Topic Archives: Numbers from Around the Web
I’m typing this post while flying back to Southern California after spending a few days at a “Big Data” conference in San Francisco. One of the best things about the conference was meeting the subject of today’s round of Numbers From Around the Web. I first stumbled upon Bastian because he’s the main instigator and developer behind a great project called openSNP. Simply put, openSNP is a place you can host your direct-to-consumer genomic data for the world to see, understand, play with, download, well you get the point. This is a really interesting phenomenon that deserves it’s own post, but we’re going to explore some really neat QS experimentation and learning Bastian engaged in to better understand his sleep.
So, I found out about Bastian because openSNP announced that they also built a method to link and host Fitbit data (you can do that here if you’re so inclined). Turns out Bastian is an avid Fitbit user and has been using it to explore his sleeping patterns. His first major insight from his data indicated that in about 5.5 years he should be sleeping 24hrs per day:
So I downloaded my data from openSNPand started playing around with it: I did a simple linear regression over the time series and could indeed find a trend towards more sleep. The regression came out as y = 0.5x + 417, which ± says that for each two days that pass I will sleep a minute longer, which also means that it will be about 2000 days (or 5.5 years) until I will sleep 24 hours a day.
So yes, obviously regression may not be the best tool in the statistical toolbox to understand sleep so he decided to examine another question: “Do I sleep better or worse when there is someone in bed with me?” Using his sleep and calendar data he was able to identify nights he spend alone and nights he slept next to a warm body and found some pretty interesting stuff.
You can clearly see here in his table of 80 days of sleep (60 alone vs 20 with a companion) that he actually tends to sleep worse when he is sharing his bed. While he spends more time in bed, he takes longer to fall asleep, spends more time awake, and is awakened more often. For those of you who are not statically inclined those p-values indicate the probability that the difference in the two categories is due to chance (you can learn more about p-values here).
Like many good scientists he dug deeper to make sure what he was observing wasn’t related to other confounding variables such as the day of week:
Bastian didn’t find any significant differences in sleep quality between weekend and week days for his sleeping situation, but as one might expect he’s less active and sleeps more on weekends.
So, while this analysis might seem simplistic, one of the great things about Bastian and what he’s developing at openSNP is his willingness to be open with his data. Do you have some ideas about what you might find about Bastian from his Fitbit data? Have another hypothesis about sleep? Well you can test it out by downloading his data! You can start by reading his excellent post about this sleep analysis here.
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.
At some point, we’ve all been frustrated with our experiences interacting with the medical community. This isn’t a big secret, especially here in the United States. Many individuals involved in QS meetups around the world gravitate towards news tools and data sources that let them understand and interact with health-related data in news ways. Whether it’s because of a genuine medical issue or just out of curiosity, examples of tracking and visualizing medical data are always really interesting. With that in mind we wanted to highlight a neat health tracking project this week.
Kenneth Spriggs has Crohn’s disease and he’s been collecting and making sense of his medical records since the summer of 2011. Along with trying to better understand his own medical history, partly to understand what went right and what went wrong, he’s also spent some time creating some really unique and interesting visualizations. Let’s dive right in and see what he’s done!
How many different medications have you been on during your lifetime? Probably not the easiest question to answer unless you’ve been with the same medical system your entire life. One of Kenneth’s first forays into his medical data and visualizations was trying to represent My Life on Drugs. Admittedly a hard process that probably required a lot of patience and persistence, Kenneth was able to create a really nice timeline that illustrates his medical history through his medication.
10 Years of Crohn’s
If you don’t read anything else about Kenneths, his ordeal, and his experience working with his own medical data you should take a look at his amazing 10 Years of Crohn’s Disease infographic and accompanying commentary. As you’ll see Kenneth did a wonderful job working with his numerical health data as well as written notes, diagrams, and other information sources to paint a picture of how Crohn’s disease has impacted his life. Let’s just look at a few sections here.
The diagnosis portion of his medical infographic is one part that I find really interesting. Many times we think of QS as only dealing with numerical data, but in the medical universe data can come in all shapes and sizes. By looking at notes and diagrams along with other vital information Kenneth was able to create a much clearer picture of his history.
