Jamie Aspinall on Learning From Location Data

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.

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What We Are Reading

We’ve assembled another great list of articles, posts, and other interesting ideas for you to enjoy.

Articles
Billy Beane’s Ascendant A’s Are Playing a Brand-New Brand of Moneyball by Will Leitch. I know what you’re thinking, “What’s an article about baseball doing in this list?” First, it’s about how the Oakland Athletics are using metrics to improve their team. And two, I was struck by the following:

“Instead, Beane and his front office have bought in bulk: They’ve brought in as many guys as possible and seen who performed. They weren’t looking for something that no one else saw: They amassed bodies, pitted them against one another, were open to anything, and just looked to see who emerged. Roger Ebert once wrote that the muse visits during the act of creation, rather than before. The A’s have made it a philosophy to just try out as many people as possible—cheap, interchangeable ones—and pluck out the best.”

Sounds a lot like our old friend, Seth Roberts, describing the value of self-experimentation - start small, do a lot of them, learn by doing.

Build Great Models . . . Throw Them Away by Mark Ravina. A digital humanities researcher makes the case for using data and statistical methods of modeling not to answer questions, but to come up with better questions. Really enjoyed the great examples in this post.

App data reveals locations, times and distances of Calgary’s runners and cyclists by Meghan Jessiman. A collaboration between RunKeeper and the local Calgary Herald newspaper led to some interesting findings and, of course, some activity heat maps.

A Digital Dose of Magic Medicine by Naveen Rao. Naveen connects the dots between the recent controversy surrounding Doctor Oz to the possibly misplaced hopes we’re putting in tools like HealthKit.

9-Volt Nirvana by RadioLab. This episode of the always interesting RadioLab tells the story of a journalist who was hooked up to a tDCS device for a sniper shooting exercise. The device helped her accuracy in the simulation, but then there was an unexpected after-effect. For three days afterward, the voices of self-doubt and self-abnegation receded from her consciousness. She talks about that experience directly on her blog. (Thanks to Steven Jonas for sending this one in!)

Tracking Sleep With Your Phone by Belle Beth Cooper. A great roundup here of iOS and Android apps you can use to track sleep. I especially appreciated the nice discussion of the current limitations of using mobile apps to track and understand sleep.

From Missiles To The Pitch: The Story Behind World Cup Tech by Melissa Block and NPR. If you’re wondering how FIFA is able to track the movement of individuals players during this year’s World Cup then this is for you. You can also check out all the data on FIFA’s website here.

Show&Tell
Productivity, the Quantified Self and Getting an Office by Bob Tabor. Bob works at home and was curious about how productive he really was. After using RescueTime he realized maybe he wasn’t getting the productive time he really need.

Basis to Roambi by Florian Lissot. Florian wanted to explore his Basis data. After using Bob Troia’s great data access script and some additional tools to aggregate multiple files he was able to create some great visualizations with Roambi and learn a bit more about his daily patterns of activity.

Do you have a self-tracking story you want to share? Submit it now!

Visualizations
losangeles-transport
How We Move in Cities by Human.co. It seems that making heatmaps based on movement is all the rage these days. Human has gone one step further than previous entries in this category by including motorized travel alongside cycling, walking, and running data. Don’t forget to check out the amazing GIFs as well.

cecinestunedataviz
This is Not a Data Visualization by Michael Thompson.

“[...] visualizations are not the data. The data is not the sum of the experience. We’ve been inappropriately using data visualizations as the basis for statements and conclusions. We’re leaving out rigorous statistical analysis, and appropriate qualifiers such as confidence intervals. It’s exciting that we’ve become more and more a society of pattern-seekers. But it’s important that we don’t become lazy and cavalier with what we do with those observations.”

MSFTdataviz
Reflections on How Designers Design With Data [PDF] by Alex Bigelow, Steven Drucker, Danyel Fisher, and Miriah Meyer. Researchers from Microsoft and the University of Utah sought out to understand how designers go the process of understanding data and creating unique visualizations.

Do you have a QS data visualization you want to share? Submit it now!

From the Forum
Best passive GPS Logger?
Quantified Baby
Android App for Self Surveys

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Alberto Frigo: A 36-year Tracking Project

AlbertoFrigo_RHand

“I’ve been systematically tracking my life since the 24th of September, 2003.”

