Tag Archives: moves

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.


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.)


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.


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).


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.


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.


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.


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.


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

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

Toolmaker Talk: Sampo Karjalainen (Moves)

Today we are happy to bring you another interview in our Toolmaker Talk series. We had the great pleasure of speaking with Sampo Karjalainen, the designer and founder of Moves. Over fifty percent of U.S. adults have a smartphone. That’s a lot of people walking around with a multi-sensored computer in their pockets. Moves is another example of how developers and designers are focusing on the smartphone as a Quantified Self tracking and experience tool. This is an exciting space, and one we intend to keep a close eyes on moving forward.

Watch our conversation or listen to the audio (iTunes podcast link coming soon!) then read below to learn more about Sampo and the Moves app.

How do you describe Moves? What is it?
sampo-200x200Moves is an effortless activity tracker. It’s a bit like Fitbit or Jawbone UP, but in your smartphone. There’s no need to buy, charge and carry one more device. In addition to steps and active minutes, the app also automatically recognizes activity type: walking, running, cycling or driving. It also shows routes and places and builds ‘a storyline’ of your day. It helps you remember your days and see which parts of your day contribute to your physical activity. It’s a simple, beautiful app that hasn’t existed before.

What’s the backstory? What led to it?
We started Moves to motivate us to move more. Aapo Kyrola was doing his Ph.D at Carnegie Mellon University, working hard, gaining weight and lacking the motivation to exercise. We began discussing how to motivate people like Aapo to move more. The first prototype used game motivations: we had badges, leaderboards and virtual pet to motivate people. The problem was that they still had to remember to start and stop tracking. We quickly learnt that people didn’t remember to use it for everyday walks. That made us think that maybe we could make it work continuously in the background. It took plenty of R&D to find a way to minimize battery use while still collect enough data to recognize activity types and places correctly.

What impact has it had? What have you heard from users?
We’re seeing that when you make activity visible, people start to think about it. And when they think about it, they start to do small changes in their lives. They may park their car a bit further or consider biking instead of car. They may choose to walk just to get some steps and take a break from everyday hurries. It also helps people see how long it takes to travel between places and how much they actually use time in different places.

What makes it different, sets it apart?storyline-us
Other phone-based trackers are good for tracking one run or one biking event. Moves is made to track all-day activity. Compared to activity gadgets, Moves recognizes activities by type, recognizes places and shows routes. It’s collecting a new type of a dataset that hasn’t been available before. And best of all, we now have a public API, so you can use your data as you like!

What are you doing next? How do you see Moves evolving?
Currently we’re busy with the Android version of Moves and adding some features to the iPhone version. Over time we see that Moves will become a tool to understand not only your physical activity, but also your use of time, travels – your life in general.

Anything else you’d like to say?
Moves is collecting your location in time and space. It’s a great ‘backbone’ for connecting all kinds of other data. We’re excited to see what type of visualizations and mashups people create!

Product: Moves
Website: moves-app.com
Price: Free

This is the 20th post in the “Toolmaker Talks” series. The QS blog features intrepid self-quantifiers and their stories: what did they do? how did they do it? and what have they learned?  In Toolmaker Talks we hear from QS enablers, those observing this QS activity and developing self-quantifying tools: what needs have they observed? what tools have they developed in response? and what have they learned from users’ experiences? If you are a toolmaker and want to participate in this series please contact Ernesto Ramirez.

Posted in Toolmaker Talks, TTPodcast | Tagged , , , , , , | 1 Comment