After Kenneth was diagnosed with Crohn’s disease he was prescribed five different medications. Here you’ll see the number of side effects he found for those medications and what he’s experienced. Along with his medication and side effect history you’ll see a histogram of his life history over the last 10 years.
There are so many data gathering and sense-making insights that Kenneth has has done a wonderful job of exploring and explaining. If you’re interested in compiling your own medical history and creating your own electronic health record then make sure to take some time perusing DIYEHR .
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.
Some people may be wondering how I find all the amazing people conducting neat self-tracking experiments and creating jaw-dropping personal data visualizations. Well, for the most part I just listen. I’m constantly paying attention to what’s being said on twitter about #QuantifiedSelf. When that doesn’t work I just use the power of Google to find people who are blogging about self-tracking, self-experimentation, or personal data. It’s great to look through the search results and see how many people are sharing their personal stories and insights. While doing some searching this morning I stumbled across a project that immediately brought a smile to my face. Hopefully you’re excited by this as much as I am.
Chris Volinsky is a statistician at AT&T Research and he’s no stranger to handling large data problems. Back in 2008 he was part of the team that won the $1 Million Netflix prize. He also has quite the impressive list of research papers that illustrate the many different uses of cellphone location data. But what is really interesting about Chris is his newest project: My Year of Data
Back in November of 2011 Chris started off a blog entry that with this:
My name is Chris. I am 40 years old. I am 5’9 1/2″ and weigh 174 pounds. I walked 9,048 steps and have consumed 1,406 calories today (so far).
Realizing that he’ld been gaining weight and wasn’t at his optimal health he decided to take a data-centric approach to improving his health. He is a statistician after all. So far, he’s found some interesting things. Take for instance his weight and dietary tracking.
As he explains in this post, Chris typically has a hard time tracking his diet consistently. This can be pretty frustrating when you hear about how important it is to eat this or not eat that to help with weight reduction. Rather than get frustrated Chris turned to the data to see what he could learn. When he stopped looking at the data he was entering and started looking at the missing data an interesting trend lept out. He found that fluctuations in his weight appeared to be correlated with whether or not he was logging food. Take for instance the plot below. It appears that there is a pretty clear association with periods of weight loss and periods of actively logging his food (pink zones). The opposite also appears to be true – no food logging = weight gain.
So this is where a typical NFATW post would stop. We have an interesting finding and a neat data visualization. But, Chris is doing something much more interesting than just talking about his weight data. He is on a long-term self-tracking and self-discovery journey and he is trying to enlist other interested parties to help him. Chris is going the extra step and posting all of his self-tracking data online for anyone to analyze, visualize, or just get inspired.
You can access all of his amazing data via a public dropbox folder that he’s set up. He even has a nice README file explaining the datasets and formats. So far he’s sharing the following:
- Fitbit: sleep and activity data
- FitLinxx: weight training data from gym activities
- Livestrong: dietary tracking data
- Runkeeper: running and other exercise activity data
- RescueTime: productivity tracking (computer/internet use)
All the data is open and available for you to play with. This should be a really interesting project to keep “track” of in the future (pun definitely intended). To help inspire some action on your part I took some time today and looked at Chris’s most recent available data to see what I could find out. I downloaded his Fitbit data and decided to look for any interesting patterns. Turns out that when taking a look at his daily patterns of activity there seems to be something going on on Thursdays that reduces his step count and activity time . Also, Saturday is by far the best day with an average of 9,862.56 steps and a 5.3 hours spent being active (data available here).
Make sure to reach out to Chris over at his blog and take a took at his data to see what interesting thing you can figure out!
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.
If you have diabetes, or know someone who does, you’ve probably encountered a blood glucose monitor. Like many medical devices, design and data visualization are usually an afterthought. While there are many new exciting products coming to market like the iBGStar designed by Agamatrix, there are individuals who want to learn more than just their current blood glucose values. Diabetes care is also moving towards an automated and coordinated process driven by continuous blood glucose monitoring and implantable insulin pumps. These devices live on data, huge amounts of data, but what do their users know? More specifically, what do their users understand about their data, their condition, and themselves?