A little over 10 years ago Alberto Frigo embarked on an ambitious project, 2004-2040, to understand himself. Starting with tracking everything his right (dominant) hand has used, he’s slowly added on different tracking and documentation projects. Keeping the focus on himself and his surrounding has helped him connect to himself and the world around him.

Beside the more technical challenges ahead, I have learned how much we can engage in tracking and quantifying ourselves. I have learned that I am what I track and in this respect what I track shapes my life. I believe that every individual can activate him or herself to record his or her life and create a playful engagement with an otherwise dull surrounding. In my opinion then, it is a very healthy commitment as it makes us more aware as well as more engaged with our everyday life.

At the 2014 Quantified Self Conference Alberto led our second day opening plenary, which focused on “Tracking Over Time.” We invite you to watch his talk and read the transcript below to learn about his plans for the next 26 years and what hear what he’s learned through this process.

Transcript
Good morning everyone and thank you for inviting me to this inspiring and well organized event! My name is Alberto Frigo, I am 34 years old, and I was born in a small village in the Italian Alps but I have spent most of my life abroad doing media research and living in Northern Europe, North America and China. Currently I live in Sweden where I have been systematically tracking my life since the 24th of September 2003. I clearly remember that day: it was very sunny in Stockholm, but I felt very frustrated since I was about to start a residency the following day and the wearable computer I had for two years designed to record my life was not working. I was walking through the city with my frustrations when I passed by a tiny store selling dusty photographic equipment. In the middle of it there was a brand new and tiny digital camera. I immediately gave up all my frustrations with the clumsy wearable I had so far been constructing, and just got that very camera to start photographing every object my right hand use.

It has been more then 10 years since I have started that project, to be precise today the 11th of May 2014, is my 3.882nd day I have been photographing every object my right hand uses. With this project, my idea is to track all my daily activities that I do through the very objects I utilize to accomplish them. In total, I have been photographing 295.032 activities, an average of 76 objects a day and one picture every 15 minutes, depending on how busy that day has been. I have also discovered that, if I keep up the project until I turn 60, I will have photographed 1.000.000 objects and could thus claim to have some kind of DNA code of my life, or at least of the core of my life as a mature individual. I like to see this code as made of a continuous sequence of repeating elements, the objects I use as the letters of an alphabet which can give rise to different patterns and understandings.

In this respect, I have embarked on a 36 years long project, from 2004 to 2040, and new kinds of self-tracking projects, or life-codes have naturally come about, mostly in order to compensate the photographic tracking of activities. After a few years, in 2005, I have also started to consider to keep track of myself looking at other perspectives and using different other media such as recording my dreams through writings and recordings the songs I listen to through musical notations. Additionally, I also started to keep track of my social surrounding and the weather. I thus ended up, for example, filming every public space in which I seat and keeping track of the wind when I am outdoor. At this point of time I am conducting 36 different projects to record my life… as many as the years I mean to undertake the project. 18 of these projects are actual tracking of either myself, or the surrounding or the weather and 18 of them are elaborations, like books, gadgets or exhibitions I make about them… not the least this very speech and other meta projects like a virtual memory cathedral I will present in the office hour section after lunch.

To give you an idea of what I am up to these days, I can tell you what I have accomplished so far. Beside recording 295.032 of my activities by photographing the objects my right hand has used with this camera, I have been tracking 12.360 dreams, 5.440 songs that I have heard and recognized using this phone to keep track of them, 620 portraits of new acquaintances using this camera, 285 square meters of discarded objects picked from the sidewalk using this pouch to collect them, 1.512 news of casualties, 15.660 films of public spaces where I seat using this video-camera, 7.560 drawings of ideas, 2.760 recordings of thoughts while walking alone using this recorder, 1.704 shapes of clouds and so forth. By the way this the USB where all my work is stored, always on me.

It was not immediate to be able to track so many things at once; I have learned that one has to start with something basic and simple and then add up to new perspectives and tracking technologies, preferably crafting his or her framework. “From one thing comes another” then but I am also interested to keep up all the projects, more as some sort of a challenge in addition to simply a tool to later make sense of my life. I feel in this respect like the character of a computer game, with a mission to accomplish and this is really my drive in life, conduct these 36 tracking projects till I am 60, in 2040 then, 26 years ahead of me. 26 years in which new challenges arise such as the fact that the technology I have been deploying, like my camera, is already out of production. Well, one ought to act providently and myself, I have got a box full of these refurbished cameras… so lets just hope the operating system now won’t change too drastically in the coming years!!

