Tag Archives: visualizations
Enjoy this week’s reading list. If you’d like to submit something for future What We’re Reading posts we invite you to get in touch!
Data Journalism Needs to Up Its Own Standards by Alberto Cairo. The influx of new data-based journalistic endeavors seems to grow by the day. In this great piece Alberto Cairo presents four suggestions for those practicing that art and science of data-based reporting.
Big Data Should Not be a Faith-Based Initiative by Cory Doctorow. The idea of “big data” as a miraculous fountain of new knowledge is widespread. In this article Cory Doctorow brings to light some of the major concerns about personal data and the true possibility of de-identification.
Data Privacy, Machine Learning, and the Destruction of Mysterious Humanity by John Foreman. This is a long read, but definitely worth the time. If you’re like me you’ll spend the next few hours (day?) thinking about yourself, the various companies and organizations consuming your data, and how your life may (or may not) be shaped by the information you willingly hand over.
Privacy Behaviors of Lifeloggers using Wearable Cameras [PDF] by Roberto Hoyle, Robert Templeman, Steven Armes et al. This research paper paper offers a good glimpse into the the concerns and real behaviors of people using photo lifelogging systems. This is an area we’ve previously explored (see Kitty Ireland’s great write-up about our lifelogging town hall at QSEU13) and we expect to continue discussing.
Battery Life, 6mo Checkup By James Davenport. It may seem odd to have a post about tracking battery life from a laptop here in the Show&Tell section, but this is a really neat post. As part of tracking his laptop battery he also tracked his usage and led to some interesting data about his sleep. (Don’t forget to check out the post that kicked off his battery tracking.)
Bringing My Data Together by John T. Moore. John is on a journey of improving his health and being more active through self-tracking/monitoring. In this post he pulls together some of his most important data, but I also suggest reading his summary of how he got started with self-tracking.
Seven Days of Carsharing by Density Design. Not exactly personal data here, but some beautiful visualizations based on one week of data from the Enjoy, a carsharing service in Milan.
Lee Rogers’ Annual Reports by Lee Rogers. Lee has been tracking different aspects of his life for more than three years. Since 2011 he’s put together Annual Reports detailing his personal data. You can view his 2011, 2012, and 2013 reports on his website.
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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.
A bit of a change this week. Today we’re posting some of our favorite academic and scholarly articles dealing with many different aspects of Quantified Self tools and methods. If that’s not for you, make sure to scroll down for some great self -tracking projects and visualizations. (Make sure to click [pdf] for the full article.)
Understanding Physical Activity through 3D Printed Material Artifacts [pdf] by Rohit Khot, Larissa Hjorth, and Florian Mueller. A fascinating paper on what happens when you transform digital physical activity data into representative physical objects.
Personal Tracking as Lived Informatics [pdf] by John Rooksby, Mattias Rost, Alistair Morrison and Matthew Chalmers. The authors of this research paper interviewed users of self-tracking tools to better understand how they incorporate personal data into their lives. From the abstract, “We suggest there will be difficulties in personal informatics if we ignore the way that personal tracking is enmeshed with everyday life and people’s outlook on their future.”
Persuasive Technology in the Real World: A Study of Long-term Use of Activity Sensing Devices for Fitness [pdf] by Thomas Fritz , Elaine M. Huang, Gail C. Murphy and Thomas Zimmermann. The authors of this study interviewed thirty individuals who had been using different activity tracking tools for different amounts of time (3-54 months). Those interviews unearthed some of the reasons why people starting using and continue to find activity trackers useful in their lives.
Using MapMyFitness to Place Physical Activity into Neighborhood Context by Jana Hirsch, Peter James, Jamaica Robinson et al. What can you find out about a population by partnering with a QS toolmaker? Jana Hirsch and colleagues tried to answer that question by partnering with MapMyFitness to better understand where and how individuals in Winston-Salem, North Carolina were exercising.
Visualized and Interacted Life: Personal Analytics and Engagement With Data Doubles [pdf] by Minna Ruckentstein. Don’t let the the title fool you, this article is not about new analytical methods for personal data. Rather, it is an thorough examination of the phenomenology of self-tracking and how people construct understanding of themselves through personal data collection.
