Tag Archives: data visualization

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

Want to receive the weekly What We Are Reading posts in your inbox? We’ve set up a simple newsletter just for you. Click here to subscribe

Posted in What We're Reading | Tagged , , , , , | 1 Comment

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.

Posted in Conference, Videos | Tagged , , , , , , , | 1 Comment

What We Are Reading

We’re back with another great set of articles, show&tells, and visualizations for you.

Articles
How to Make Government Data Sites Better by Nathan Yau. Government entities are some of the largest holders of interesting data. Nathan focuses this article on the difficulties of accessing and making sense of data from the United States Centers for Disease Control and offers some good ideas on how to make it better.

Project Eavesdrop: An Experiment At Monitoring My Home Office by Steve Henn. What happens when you start monitoring yourself in the same manner the NSA might be doing? The author employs some technical help to learn what data leaks are possible and what you can figure out from your digital trails.

Sitting is Bad for You. So I Stopped. For a Whole Month. by Dan Kols. As a past frequent user of a treadmill desk and a sedentary behavior researcher I found this article intriguing. Yes, a bit silly in nature, but an interesting look at what happens when you go to the extreme. I especially enjoyed the integration of personal tracking in the piece.

The Future of Biosensing Wearables by Rock Health. Our friends at Rock Health did some great research on where personal sensing is going.

Show&Tell
Analyzing Squash Performance Using Fitbit by Ben Sidders. Ben sought out to see if he could learn anything from his step data to improve his squash playing. In this post he explains how he used R to access his data and plot it against his squash records, which he also records.

My Life As Seen Through Fitbit. Reddit user, VisionsofStigma, plots a year and a half of Fitbit data to find out what is related to the rise and fall of his activity.

Visualizations
histogramFitbitFreeing My Fitbit Data by Bonnie Barrilleaux. Bonnie used our instructions for accessing Fitbit data in Google Spreadsheets then used Python to visualize her data. I especially like the histogram pictured at left. If you want to visualize your Fitbit data, she’s included her code in the post.

 

 

 

diurnalFitbitDiurnal Plot of Fitbit Data by Matthew Gaudet. Matthew was inspired by the diurnal plots from Stephen Wolfram’s in-depth personal data review. He implemented the same methods to better understand his activity.

 

 

 

iconHistoryIconic History by Shan Huang. As part of a the Interactive and Computational Design class at Carnegie Mellon University, Shan created a Chrome browser extension that visualizes your browser history. More about the project here.

 

 

 

lastfmAlbumsVisualizing Last.fm History by Andy Cotgreave. Andy has been using Last.fm since 2006 to track his music listening activity. As a data scientist he was interested in what he could learned from all that data. In this four-part series, he explores his data along side data from eight of his friends. (If you want explore your Last.fm data you can export it using this awesome CSV export tool.)

 

 

From the Forum
23andMe Data Analysis Tools

Track Your Phone Addiction

Just Want To Track Hours in Bed

Want to receive the weekly What We Are Reading posts in your inbox? We’ve set up a simple newsletter just for you. Click here to subscribe

Posted in What We're Reading | Tagged , , , | Leave a comment

How to Map Your Moves Data

In the Quantified Self community we focus on projects and ideas that help people access and get meaning out their personal data, including the information you can collect with your smartphone. If you have an iPhone, Android, or Windows phone you’re already have carrying of the world’s most sophisticated self-tracking tools. The GPS, accelerometer, the microphone, all of these tiny sensors make up a great set of tools you can use to understand how you move around the world.

I’m going to focus this short “how to” on geolocation data and mapping your movement, specifically using data gathered by the Moves application. Moves is a passive activity and location tracking tool available for the iPhone and Android. We’ve written a bit about it in the past and had a chance to interview their CEO, Sampo Karjalainen. I’ve been using it since May, 2013 and I wanted to share some neat tools and methods for getting a bit more out of the data Moves collects.

I find that visualizing my data on a map to be incredibly powerful. It might by my inner cartographer, but seeing my patterns of movement (or lack there of) in reference to known places and landmarks is a great mechanism for inducing recall and reflection on where I’ve been and what I’ve done. Hopefully you’ll use one of the tools or methods below to map you data and learn something new!

Moves Connected Apps
Like many self-tracking applications and devices, Moves has a API that many different developers have built services on top of. Here are just a few of the services that allow you to see your data on a map. Be advised that each of these services has access to your data. Make sure to read their Terms of Service before agreeing to the data transfer.

