Tag Archives: data visualization

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

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.

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!

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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.)

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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.)

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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

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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

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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.

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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

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QS Gallery: Anita Lillie

Our first post in our new ongoing QS Gallery Series comes to us from Anita Lille, a member of our Bay Area QS community and fantastic data visualization designer.


click for larger image

This is concatenation of screenshots from my sleep app. Most sleep apps don’t let you zoom out like this and still see daily/nightly detail, so I just made it myself. I like that it shows how almost-consistent I am with my sleep, and made me ask new questions about the “shape” of a night of sleep for me.

Tool: Azumio Sleep Time

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

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Peter Denman on Visualizing Data with Biomimicry

Biomimicry is an interesting topic and one that we’ve started to see creep into our Quantified Self tools and visualizations. While recovering from surgery, Pete Denman, an interaction designer at Intel, became inspired to start to explore biomimicry as a way to show data. In this short Ignite talk from our 2013 European Conference, Pete talks about  his inspiration and how he’s begun testing and learning about using “beautiful mathematics” to explore visualizing data.

Update:
Thanks to Gary Wolf, we were able to find a great presentation delivered by Pete that provides a bit more detail on his excellent work:

 

This talk was filmed at our 2013 Quantified Self European Conference. We hope that you’ll join us this year for our 2013 Global Conference where we’ll have great talks, sessions, and discussions that cover the wide range of Quantified Self topics. Registration is now open so make sure to get your ticket today!

 

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Doug Kanter on Data, Diabetes, and Marathon Training

Doug Kanter has been a Type 1 diabetic for 26 years. Through this time he’s come to learn more about his disease by using many data-gathering tools and his own work in visual analysis at the NYU ITP program. We’ve featured Doug’s compelling work here on the blog before and we were excited to hear him talk at the NY QS Meetup about his new project to understand how marathon training and running effect his blood sugar and insulin treatment.

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