Stress Trigger Personal Survey by Paul LaFontaine. We were lucky to hear about Paul’s stress tracking at the 2014 QS Europe Conference. While we work on getting that talk edited and posted online we thought this would be a great sneak preview.
Data, Pictures, and Progress by Chris Angel. Chris found out about QS while he was thinking about figuring out how to best lose weight. This post is his “first quarter” report from 2014.
Google has most of my email because if has all of yours by Benjamin Mako Hill. Benjamin has been running his own email server for 15 years. After a conversation with a friend he began wondering about how much email Google has a copy of. What followed was an amazingly in-depth analysis.
Strava Labs Global Heatmap. You can explore over 220 billion data points from almost 100 million different running and cycling activities tracked with the Strava app. (If you’re interested in the engineering side of this visualization they’ve written a great blog post here.)
From the Forum
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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.
On October 10th and 11th we held our fifth Quantified Self conference. These conferences have become a wonderful part of our ongoing work to share how people get personal meaning from their personal data. QS Show&Tells are the cornerstone of the program. In these short talks, we get to hear what you did, how you did it, and what you learned.
Visualizations of personal data are often important in a QS Show&Tell, so this year we made a simple request of all our conference attendees: send us your favorite personal data visualization and tell us what it means to you. Within a few hours we started receiving amazing images. We posted them at the conference and created some great conversation around making meaning through visualization. But a conference only lasts a few days, so we decided to start publishing them here, along with the same request to you. 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.
Be on the lookout as we begin this journey of sharing our personal meaning making through visualization. The images below are just a preview. Over the coming days, we’ll be putting each of the over 50 QS visualizations into its own post, along with a description and some links.
Thank you to everybody who came to the conference this year and shared their amazing work. See you Amsterdam in May!
In our daily lives, we are keenly aware of the power of each of these individual concepts. However taken together, their influence on our wellbeing, to borrow a phrase from my friend Karen Herzog, “our wholeness”, is exponentially influential. So why do they seem to rarely coalesce during our conversations, discussions, and interactions with the individuals and institutions tasked with tracking, diagnosing, and treating the cracks and fissures in our wholeness?
This is the first in a three-part series about the data we produce about our health and how we communicate that information to the medical system, specifically the providers of care. We’re starting from the perspective of the patient because we’ve all been there. Whether it was a routine check up or a 3AM visit to the emergency room, we’ve all had to relay information to a medical provider about out health. So what happens when we’ve collected, stored, and tried to understand our own health information in preparation for those visits?
Our guide today for the patient perspective of health data communication is Katie McCurdy. Katie is a user experience designer and researcher living and working in New York. She is also living with Myasthenia gravis, an autoimmune disease that causes muscle weakness in voluntary muscles. Like many individuals with autoimmune diseases, Katie spend a lot of time communicating and working with the medical system. These visits, although regular, were a point of contention between Katieand the individuals entrusted with her care. So when she was going to see a new physician for the first time she decided to apply her interaction design knowledge and skill. She’s talked about this on her blog and on the e-patients.net blog so I’ll let here words speak for themselves:
As I was getting ready to see a new doctor, I realized that the best way to tell my story would be to create a medical “life story” timeline that reflected:
- The course of my autoimmune disease
- Severity of my gastrointestinal problems
- Key moments in time when I started and stopped certain medications or took antibiotics
- Any significant dietary changes
I sketched out the two timelines (autoimmune and gastrointestinal) separately, and then created them electronically using Adobe Illustrator. (I’m an interaction designer by day, so fortunately I had the skills/know-how to create a somewhat legible artifact.) I used a peach color to represent gastrointestinal wellness/symptoms, and a blue color for Myasthenia Gravis.
QS: Why visualize? Do you think doctors are more receptive to the visual translation of data rather than the raw numbers that are commonly associated with health data?