Doug Kanter is a designer, photographer and a student in the Interactive Telecommunications Program (ITP) at NYU. He’s also a Type-1 diabetic who has a keen interest in applying actionable design and interaction schemes to the data he gathers from his monitoring systems.
It is time to re-imagine the entire user experience of being a patient with diabetes. There is tremendous potential in applying information technology, creative design and research into behavior change into a comprehensive product for patients. Technology-based solutions are increasingly important resources in these times of skyrocketing treatment costs and lmited doctor availability.
Doug has been using his skills to better visualize and understand his own data, particularly his continuous blood glucose monitor. His first project, 7729, explored one month of his continuous blood glucose monitoring – the 7729 readings to be exact.
His second project expanded on the 7729 project to include not only his blood glucose monitoring, but also the insulin he was receiving. Insulin on Board, is based on 100 days of data collection and includes 820 insulin pump reading and 25,012 blood glucose reading. By coordinating these two data sets he was able to look for patterns and identify the efficacy of his insulin dosing.
The goal of Insulin on Board was to better understand the relationship between the insulin I take and the resulting blood sugar readings. It visualizes not simply when I take a dose of insulin, but when that insulin “kicks in.” Because insulin has a latency, it is helpful to see it actually has an effect on blood sugar. Often times I’ll take two or more doses of insulin within a few hours. Insulin on Board calculates the sum overlapping effect of these dosages.
I think patients like me could benefit massively from having improved visualizations that give you both a solid overview of how you are doing but also allow you to dial down into the details if you want.
Being a student and designer, Doug has done a great job explaining the process he takes for developing these visualizations. If you’re interesting in learning more about how he created these visualizations, what he learned, and future work you can follow along at Databetic and his blog.
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:
There is something really magical about taking data and turning it into a compelling visual image. Even though I’ve already written a bit about the importance of making data visual, I am consistently amazed at how data can be made more appealing and informative by creating eye-popping graphics. Today we are devoting this NFATW post to some amazing projects with beautiful data.
Tom MacWright is an engineer for MapBox and Development Seed and spends his time creating and using amazing visual representations of his data. Here are just two of many wonderful projects.
A New Running Map
Tom wasn’t happy with the data visualization he was getting from his Garmin GPS and heart rate watch so he decided to build his own using tools he works with every day. What came out was a really interesting interactive website that visualizes his running routes along with his heart rate. Click on the image above to play around with him data.
He’s also created a unique representation of the same time of running data (GPS + HR) that anyone can play with called Ventricle. Ventricle allows you to plot your own running data if you have .gpx files.
I’ve had a long standing interest in how I spend my time interacting with my computer. As a long time RescueTime user I’ve gotten used to having something watching my computer use and informing me about my habits. Tom was also interested in his computer use, but wanted something that had less functionality while still giving him information that was important. So, he developed Minute, a keystroke counter and visualization system that constantly records and displays the keystroke frequency over time.
By using a heat map he is able to better understand the pattern of his technology usage. Interestingly, he is also able to make inferences about his sleep and leisure time as he treats them as the inverse of his keystroke time:
Minute is an open-source application hosted on github so if you’re interested in understanding your own computer use or want to contribute to the project go take a look at the source code.
We’ll wrap up today with a quote from Tom’s post on what he learned from developing and using Minute:
Tracking nearly anything you do is alarming and humbling. The aggregates of our actions are lost on us: we can watch hundreds of hours of television and write it off as a small time commitment. How much is too much? It’s hard to make pretty charts without learning something and thinking about what they should look like.
Dale Lane is a software developer for IBM living and working in Hampshire and he has been developing neat personal tools for his self tracking for the last few years. Let’s take a look at a few of them.