WebTrack. This is by far the most utilitarian data mapping tool. However, you shouldn’t get discouraged by the lack of fancy design because it gives you an very unique data view. When you use Moves on your phone you typically only see the “storyline” and the detected places you’ve spent time at. However, Moves is constantly pinging and recording your location when it detects movement. WebTrack allows you to see all those movement points by hovering over the associated timestamp.

WebTrack_MovesMap

Fluxtream. You might know Fluxtream as Friend of QS and a great open-source data aggregation tool. They’ve set up a “Moves Connector” that allows you to import and visualize your Moves data. Because Fluxtream is set up as an aggregation and visualization tool you can also map other interesting data sets. Want to know where you were tweeting last week? Fluxtream will map it for you. (You can see me tweeting on a CalTrain ride between San Francisco and Palo Alto below.)

Fluxtream_MovesMap2

Zenobase. Another interesting data aggregation service here. Zenobase treats your Moves data bit differently. Rather than importing all the movement geolocation data it focuses on your place data and visualizes those locations. I like the high-level view it start with, but make sure to keep zooming in to see more specific place data.

Zenobase_MovesMap

Resvan Maps. This mapping application adds a unique twist to the typical mapping visualizations. It will plot your places, paths, and categorize paths depending on the activity (transport, walking, running, and cycling). Additionally, you can create “analysis cirlces” and have the application compute the time you spent in a certain location you bound (it aggregates to hours:minutes per day).

Resvan_MovesMap

MMapper. This method for mapping your data, developed by Nicholas Felton, is by far the most technical, but it produces some really neat visualizations. You’ll have to download Processing and follow the instructions Nicholas provides on the Github repository page here. The great thing here is that the mapping and data access is all happening locally.

MMapper_MovesMap

Move-O-Scope. Another great mapping application here from the folks at Halftone.co. They’ve probably completed the most thorough mapping and exploration tools for your Moves data. After linking your Moves account you can explore maps by activity type, day of the week, and custom data ranges. Additionally, they’ve implemented a neat feature for exploring place data. You can see how many times you’ve visited a specific place, where you’ve come from and where you go next, what days you typically visit, and your typical time of day at that place. (See this post for background on why they created this nifty tool.)

MoveOScope

Map It Yourself!
If you don’t want to trust your data to a third party, but you still want to explore your movement maps there is really great option for you. Our friend and co-organizer of the QS LA Meetup, Eric Blue, recently published a method for easily exporting your data: the Moves CSV Exporter. You’ll have to login and use the Moves pin system in order to download your data, but Traqs isn’t storing your data, just providing a way for you to access it. The tool allows you to download and explore your activity, summary, tracks and place data. We’ll focus on the place data for creating maps. You can also use your full tracks history for mapping all the geolocation points Moves collects.

Because this data is based on latitude/longitude coordinates there are many different methods available that you can use to map your data. I’m going to focus on two here: Google Fusion Tables and CartoDB (if you know of others share them in the comments or our forum).

Google Fusion Tables
Fusion Tables are a new Google Drive tool that you can use to store, analyze, and visualize many different types of data. Once you download your Moves places.csv file you can upload it to a new Google Fusion Table. Once you upload your data, which takes about 2 minutes, you’ll see a menu bar and three tabs: Rows, Cards, Map of longitude. Just click on the “Map” tab and you’ll see your data already placed on a map. If you want to see a heatmap rather than a point map just navigate to Tools -> Change Map and you’ll see an option for a heatmap on the lefthand side. This is just the tip of iceberg for mapping fusion table data. You can learn more about different mapping methods and tricks here.

MovesPointMap
MovesHeatMap

CartoDB
CartoDB is a visualization and analysis engine for geospatial data. I’ve been using it to play around with a few of the different geolocation datasets that I have (I actively keep three). Although it is paid service, they do offer a free plan for smaller datasets, which is perfect for your Moves data. Again, you’ll have to upload your places.csv file to a new table once you set up your account. Once the data is uploaded there are quite a few different map visualization wizards you can use to view your data in different ways. Pesonaly I like playing with the “Torque” visualization that gives you a real feeling of space-time to your data.

