Tag Archives: Maps
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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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.
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.)
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!
Today’s gallery image comes to us from Eric Jain. Eric is the creator of Zenobase a neat data aggregation and tracking system. He’s also been a great contributor to our community at meetups in Seattle, our conferences, and on the forum.
This map shows my outdoor trips in the Pacific Northwest since 2008. Red is driving, yellow is hiking or paddling. The map doesn’t just help me remember past trips, but also helps me decide what areas to explore next. The tracklogs were recorded with a Garmin GPS device, processed with a simple script and uploaded to Google Fusion Tables with additional meta data stored for each trip in my Zenobase account.
Where are you? A pretty easy question to answer. But, what about, “Where was I?” Not so easy to answer, especially when we start talking about periods of time more than a few days or weeks. Sure, we all have GPS running on our phones now. We can check in with Foursquare/Facebook/Path etc. to keep a log of locations, but that data is fragmented and only represents certain specific locations. What about paths? What would we learn if we knew more about how we traveled about our world?
Aaron Pareki is one of the founders of Geoloqi, a location-based services platform. He has also been tracking his location every 6 seconds for the last four years and he has created some amazing visualizations to better understand his movement:
You may think this is just a boring old map with some travel data layered on top, but what makes this map special is that there is no underlying geospatial data. The lines you see above are Aaron’s actual travel paths from his GPS data. Using this information you can easily see the well traveled roadways by finding the thicker lines. You can even quickly pick out freeways and interstates due to their high speed.
Aaron has a lot more visualizations of his GPS traces, but I’ll leave you with this neat video showing a timelapse of his minute-by-minute movement:
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.
From the New York QS Show&Tell group: Robert Rabinovitz, a design teacher at the Parsons New School of Design and a designer himself, mapped the 40-minute period on January 19, 2007 when he experienced his first brain seizure. He takes us through his gripping story, moment by moment, with images of what he saw that day. Robert is also writing a play, writing a research report and planning a film about his experience of survival. Watch the video below to see how the design process saved his life.
At our June Bay Area Quantified Self Show&Tell, Jim Keravala of Flaii gave us a brief tour of the mind map he developed using TheBrain. He spends 1-2 hours a day entering information into his virtual brain, and has recorded about 65,000 thoughts so far. He feels that the main benefit he gets from it is enhanced recall, which has given him an advantage in business situations. In the video below, he reveals that he has become very attached to the system he uses and doesn’t like to be away from it for more than a few hours at a time.
Bo Adler, a regular at Quantified Self Show&Tell meetups in the Bay Area, describes a mapping mashup he built for his naturalist friends who work with Outdoor Education groups. He wanted to capture their location from the pictures they are taking along the Pacific Crest trail, from Mexico to Canada. Find out what he learned from the power of location.
Would you like to see a heatmap of all your FourSquare check-ins?
Steven Lehrburger shows a mashup he built called Where Do You Go? at a recent New York City Quantified Self Show&Tell meetup. He combined Google Maps, the FourSquare API, and the GHeat heat mapping library to create surprising visualizations. With amusing audience brainstorming and even a “dance break” moment, this is a fun one.
A Stanford professor in Human-Computer Interaction and Quantified Self advisor on data visualization, Heer and his colleagues Mike Bostock and Vadim Ogievetsky have put together a terrific guide to the various kinds of data visualization, and when and how to use each one.
They call their guide A Tour through the Visualization Zoo:
“In 2010 alone we will generate 1,200
exabytes–60 million times the content of the Library of Congress. Within
this deluge of data lies a wealth of valuable information on how we
conduct our businesses, governments, and personal lives. To put the
information to good use, we must find ways to explore, relate, and
communicate the data meaningfully…
Well-designed visual representations can replace cognitive calculations
with simple perceptual inferences and improve comprehension, memory, and
decision making. By making data more accessible and appealing, visual
representations may also help engage more diverse audiences in
exploration and analysis…
Creating a visualization requires a number of nuanced judgments. One
must determine which questions to ask, identify the appropriate data,
and select effective visual encodings to map data values to
graphical features such as position, size, shape, and color.”
Stops along the tour include Time-Series Data, Statistical Distributions, Maps, Hierarchies, and Networks. Each one is broken down into subtypes, with helpful examples that can be applied to your own dataset.
The authors end with a challenge:
“As you leave the zoo and head back into the wild, try deconstructing the
various visualizations crossing your path. Perhaps you can design a
more effective display?”
His list includes blogs on statistics, visualizations, maps, design, and “others worth noting,” a category that includes our own Quantified Self blog. Thanks Nathan! (Adding Nathan’s blog to the list makes 38.)