Tracking TV Watching
Inspired by the background data collection offered by last.fm designed to capture music listening habits Dale set out to create his own “scrobbler” to better understand his TV viewing habits. What he came up with is amazing:
Using a bit of code running on his media PC he is able to track a number of variables including time of day, what program he’s watching, his most watched channels, and many many more. Take a bit of time to check out his comprehensive blog post about the project and the TV Scrobbling project page.
Not satisfied while merely understanding what he was watching on TV, Dale took it upon himself to better understand how we was reacting to what he was watching. Using a webcam and a bit more code he was able to piece together a program that snaps a picture and then uses the Face.com API to determine interesting characteristics about the picture. The Face.com API enables him to see if he’s smiling as well as estimating his mood based on the facial characteristics that show up in the webcam shot. This little program has enabled him to find out some really interesting things such as:
He was also able to track his estimated emotional state while gaming and found some interesting insights:
This shows my facial expressions while playing Modern Warfare 3 last night. Mostly “sad”, as I kept getting shot in the head. With occasional moments where something made me smile or laugh, presumably when something went well.
These are really interesting and unique methods for understanding ourselves and our behavior. Dale’s work on self-tracking is fascinating and is an inspiration to those of us looking to expand our understanding of ourselves and how we interact and react with the digital world. Be sure to check out his blog for more self-tracking projects and interesting tools!
On March 8th, 2012 Stephen Wolfram opened the curtains and gave the world a glimpse of his own self-tracking and personal analytics practice. It was jaw-dropping. It was dense. It was beautiful. And, it might have shown us the future of what the Quantified Self could become. We were lucky enough to have Dr. Wolfram answer a few questions about his data, his personal insights, and the future of personal analytics.
First, some background. Stephen Wolfram is a no ordinary self-tracker. Reading through his biography has honestly given me a case of the envies. First published scientific paper by age 15. Five years later a PhD from CalTech in theoretical physics. He then went on to study and develop the field of scientific computing. This led him on a path towards trying to understand the underlying principles that drive the complex systems we often observe in nature. Of course he also had to invent his own computational engine to help discover those principles and in 1988 Wolfram Research released Mathematica. Not one to rest on his laurels he then set out to develop pioneering projects and in 2009 oversaw the release of Wolfram|Alpha, a new kind of computational knowledge engine.
During this time he also set up some really neat systems to store, process, and analyze different streams of data that make up his day-to-day life. I’ll just go over a few here that I found particularly interesting.
Phone Calls. Throughout all the development of his groundbreaking work and running a successful company, he has managed to operate as a remote CEO. So this means a lot of time on the phone. A lot.
We can clearly see that when you examine the available data Stephen spends more than 4 hours per day on the phone. But suppose you need to speak to him. When is the time he’s already most likely on the phone?
Probably best to call him between 11 AM and 6 PM. Although, maybe the weekend evenings would also be good. Personally I found this really interesting that his weekend and weekday evening phone use probabilities were so closely matched.
Steps. If you know me you know how much time I spend thinking about steps. I’m an avid Fitbit user and really believe in the power of subtle passive data tracking for physical activity. Stephen has been wearing a digital pedometer for a few years and has kept some amazing records of his daily activity:
And, I’ll be completely honest here. I was a little beside myself to learn that Stephen and I are kindred spirits when it comes to our use of treadmill desks:
There’s no mystery to this: years ago I decided I should take some exercise each day, so I set up a computer and phone to use while walking on a treadmill. (Yes, with the correct ergonomic arrangement one can type and use a mouse just fine while walking on a treadmill, at least up to—for me—a speed of about 2.5 mph.)
Those are just a few of the numbers and analyses he described in his fascinating blog post. Be sure to give it a read, but before you do read below for our interview with Stephen and a summary of thoughts on what a computational quantified self might become.
Q: There has been a lot of reaction around the internet regarding your post with many people astounded at how much data you’ve collected. In your estimation how much of that data is passively collected/recorded versus actively collected/recorded?
Stephen Wolfram: It’s essentially all passive. I’ve had systems set up at different times, then I’ve just let the systems run. And after a decade or two they’ve accumulated a lot of data.
