Data collected from mobile phones have the potential to provide insight into the relational dynamics of individuals. This paper compares observational data from mobile phones with standard self-report survey data. We find that the information from these two data sources is overlapping but distinct. For example, self-reports of physical proximity deviate from mobile phone records depending on the recency and salience of the interactions. We also demonstrate that it is possible to accurately infer 95% of friendships based on the observational data alone, where friend dyads demonstrate distinctive temporal and spatial patterns in their physical proximity and calling patterns. These behavioral patterns, in turn, allow the prediction of individual-level outcomes such as job satisfaction.

Reality Mining defines the collection of machine-sensed environmental data pertaining to human social behavior. This new paradigm of data mining makes possible the modeling of conversation context, proximity sensing, and temporospatial location throughout large communities of individuals. Mobile phones (and similarly innocuous devices) are used for data collection, opening social network analysis to new methods of empirical stochastic modeling.

The original Reality Mining experiment is one of the largest mobile phone projects attempted in academia. Our research agenda takes advantage of the increasingly widespread use of mobile phones to provide insight into the dynamics of both individual and group behavior. By leveraging recent advances in machine learning we are building generative models that can be used to predict what a single user will do next, as well as model behavior of large organizations.

The paragraph is not an arbitrary construct nor was it an optional invention. It is required for written communication; else communicative writing would be relegated to individual words or unwieldy, enormous chunks of copy.

A session talking about what people can/are doing with proximity sensing (and how/why in many cases, locative services aren't about absolute (geo) location, but relative location. Covering implementation and code of Bluetooth in particular, including information on the setup I used at Where 2.0 and WhereCamp 2008. (The morning session already covered lots of concerns about privacy that are applicable here, so this session will focus on cool stuff you can do and how to get out there and do it)

Would you know if a dear, but seldom seen, friend happened to be on the same train as you? The proximeter is both an agent that tracks the past and future proximity of one’s social cloud, and an instrument that charts this in an ambient display. By reading existing calendar and social network feeds of others, and abstracting these into a glanceable pattern of paths, we hope to nuture within users a social proprioception and nudge them toward more face-to-face interactions when opportunities arise.

PEIR gives you greater control over your environmental impact and exposure by allowing you to interactively explore how it creates its results from your activity patterns.

For iPod touch with Maps, the Maps application provides your approximate location using information based on your proximity to known Wi-Fi networks (when on and available).

The API will give you access to Lonely Planet destination information, points of interest, and Bluelist “best-of” traveller recommendations that can be searched by destination name, geographic proximity, tag and keyword.

automated charting method as it emphasizes proportions while still approximating geographic relationships (spatial proximity) yet avoiding the abstract art distortion noise common in the fish eye method.