Beside the more technical challenges ahead, I have learned how much we can engage in tracking and quantifying ourselves. I have learned that I am what I track and in this respect what I track shapes my life. I believe that every individual can activate him or herself to record his or her life and create a playful engagement with an otherwise dull surrounding. In my opinion then, it is a very healthy commitment as it makes us more aware as well as more engaged with our everyday life.

Saying this however, I have to warn you that it is important to give priorities also in what we track. I mostly give priorities to the tracking relating to myself, giving less priority to other forms of tracking, particularly to those forms that are more dependent to the social surrounding and I cannot control. With these priorities in mind I am rather positive that I can succeed in my self-exploration and the exploration of the world through myself, sharing my experience as time passes by and new insights are gained. I really hope my ten years old experience and commitment can be of inspiration of you. Please feel free to approach me so that I can photograph you as my 621st new acquaintance. Thank you!

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Rain Ashford on Wearing Physiological Data

Rain Ashford is a PhD student in the Art and Computational Technology Program at Goldsmiths, University of London. Her work is based on the concept of “Emotive Wearables” that help communicate data about ourselves in social settings. This research and design exploration has led her to create unique pieces of wearable technology that both measure and reflect physiological signals. In this show&tell talk, filmed at the 2013 Quantified Self Europe Conference, Rain discusses what got her interested in this area and one of her current projects – the Baroesque Barometric Skirt.

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QSEU14 Breakout: Open Privacy

Today’s post comes to us from Laurie Frick. Laurie led a breakout session at the 2014 Quantified Self Europe Conference that opened up a discussion about what it would mean to be able to access all the data being gathered about yourself and then open that up for full transparency. In the summary below, Laurie describes that discussion and her ideas around the idea of living an open and transparent life. If you’re interested in these ideas and what it might mean to live an open and transparent life we invite you to join the conversation on our forum.  

LFrick

Open Privacy
by Laurie Frick

Fear of surveillance is high, but what if societies with the most openness develop faster culturally, creatively and technically?

Open-privacy turns out to an incredibly loaded term, something closer to data transparency seems to create less consternation. We opened the discussion with the idea, “What if in the future we had access to all the data collected about us, and sharing that data openly was the norm?”

Would that level of transparency gain an advantage for that society or that country? What would it take to get to there? For me personally, I want access to ALL the data gathered about me, and would be willing to share lots of it; especially to enable new apps, new insights, new research, and new ideas.

In our breakout, with an international group of about 21 progressive self-trackers in the Quantified Selfc community, I was curious to hear how this conversation would go. In the US, data privacy always gets hung-up on the paranoia for denial of health-care coverage, and with a heavy EU group all covered with socialized-medicine, would the health issue fall away?

Turns out in our discussion, health coverage was barely mentioned, but paranoia over ‘big-brother’ remained. The shift seemed to focus the fear toward not-to-be-trusted corporations instead of government. The conversation was about 18 against and 3 for transparency. An attorney from Denmark suggested that the only way to manage that amount of personal data was to open everything, and simply enforce penalizing misuse. All the schemes for authorizing use of data one-at-a-time are non-starters.

“Wasn’t it time for fear of privacy to flip?” I asked everyone, and recalled the famous Warren Buffet line “…be fearful when others are greedy and greedy when others are fearful”. It’s just about to tip the other way, I suggested. Some very progressive scientists like John Wilbanks at the non-profit Sage Bionetworks are activists for open sharing of health data for research. Respected researchers like Dana Boyd, and the smart folks at the Berkman Center for Internet and Society at Harvard are pushing on this topic, and the Futures Company consultancy writes “it’s time to rebalance the one-sided handshake” and describes the risk of hardening of public attitudes as a result of the imbalance.

Once you start listing the types of personal data that are realistically gathered and known about each of us TODAY, the topic of open transparency gets very tricky.

  • Time online
  • Online clicks, search
  • Physical location, where have you been
  • Money spent on anything, anywhere
  • Credit history
  • Net-worth
  • Do you exercise
  • What you eat
  • Sex partners
  • Bio markers, biometrics
  • Health history
  • DNA
  • School grades/IQ
  • Driving patterns, citations
  • Criminal behavior

For those at the forefront of open privacy and data transparency it’s better to frame it as a social construct rather than a ‘right’. It’s not something that can be legislated, but rather an exchange between people and organizations with agreed upon rules. It’s also not the raw data that’s valuable – but the analysis of patterns of human data.