I should say that quite a few of the systems are set up to send me mail each day with a report on the previous day (how much I typed; how many steps I took; etc.). I find this a useful form of self-awareness and self-management. But it has the side effect that it checks that the systems are still running. Systems that aren’t checked “on“ have a nasty habit of decaying and failing.
About active data collection: I’m frankly too busy (and perhaps too lazy) for that. Last year I decided to record everything I ate. I kept it up for the whole year, but it was a pain. I decided I’d wait for that data until it could be more automated (which it will be soon).
Q: The main point of contention that many people are making after learning about your data collection and analysis is along the the line of “Who has the time for that?” How would you answer that question and where do you see the field of personal informatics and analysis going to meet the needs of people who just don’t have the time?
SW: It has taken only a tiny amount of my time over the last 20 years or so! It did take time to set up the systems. But not to run them.
The analysis now has taken time too. And there’s lots more that should be done as well.
Both the systems and the analysis were made vastly easier by the fact that we used Mathematica for all of them. I’m hoping that we can set up systems that let other people use what we’ve built to do their own personal analytics–with essentially no expenditure of time.
Q: You’ve obviously spent a lot of time with your personal data, and mention that you have even more in reserve that you didn’t expand upon. What was the most surprising thing you’ve learned in so far in your exploration?
SW: I wish I’d had more time to spend with my data. The blog post and the data behind it are really only scratching the surface. Almost every plot we made, I said, “Hmmm … that’s interesting.” Often it was just confirming some impression about my life that I already had. But sometimes I learned things. An example that surprised me a bit was that so much of my email I end up processing late at night; I thought I was keeping up better during the day. I’d been thinking of going to sleep earlier … but I’d clearly have to find another scheme for my email then….
Q: Have there been or did you experience any negative effects from either tracking or analyzing your data? Where there any surprises about yourself that you weren’t expecting that weren’t positive?
SW: Not really. My children sometimes tease me about my obsessive data keeping, but that’s about it. I had thought perhaps I might be able to see some kind of degradation in my performance over the past 20 years, which would have been sobering … but there’s nothing that I’ve found so far. Quite to the contrary, in fact: it seems, for example, that I’m getting more done than I did 20 years ago.
Now, there are pieces of tracking that would be nice to do, but that I don’t feel comfortable doing. An example is keeping an image stream. I have a camera to wear around my neck, but it would be too weird for me in social situations to have it be there, so I have essentially never used it.
Q: Now that you’ve looked at such a large portion of your longitudinal personal data I wonder if you’ve changed your behavior in any way. Was there any point during the analysis, or possibly during previous analyses of this or other data that helped you decide to change something in your life, and if yes, what was it?
SW: About 10 years ago I did some analysis of my email flow, and concluded that it was better to wait awhile before responding to internal emails, because they tended to be on threads that “resolved themselves”, so I didn’t need to spend time.
There are lots of details that I’ve changed. An example is making sure spelling mistakes, especially in people’s names, get immediately resolved in e.g. calendar entries … because if they’re not, it becomes hopeless to search for them later.
A bigger thing some time ago from personal analytics is that I discovered that whenever I took a trip, my work was perturbed for quite awhile afterwards. That made me take fewer trips. Though recently my children have got me to take more trips … and I’m happy to find that technology has advanced to the point where I can avoid getting so behind as a result of them ….
Q: A majority of your post really gets into the correlational structure of multiple data sets. One would say that this is the epitome of post-hoc analysis. How do you think you could use the information you’re collecting now to help you make real-time decisions about your life and your behavior? What kind of system or data would have to be in place to make that worthwhile?
SW: As I’ve mentioned, I do have daily email sent to me, with data on quite a few things. I find that somewhat useful in regulating my behavior (“I didn’t get enough done yesterday; I’d better be more focused today”, etc.). Also, I think it always puts me in a good mood when I see that the previous day was very productive, and it helps me be more productive then.
I’ve thought about having some real-time displays. An example I recently set up is a count-up timer (really it’s just an iPad looking at a web page) that tells me how long I’ve been asleep. I also quite often look at my digital pedometer to see if I’ve walked enough steps yet in a day.
