms (patent pending) to reduce battery consumption caused by sensor sampling. Though there is still a lot of optimization for us to do, we believe we have achieved one of the best battery life scores among such apps.
It runs on a smartphone, which most people are already using today. The user doesn’t need to carry a separate data gathering device (like Fitbit). All he needs is his smartphone running the Placeme app. In addition, the application is always connected to the Internet. So it can automatically save data to the cloud, have the cloud run sophisticated analytics algorithms, search for related info over the Internet, and then generate more interesting recommendations to the user.
In the mean time, all the data is kept private, and the user has full control of the data.
Q: What are you doing next? How do you see Placeme evolving?
Liang: The Android app has just launched, and we are currently developing the iPhone app.
We have an ambitious plan to build more and more intelligent features to better understand people’s habits and intentions, and make recommendations to help them improve their lifes. In the long run, we see Placeme evolving into an Intelligent Personal Assistant.
In a future version of Placeme, we want to offer a reminder service to notify people to break from bad health habits, and form good ones. For example, when our app detects that the user has been stationary for too long today, the app will automatically talk to the user and ask him/her to take a walk. Also, when our app detects that the user has visited junk food restaurants 3 times in a week, the app will send a warning to the user and recommend healthy alternatives.
We realize that we won’t be able to build all the great future features by ourselves, so we plan to offer a platform to make the technical functionalities available through an open API. Therefore, any mobile app developer can use our SDK and open API to build their own unique mobile applications by leveraging the mobile data collection and data analytics algorithms we have already developed. In addition, several mobile health application developers want to leverage the infrastructure Alohar is building, including the power-efficient data sampling algorithms and the mobile sensor data analytics system running in the cloud. And, several mobile game developers would like to use Alohar’s infrastructure to build more personalized games. (Developers interested in SDKs and APIs: email@example.com)
Q: Anything else you’d like to say?
Liang: The QS group is very passionate about self-measurement and self-improvement. We would like to invite more QS members to try Placeme so we can learn your feedback and suggestions for additional features.
(If you are a “toolmaker” and want to participate in this series, contact Rajiv Mehta at firstname.lastname@example.org)