I’m imagining one country or society will lead the way, and it will be evident that an ecosystem of researchers and apps can innovate given access to pools of cheap data. I don’t expect this research will lessen the value to the big-corporate data gatherers, and companies will continue to invest. A place to start is to have individuals the right to access, download, view, correct and delete data about them. In the meantime I’m sticking with my motto: “Don’t hide, get more”.

If you’re interested in the idea of open privacy, data access, and transparency please  join the conversation on our forum or here in the comments. 

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Alex Collins on Managing Type 1 Diabetes

Last year Alex Collins was diagnosed with Type 1 diabetes. Prior to his diagnosis Alex was frequently engaged in different types of exercise and physical activity. After his diagnosis his doctor mentioned that he might have a hard time exercising and controlling his blood sugar to prevent hypoglycemia. In this talk, presented at the London QS meetup group, Alex described his process for tracking and understanding the data that affects his day-to-day life so that he could “live my life normally without a high risk of complications.” This process of collecting and analyzing data has even pushed him to continue to explore his athletic boundaries, resulting in a running a ultramarathon and setting the world record for the fastest marathon while running in an animal costume.

Slides are available here.

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What We Are Reading

We’ve compiled another list of interesting personal data and Quantified Self articles, self-tracking stories, and data visualizations. Enjoy!

Articles

Experimental evidence of massive-scale emotional contagion through social networks by Adam Kramer, Jamie Guillory, and Jeffrey Hancock. Facebook engaged in a large study to see if emotional states could be transferred/changed via the emotional content of the News Feed. A lot of hubbub recently about this research article related to what it means to knowingly consent to research.

Who Owns Your Personal Data: The Incorporated Woman. Jennifer Morone has added herself to a long list of individuals making a statement against the commercialization of personal data. What started as a design assignment has morphed into a statement against others profiting and controlling personal data. (Immediately remind me of this Kickstarter project.)

Quantified Self and the Ethics of Personal Data by Ian Forrester. Ian does a great job here of exploring current conversations about the variety of ethical questions that come with creating, using, and owning personal data.

Visualizing Algorithms by Mike Bostock. Mike is the creator and steward of the d3.js data visualization library. In this fascinating post, he recounts one of his recent talks about how visualization can be used to understand how algorithms work.

“[...] algorithms are also a reminder that visualization is more than a tool for finding patterns in data. Visualization leverages the human visual system to augment human intellect: we can use it to better understand these important abstract processes, and perhaps other things, too.”

Biometric Shirt for Astronauts Gets Antarctic Tryout by Eliza Strickland. Eliza describes a “try-out” for using self-tracking technology to better understand vital signs and activity during space travel.

Show&Tell

My Solution for Quantified Self: Prompted Data Aggregation by Jonathan Cutrell. Jonathan decided to build his own simple system for self-directed data collection prompts. “While they may be simple data points, and while the questions will repeat, the concept is simple: my computer asks me a question, and tracks my answer for me.”

Quantified Splunk: Tracking My Vital Signs by David Greenwood. David describes how he uses Splunk, a data monitoring and analysis tool, to help him track and make sense of new personal health data he’s collecting. It will be interesting to see what he learns as he starts adding and exploring more of his self-tracking data.

How I Wrote 400K Words in a Year by Jaime Todd Rubin. In March of 2013, Jaime decided he was going to try and write every day. In this post he describes some of the lessons he learned through tracking his writing practice. I was particularly drawn to his methods for tracking all his writing through Google Docs.

Do you have a self-tracking story you want to share? Submit it now!

Visualizations

Withings-via-IFTTTCharting Withings Data Using IFTTT, Google Spreadsheets, and d3.js by Dan Harrington. Dan didn’t like the way Withings presented weight data in it’s visualization. So, he put together a tutorial to show how you can grab your Withings data via IFTTT, import it into a Google Spreadsheet, then visualize it using d3.js, an open-source data visualization library.

 

 

MapRunKeeper1.5 Million Walks, Runs, and Bike Rides by Mapbox and RunKeeper. The Mapbox team worked with Runkeeper to map publicly shared routes. Really interesting to zoom around the world map to see where people who use RunKeeper are exercising.

 

 

Do you have a QS data visualization you want to share? Submit it now!

