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Chapter 1. Seeing Your Life in Data

Nathan Yau

IN THE NOT-TOO-DISTANT PAST, THE WEB WAS ABOUT SHARING, BROADCASTING, AND DISTRIBUTION. But the tide is turning: the Web is moving toward the individual. Applications spring up every month that let people track, monitor, and analyze their habits and behaviors in hopes of gaining a better understanding about themselves and their surroundings. People can track eating habits, exercise, time spent online, sexual activity, monthly cycles, sleep, mood, and finances online. If you are interested in a certain aspect of your life, chances are that an application exists to track it.

Personal data collection is of course nothing new. In the 1930s, Mass Observation, a social research group in Britain, collected data on various aspects of everyday life—such as beards and eyebrows, shouts and gestures of motorists, and behavior of people at war memorials—to gain a better understanding about the country. However, data collection methods have improved since 1930. It is no longer only a pencil and paper notepad or a manual counter. Data can be collected automatically with mobile phones and handheld computers such that constant flows of data and information upload to servers, databases, and so-called data warehouses at all times of the day.

With these advances in data collection technologies, the data streams have also developed into something much heftier than the tally counts reported by Mass Observation participants. Data can update in real-time, and as a result, people want up-to-date information.

It is not enough to simply supply people with gigabytes of data, though. Not everyone is a statistician or computer scientist, and not everyone wants to sift through large data sets. This is a challenge that we face frequently with personal data collection.

While the types of data collection and data returned might have changed over the years, individuals' needs have not. That is to say that individuals who collect data about themselves and their surroundings still do so to gain a better understanding of the information that lies within the flowing data. Most of the time we are not after the numbers themselves; we are interested in what the numbers mean. It is a subtle difference but an important one. This need calls for systems that can handle personal data streams, process them efficiently and accurately, and dispense information to nonprofessionals in a way that is understandable and useful. We want something that is more than a spreadsheet of numbers. We want the story in the data.

To construct such a system requires careful design considerations in both analysis and aesthetics. This was important when we implemented the Personal Environmental Impact Report (PEIR), a tool that allows people to see how they affect the environment and how the environment affects them on a micro-level; and your.flowingdata (YFD), an in-development project that enables users to collect data about themselves via Twitter, a microblogging service.

For PEIR, I am the frontend developer, and I mostly work on the user interface and data visualization. As for YFD, I am the only person who works on it, so my responsibilities are a bit different, but my focus is still on the visualization side of things. Although PEIR and YFD are fairly different in data type, collection, and processing, their goals are similar. PEIR and YFD are built to provide information to the individual. Neither is meant as an endpoint. Rather, they are meant to spur curiosity in how everyday decisions play a big role in how we live and to start conversations on personal data. After a brief background on PEIR and YFD, I discuss personal data collection, storage, and analysis with this idea in mind. I then go into depth on the design process behind PEIR and YFD data visualizations, which can be generalized to personal data visualization as a whole. Ultimately, we want to show individuals the beauty in their personal data.

Personal Environmental Impact Report (PEIR)

PEIR is developed by the Center for Embedded Networked Sensing at the University of California at Los Angeles, or more specifically, the Urban Sensing group. We focus on using everyday mobile technologies (e.g., cell phones) to collect data about our surroundings and ourselves so that people can gain a better understanding of how they interact with what is around them. For example, DietSense is an online service that allows people to self-monitor their food choices and further request comments from dietary specialists; Family Dynamics helps families and life coaches document key features of a family's daily interactions, such as colocation and family meals; and Walkability helps residents and pedestrian advocates make observations and voice their concerns about neighborhood walkability and connections to public transit[1] All of these projects let people get involved in their communities with just their mobile phones. We use a phone's built-in sensors, such as its camera, GPS, and accelerometer, to collect data, which we use to provide information.

PEIR applies similar principles. A person downloads a small piece of software called Campaignr onto his phone, and it runs in the background. As he goes about his daily activities—jogging around the track, driving to and from work, or making a trip to the grocery store, for example—the phone uploads GPS data to PEIR's central servers every two minutes. This includes latitude, longitude, altitude, velocity, and time. We use this data to estimate an individual's impact on and exposure to the environment. Environmental pollution sensors are not required. Instead, we use what is already available on many mobile phones—GPS—and then pass this data with context, such as weather, into established environmental models. Finally, we visualize the environmental impact and exposure data. The challenge at this stage is to communicate meaning in data that is unfamiliar to most. What does it mean to emit 1,000 kilograms of carbon in a week? Is that a lot or is that a little? We have to keep the user and purpose in mind, as they drive the system design from the visualization down to the data collection and storage.



[1] CENS Urban Sensing, http://urban.cens.ucla.edu/

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