O'Reilly logo

Beautiful Data by Toby Segaran, Jeff Hammerbacher

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Data Processing

Data processing is the important underpinning of the personal data collection system that users almost never see and usually are not interested in. They tend to be more interested in the results of the processing. This is the case for YFD. PEIR users, on the other hand, benefit from seeing how their data is processed, and it in turn affects the way they interpret impact and exposure.

The analytical component of PEIR consists of a series of server-side processing steps that start with GPS data to estimate impact and exposure. To be precise, we can divide the processing into four separate phases[2]

  1. Trace correction and annotation: Where possible, the error-prone, undersampled location traces are corrected and annotated using estimation techniques such as map matching with road network and building parcel data. Because these corrections and annotations are estimates, they do carry along uncertainties.

  2. Activity and location classification: The corrected and annotated data is automatically classified as traveling or stationary using web services to provide a first level of refinement to the model output for a given person on a given day. The data is also split into trips based on dwell time.

  3. Context estimation: The corrected and classified location data is used as input to web-based information sources on weather, road conditions, and aggregated driver behaviors.

  4. Exposure and impact calculation: Finally, the fine-grained, classified data and derived data is used as input to geospatial data sets and microenvironment models that are in turn used to provide an individual's personalized estimates.

While PEIR's focus is still on the results of this four-step process, we eventually found that users wanted to know more about how impact and exposure were estimated. So for each chunk of data we provide details of the process, such as what percentage of time was spent on a freeway and what the weather was like around where the user was traveling. We also include a detailed explanation for every provided metric. In this case, transparency in the estimation process allows users to see how their actions have an effect on impact and exposure rather than just knowing how much or how little they are polluting their neighborhood. There is, of course, such a thing as information overload, so we are careful in how much (and how little) we show. We address much of these issues in the next section.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

Start Free Trial

No credit card required