Q: Something that keeps popping up in my exploration of Quantified Self and people who use personal data tracking devices and services is the issue of “getting credit”. You yourself express regret over not having more information from earlier in your life. Some might argue that this dependence borders on the obsessive or unhealthy. That we are offloading our human intuition to these data capture systems. How do you balance your intuition and instinct with what you learn and access through your personal data collection and analysis?
SW: I am spoiled by personally having a rather good memory, especially for facts, names, etc. So I’ve always had the issue of remembering a lot about what happened in the past. Of course, with actual data I continually learn how sparse my memory is.
I’ve been fortunate enough to have all sorts of experiences in my life, and I’ve learned a lot from them. And the more I can remember the experiences, the better I can make use of what I learned from them. And the more I am able to repeat my successes, and avoid repeating my mistakes.
I’m definitely a person who likes to do things, not just contemplate my past life. But I like to build on my past life to continually be able to do more things, and have more personal satisfaction.
Q: Lastly, this post has been a big inspiration to a lot of people and has been shared and passed around by many. Are there any last insights or thoughts you’ld like to share with our community?
SW: I think it’s great that your community is working in these directions. There’s a lot of “best practices” to figure out, that will probably affect our lives a lot in the future. I’m looking forward to seeing what your community figures out!
So there you have it. Some very insightful words from the man behind the numbers. Now that I’ve read through his wonderful answers to my overly wordy questions, spent some time watching his wonderful TED talk, and doing some late night thinking, I’m starting to wonder about how his process and systems might be a window into our quantified futures.
The reason this post is entitled, The Computational Quantified Self, is because I think that Stephen is onto to something brilliant here with his process of having Wolfram|Alpha Pro handle all of the processing for him. One of the things that I hear time and time again from individuals who are either interested in or just starting their own self-tracking practice is, “What next?” That is to say, there are a lot of people out there who either do not have the skills, knowledge, or time to handle the analysis needed to make sense of the large data sets they’re creating. So what happens when there is a system to do that work for you? Maybe that system is something from Wolfram Research, or Google, or some new startup we don’t even know about yet. But surely there will be people who embrace those systems to help them make decisions. Better decisions. Informed decisions. Personalized data driven decisions that enhance and improve their lives.
That sounds like a pretty amazing world to live in.
Special thanks to Stephen Wolfram for taking time out of his busy schedule to answer our questions and to his staff for making this possible. This was a special edition of our regular Numbers from Around the Web series. If you have data you’ld like to have featured in the series you can contact the author here.
Karsten W. is on an amazing journey towards understanding his personal finances. Thankfully for us he’s been writing about his methods and what he’s learned along the way over at his blog: FactBased. Let’s dive in!
Step 1. Track
Karsten decided to use twitter to track his expenses and supplement that data with his normal bank statements. Not simply satisfied with this seemingly simple step, he went a bit further and compared his twitter entries to the data from his bank to see how well he was able to self-track:
Step 2: Classify
Having compiled his full year of monetary tracking, Karsten then looked to how to better understand where his money was going by classifying his spending. He looked into classification schemes and settled on using the United Nations Classification of Individual Consumption According to Purpose. Why?
It is made by people who have thought more about consumption classification than I ever will.
Again, he did some amazing number crunching and visualized his entire year.
Step 3. Compare
So Kartsen has his expenditure data and he has it classified according to a simple schema. What’s next? Why not compare it to what is typical for someone like him! Karsten did some digging and found the German Federal Statistics office completes surveys of consumption and income every five years. He pulled the data that most closely reflected his income level and created a neat comparison:
I found this series of blog posts to be fascinating. For one, Karsten wasn’t just satisfied with tracking his finances for a full year. He went above and beyond and did some personal data analysis using some really neat tools and methods. I highly suggest you read his posts on tracking, classification, and comparison. Not only are the posts interesting, they also include short “how-to” write ups if you want to implement his analytical and visualization methods using R (an free statistical program). Keep up the great work Karsten!