CartoDB_MovesMap

TileMill
TileMill is an interactive map design tool from the folks over at Mapbox. If you’re looking to create custom maps with your data that you can format, style, and share then this is a wonderful tool to use. At first glance it’s a little daunting because it looks like a mashup of a CSS editor and map tool. That actually gives it the unique power to drive customization. Don’t be afraid, it’s not too hard to get started with. Mapbox has provided a great “crashcourse” to get you started with importing data, saving it as a new layer on your map, and then manipulating how it looks on your screen. If you want to go just a bit farther you can also add legends and informative popups to describe your data points. Mapbox also offers a free hosting plan if you want to share your interactive maps on a webpage. For example check out my MovesMap here, where I added a quick styling to manipulate the point size in relation to the time spent at a location.

(Update 4/24/14: Mapbox has posted an excellent how-to for mapping Moves data with the new official Moves export data function. Check it out here.)

TileMill_MovesMap

Hopefully you’ve learned something new from this. If you map your Moves data (or any other geolocation data) we want to see it! Leave a link in the comments, post it in the location mapping thread on the QS Forum or get in touch on twitter!

Posted in QS Resource | Tagged , , , , , , | 4 Comments

Enrico Bertini on Tracking Focused Work

We’ve all come face to face with tracking some aspect of our life only to realize that we’re not quite sure how to get started. Enrico Bertini encountered this roadblock when he began thinking about tracking the amount of time he spends engaging in “focused work.” As an information visualization researcher at NYU he decided on a simple rule that would give him the most accurate data that represented his interests: if it wasn’t tracked then it wasn’t focused work. In this talk, given at the New York QS meetup group, Enrico explains his process and shares his findings (including some great visualizations).

Slides available here.

(Editor’s Note: Enrico also co-hosts a great podcast on data visualization and information design called Data Stories. I highly recommend listening. If you’re looking for a place to start try Episode 17: Data Sculptures.)

Posted in Videos | Tagged , , , , | 1 Comment

Jana Beck on Visualizing Her Diabetes Data

As you may know, we get excited when someone in our community uses interesting data visualizations to help tell their self-tracking story. Jana Beck is no exception. As a woman living with Type 1 diabetes she’s constantly learning how to better understand what her Dexcom data is telling her. In this talk, Jana follows up on her previous show&tell presentation with some new visualization techniques she’s using. If you’re interested in Jana’s methods be sure to check out her Github repository and her work with Tidepool.org.

(Editor’s Note: I very interested in Jana’s use of Chernoff faces for multivariate data visualization. If you’re using this type of visualization for your own data I would love to see it. Get in touch.)

Posted in Videos | Tagged , , , , , , | 1 Comment

QS Gallery: David El Achkar

We thought it would be nice to post David’s 138 days of activities visualization here. Make sure to watch his talk from the 2013 QS Global Conference to learn how he created this and what he’s learned from tracking his time.

This is my life during the past six months. Each square = 15 minutes. Each column = 1 day. This picture represents 138 days or 3,000+ activities.
- David El Achkar

Posted in QS Gallery | Tagged , , , , | Leave a comment

QS Gallery: Doug Kanter

Doug Kanter shared this beautiful and unique visualization of his blood glucose with us. Be sure to take a peak at his other great visualizations and his wonderful talk at the 2013 Quantified Self Global Conference.

This is a visualization of one month of my blood sugar readings from October 2012. I see that my control was generally good, with high blood sugars happening most often around midnight (at the top of the circle).
-Doug Kanter

Posted in QS Gallery | Tagged , , , , | Leave a comment

Doug Kanter: A Year Of Diabetes Data

The complex relationship between behavior and diabetes control has long been a testing ground for gathering and making sense of personal data. Doug Kanter is a Type-1 diabetic who’s been thinking about how self-tracking influences his diabetes control for a few years. While in graduate school at the Interactive Telecommunications Program (ITP) at NYU he started experimenting with visualizations that helped him understand his blood sugar and insulin dosing. In 2012 he began adding more data to his exploration in order to better understand how diet played a role in his diabetes self-management. Watch this great talk to learn more about Doug’s journey and his ongoing Databetes project.

We’ll be posting videos from our 2013 Global Conference during the next few months. If you’d like see talks like this in person we invite you to join us in Amsterdam for our 2014 Quantified Self Europe Conference on May 10 and 11th.

Posted in Conference, Videos | Tagged , , , , , , | Leave a comment

QS Gallery: Bob Troia

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! :)

Tools: Moves; Moves Mapper

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

Posted in QS Gallery | Tagged , , , , , , | Leave a comment