KM: For me it’s about creating a representation of my history and my health that can be communicated most efficiently. I believe in the power of visualization to help tell stories that wouldn’t be possible with raw data alone. Knowing I would be ‘on the spot’ during my doctor visit put the pressure on to make something that would help me tell my story as succinctly as possible. Also…because I was not tracking my data (it’s all from memory) I didn’t have the raw data to share anyway!
QS: I’ve been thinking about the doc-patient relationship a lot lately. It seems the walls of authority are crumbling as we speak and we’re moving from a “You do this” or “You listen to me” type of authoritative approach to medicine to more conversational. How do you see data and visualizations helping to start and possibly support those conversations.
KM: I see it as, like you said, changing the dynamics of the relationship so that the patient is more of a partner in care. By tracking data, the patient can provide a more refined and nuanced picture of what is really going on with them. By visualizing that data, the patient is helping the doctor absorb the information more painlessly. The patient is providing contextual information about his or her OWN situation that compliments the doctor’s past experience, expertise, and test results.
QS: You mention in your post that the reception from patients and caregivers has been really positive, how would do we help make it a positive and rewarding experience for the providers as well?
KM: I think that giving patients tools to create simple, clean, and attractive visualizations could help make the experience better for doctors. If doctors are presented with high-quality visualizations that tell a coherent story, it may make office visits more efficient. Imagine if the doctor could work with the patient and suggest a type of graph or visualization that would be most helpful.
QS: What tips or advice would you give to someone who is taking their data to their doc for the first time?
KM: I suggest using the data as a storytelling tool. Bring a printed artifact or something on a tablet to refer to, and point out the highlights as you talk about what’s been going on with you. Don’t be disappointed if they don’t comment on your beautiful data and all of the work you put into it. Ask if there is anything you can do to to make the data more legible/easy to understand for the doc.
QS: You mention that self-tracking has given you better insights into your own health and that you’re even trying some self-experimentation like a no-carb diet. How do you think self-tracking and data communication with physicians can support patient-initiated health experimentation?
KM: Ah, I think self-tracking and visualization can help increase patient compliance! My low-carb diet was actually prescribed by my doctor. When I saw on the timeline that my diet changes were strongly correlated with my gastro symptoms improving, it was very reinforcing of my diet behavior. I mentioned antibiotics in my post. Now, if I even think of asking for antibiotics, all I can see in my mind is the number of antibiotics I took as my stomach issues got worse and worse. That is a big change in my outlook that resulted from internalizing the data I was seeing on the timeline.
QS: Who are your design/data viz heros? Anyone who really inspires you in your health visualizations?
KM: I have a few data viz heros! Jer Thorpe, of the new york times, makes beautiful interactive data visualizations and is one of the best speakers I have ever seen. Nicholas Felton, of Feltron and now a designer at Facebook, is a compulsive self-tracker who releases a gorgeous printed yearly report. I love Mortiz Stefaner’s work as well. I am really inspired by the natural world and the work of 19th century plant and wildlife documentor Ernst Haekel. I am also inspired by the awesome patients I’ve met and the folks on e-patients.net who remind me that patients need to be their own advocates.
We also have some questions from Susannah Fox, who was kind enough contribute her thoughts and insights to this piece:
SF: Would Katie care to comment on that from her own experience? That is, is it only recently that she has both found the right tools and that her own clinicians are interested? Had she attempted something earlier, with pencil & paper? What has made the difference?
KM: I never did anything before this apart from bringing notes to my doctor visits – things to remember to say. I literally had a realization one day at work and wrote an email to my personal account with the subject: ‘very important idea.’ :) I think the idea had to incubate for a few years before it bubbled up.last fall.
My goal is to keep pursuing this idea and work toward creating a tool for patients so they can at least assemble their own health timeline, and perhaps even track their data more regularly. I am holding interviews with patients, patient caregivers (or parents), and people who are active self-trackers; if you are interested in donating about 30 minutes of your time, email me at kathryn.mccurdy at gmail.com.
Again, this is part one in a three-part series on the data centric conversation we engage in with the medical community. Look for our next part with insights from Dr. Eric Topol and Dr. Larry Chu next Thursday. If you have questions of comments feel free to discuss on Facebook, Twitter, and here in our comments.
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!
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.