This is a special NFTAW post on a project we think is full of insight and beauty.
For those of you that were lucky enough to attend the first European Quantified Self conference this past November in Amsterdam you know how inspiring our good friend Laurie Frick can be. Laurie is a visual artist who meticulously and beautifully morphs her own self tracking data into wonderful pieces of art. Personally I find her large scale mood wall installations to be just jaw-dropping.
Phenomenal in her own right, imagine how surprised we were when Laurie emailed us earlier this week to tell us about an amazing story of a school teacher bringing self-tracking and visualization into her classroom. I’ll let Abigail Soto, an art teacher at De Valle High School in Austin Texas, tell you what happened in her own words:
24 hours in the life of a Del Valle student.
On February 1st, 2012 I attended the energetic and interesting gallery talk of Laurie Frick at Women & Their Work in Austin. She counted and tracked her everyday life and inspired me to have my high school student track their lives. My high school is very high poverty and my students have very little opportunity to see art and art galleries. My students love hearing stories about artists and galleries and I couldn’t wait to share my experience from the gallery talk.
On February 2nd I came to school and changed my lesson plan to include the students tracking their lives. I gave the students 24 rectangles in a line on a pre-printed sheet. I simply told them to track their previous 24 hours. One rectangle equaled one hour. The students collectively created a unified key with teenage issues. Blue signified sleep, red for school, pink for makeup, green for cell phone use etc… I did not give them any more requirements. My literal students started at a specific time and chronologically recorded their day while other students recreated their day in a more abstract manor.
The students really had to think about the length of their activities and many were shocked to find out how they spent their time. Students generated many great questions about the project and the artist. Conversations began about the amount of texting in the thousands and how much time is consumed with electronics. A great idea would be to illustrate and calculate the amount of text messages that are sent and received by each student. Some students text over 1000 times per day. Teacher and homework time can hardly compete with cell phones, television, and video games. Just by evaluating their actions they were able to visually see where they placed importance and how they should choose their time wisely.
Once each student completed their color chart they were placed in the hallway for the entire school to see. The key was placed to the side of the color charts and students walking down the hall stopped to figure out what the colors meant. The curiosity grew and non-art students were walking in my classroom asking if they could record their day. The overall experience has been very interesting. I have never been exposed to tracking and have never included tracking in an art lesson. I would like to take this lesson a step farther and do a complete lesson and art installation with my high school students. I think the possibilities are endless. Thank you Laurie Frick for expanding the possibilities in my classroom.
I think that there are a lot of lessons here that we as a community of users and makers can take away. Sometimes we get caught up in the gadgets and new technologies that we associated with real objective data collection. While those tools and web services are fun and provide us with new insights into our lives we mustn’t forget that the tools doesn’t make the tracking happen, it just makes it easier. I was asked at a conference this past week, “All those gadgets are nice, but what about the people who can’t afford them? What do you say to those people?” I think that implementing projects such as the one illustrated by Abigail Soto here is a way to bring people from all walks of life into a practice of self-tracking. Amazing insights can happen with a piece of paper, some lines, and a few colored markers. As an aside, during this same conference I met a woman whose 83 year old mother has been meticulously tracking her blood pressure and blood glucose, not with a fancy smartphone of wifi enabled device, but with good old pen and paper.
The second major lesson I took away from this project is that Quantified Self is a community in the truest sense of the world. We (and if you’re reading this that “we” includes you too) work hard to make sure that anyone and everyone feels that they can take part. Whether it is at a meetup, at a large conference, or in one-on-one discussions there is an amazing current of inclusivity, of togetherness, of intimate and abundant sharing. I’ve never once heard someone pipe up and say, “That’s not self-tracking.” As I read Abigail’s description and looked through those beautiful pieces of data visualization it made me proud that she and her students could feel included in this wonderful movement we are all a part of.
We’ll be highlighting more art projects and self-tracking experiments in future NFATW posts so please feel free to drop me a line and share your story or point out someone’s you’ve seen or read about. Our strength lies in our courage to share with each other.