From the Forum

HealthKit
Lifelogging via Calendar Application
Help Analyzing Text Files?
Breakout: Productivity Tracking

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A Code of Conduct

Quantified Self Labs is wholeheartedly dedicated to creating conferences and events that are safe and comfortable for everyone involved. This means providing a harassment-free experience for every participant, regardless of gender identity and expression, sexual orientation, ability, ethnicity, socioeconomic status, physical appearance, and beliefs.

Providing this experience for our members requires a concrete, visible commitment to the community. We introduced our first anti-harassment policy for the Quantified Self Global Conference in 2013. Quantified Self Boston has followed suit and introduced a Code of Conduct for all their events in April, 2014. Our Bay Area QS meetup has recently implemented this Code of Conduct as well. Meetup members from the Quantified Self Community have also started three women’s* meetups (QSXX San Francisco, QSXX Boston, QSXX New York City) to promote inclusion and safe spaces for sharing.

If you are involved in Quantified Self as an organizer or meetup member we encourage you to read our anti-harassment policy and the QS Boston Code of Conduct and use them as a basis for your own work in creating events that are welcoming to all.

Editor’s Note: Thanks to our friends Amelia Greenhall and Maggie Delano for their leadership. For more analysis and practical resources in fighting sexism in technology culture, see: The Ada Initiative.

Resources / further reading:
Code of Conduct 101 + FAQ
Why Women Need Women-Only Networks
First QSXX blog post
QSXX Boston Meetup Recap

Women’s* meetups:
QSXX San Francisco
QSXX Boston
QSXX New York City

* All women and people who identify or have identified as significantly non-male are welcome at QSXX, including those with non-binary and/or trans* gender identities.

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Kiel Gilleade: Rhythmanalysis

Kiel Gilleade has been interested in measuring and visualizing physiological data for quite a while. In 2011, he presented his BodyBlogger project at the 2011 QS Europe Conference. In that talk he described what he learned from tracking and exploring a year of continuous heart rate data. This year, at the 2014 QS Europe Conference, Kiel returned to talk about a new project, Rhythmanalysis. Rhythmanalysis was a project centered on “visualising the biological rhythms of employees at different workplaces.” In this short talk, Kiel describes his experience working on this project and some of the lessons he learned along the way.

If you’re interested in learning more about this work I highly suggest you visit Kiel’s website where he has additional videos of visualizations he’s been working on that use data collected as part of this project.

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QSEU14 Breakout: Mapping Data Access

Today’s post comes to us from Dawn Nafus and Robin Barooah. Together they led an amazing breakout session at the 2014 Quantified Self Europe Conference on the topic of understanding and mapping data access. We have a longstanding interest in observing and communicating how data moves in and out of the self-tracking systems we use every day. That interest, and support from partners like Intel and the Robert Wood Johnson Foundation, has helped us start to explore different methods of describing how data flows. We’re grateful to Dawn and Robin for taking this important topic on at the conference, and to all the breakout attendees who contributed their thoughts and ideas. If mapping data access is of interest to you we suggest you join the conversation on the forum or get in touch with us directly.

Mapping Data Access
By Dawn Nafus and Robin Barooah

One of the great pleasures of the QS community is that there is no shortage of smart, engaged self-trackers who have plenty to say. The Mapping Data Access session was no different, but before we can tell you about what actually happened, we need to explain a little about how the session came to being.

Within QS, there has been a longstanding conversation about open data. Self-trackers have not been shy to raise complaints about closed systems! Some conversations take the form of “how can I get a download of my own data?” while other conversations ask us to imagine what could be done with more data interoperability, and clear ownership over one’s own data, so that people (and not just companies) can make use of it. One of the things we noticed about these conversations is that when they start from a notion of openness as a Generally Good Thing, they sometimes become constrained by their own generality. It becomes impossible not to imagine a big pot of data in the sky. It becomes impossible not to wonder about where the one single unifying standard is going to come from that would glue all this data together in a sensible way. If only the world looked something like this…

data_access

We don’t have a big pot of data in the sky, and yet data does, more or less, move around one way or another. If you ask where data comes from, the answer is “it depends.” Some data come to us via just a few noise-reducing hops away from the sensors from which they came, while others are shipped around through multiple services, making their provenance more difficult to track. Some points of data access come with terms and conditions attached, and others less so. The system we have looks less like a lot and more like this…

mapping_access

… a heterogeneous system where some things connect, but others don’t. Before the breakout session, QS Labs had already begun a project [1] to map the current system of data access through APIs and data downloads. It was an experiment to see if having a more concrete sense of where data actually comes from could help improve data flows. These maps were drawn from what information was publicly available, and our own sense of the systems that self-trackers are likely to encounter.

Any map has to make choices about what to represent and what to leave out, and this was no different. The more we pursued them, there more it became clear that one map was not going to be able to answer every single question about the data ecosystem, and that the choices about what to keep in, and what to edit out, would have to reflect how people in the community would want to use the map. Hence, the breakout session: what we wanted to know was, what questions did self-trackers and toolmakers have that could be answered with a map of data access points? Given those questions, what kind of a map should it be?

Participants in the breakout session were very clear about the questions they needed answers to. Here are some of the main issues that participants thought a mapping exercise could tackle:

Tool development: If a tool developer is planning to build an app, and that app cannot generate all the data it needs on its own, it is a non-trivial task to find out where to get what kind of data, and whether the frequency of data collection suits the purposes, whether the API is stable enough, etc.. A map can ease this process.

Making good choices as consumers: Many people thought they could use a map to better understand whether the services they currently used cohered with their own sense of ‘fair dealings.’ This took a variety of forms. Some people wanted to know the difference between what a company might be capable of knowing about them versus the data they actually get back from the service. Others wanted a map that would explicitly highlight where companies were charging for data export, or the differences between what you can get as a developer working through an API and what you can get as an end user downloading his or her own data. Others still would have the map clustered around which services are easy/difficult to get data out of at all, for the reason that (to paraphrase one participant) “you don’t want to end up in a data roach motel. People often don’t know beforehand whether they can export their own data, or even that that’s something they should care about, and then they commit to a service. Then they find they need the export function, but can’t leave.” People also wanted the ability to see clearly the business relationships in the ecosystem so they could identify the opposite of the ‘roach motel’—“I want a list of all the third party apps that rely on a particular data source, because I want to see the range of possible places it could go.”

Locating where data is processed: Many participants care deeply about the quality of the data they rely on, and need a way of interpreting the kinds of signals they are actually getting. What does the data look like when it comes off the sensor, as opposed to what you see on the service’s dashboard, as opposed to what you see when you access it through an API or export feature? Some participants have had frustrating conversations with companies about what data could fairly be treated as ‘raw’ versus where the company had cleaned it, filtered it, or even created its own metric that they found difficult to interpret without knowing what, exactly, goes into it. While some participants did indeed want a universally-applicable ‘quality assessment,’ as conveners, we would point out that ‘quality’ is never absolute—noisy data at a high sample rate can be more useful for some purposes than, say, less noisy but infrequently collected data. We interpreted the discussion to be, at minimum, a call for greater transparency in how data is processed, so that self-trackers can have a basis on which to draw their own conclusions about what it means.

Supporting policymaking: Some participants had a sense that maps which highlighted the legal terms of data access, including the privacy policies of service use, could support the analysis of how the technology industry is handling digital rights in practice, and that such an analysis could have public policy implications. Sometimes this idea didn’t take the form of a map, but rather a chart that would make the various features of the terms of service comparable. The list mentioned earlier of which devices and services rely on which other services was important not just to be able to assess the extent of data portability, but also to assess what systems represent more risk of data leaking from one company to another without the person’s knowledge or consent. As part of the breakout, the group drew their own maps—maps that either they would like to exist in the world even if they didn’t have all the details, or maps of what they thought happened to their own data. One person, who drew a map of where she thought her own data goes, commented (again, a paraphrase) “All I found on this map was question marks, as I tried to imagine how data moves from one place to the next. And each of those question marks appeared to me to be an opportunity for surveillance.”

What next for mapping?

If you are a participant, and you drew a map, it would help continue the discussion if you talked a little more about what you drew on the breakout forum page. If you would like to get involved in the effort, please do chime in on the forum, too.

Clearly, these ecosystems are liable to change more rapidly than they can be mapped. But given the decentralized nature of the current system (which many of us see as a good thing) we left the breakout with the sense that some significant social and commercial challenges could in fact be solved with a better sense of the contours and tendencies of the data ecosystem as it works in practice.

[1] This work was supported by Intel Labs and the Robert Wood Johnson Foundation. One of us (Dawn) was involved in organizing support for this work, and the other (Robin) worked on the project. We are biased accordingly.

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