[Geowanking] Predicting destination based on GPS History - looking for references
sean at geoiq.com
Wed Sep 7 10:24:59 PDT 2011
There is also interesting related work in predicting friendship
networks from mobile devices:
Inferring Friendship Network Structure by Using Mobile Phone Data
Eagle, N., A. Pentland, and D. Lazer (2009).
Proceedings of the National Academy of Sciences of the United States
of America 106(36):15274-15278
Abstract: 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.
Keywords: engineering social systems, relational inference, social
network analysis, reality mining, relational scripts, informant
accuracy, social network
On Wed, Sep 7, 2011 at 1:15 PM, Edward Vielmetti
<edward.vielmetti at gmail.com> wrote:
> Here's a 2011 thesis
> and the title page and abstract
> Predicting Future Locations and Arrival Times of Individuals
> Ingrid E. Burbey
> This work has two objectives: a) to predict people's future locations,
> and b) to predict when they will be at given locations. Current
> location-based applications react to the user‘s current location. The
> progression from location-awareness to location-prediction can enable
> the next generation of proactive, context-predicting applications.
> Existing location-prediction algorithms predict someone‘s next
> location. In contrast, this dissertation predicts someone‘s future
> locations. Existing algorithms use a sequence of locations and predict
> the next location in the sequence. This dissertation incorporates
> temporal information as timestamps in order to predict someone‘s
> location at any time in the future. Sequence predictors based on
> Markov models have been shown to be effective predictors of someone's
> next location. This dissertation applies a Markov model to
> two-dimensional, timestamped location information to predict future
> This dissertation also predicts when someone will be at a given
> location. These predictions can support presence or understanding
> co-workers‘ routines. Predicting the times that someone is going to be
> at a given location is a very different and more difficult problem
> than predicting where someone will be at a given time. A
> location-prediction application may predict one or two key locations
> for a given time, while there could be hundreds of correct predictions
> for times of the day that someone will be in a given location. The
> approach used in this dissertation, a heuristic model loosely based on
> Market Basket Analysis, is the first to predict when someone will
> arrive at any given location.
> The models are applied to sparse, WiFi mobility data collected on PDAs
> given to 275 college freshmen. The location-prediction model predicts
> future locations with 78-91% accuracy. The temporal-prediction model
> achieves 33-39% accuracy. If a tolerance of plus/minus twenty minutes
> is allowed, the prediction rates rise to 77%-91%.
> This dissertation shows the characteristics of the timestamped,
> location data which lead to the highest number of correct predictions.
> The best data cover large portions of the day, with less than three
> locations for any given timestamp.
> On Wed, Sep 7, 2011 at 1:01 PM, Rich Gibson <rich.gibson at gmail.com> wrote:
>> Hi All,
>> I think I remember an MIT project where students and faculty were tracked,
>> and after
>> 30 days the system was able to predict the destination of the lab rats, er,
>> and faculty, based on their location.
>> I seem to remember that faculty could be predicted with >80% success,
>> students a
>> bit late.
>> Does anyone have a reference for this, and/or for other work on the subject?
>> Geowanking mailing list
>> Geowanking at geowanking.org
> Edward Vielmetti +1 734 330 2465
> edward.vielmetti at gmail.com
> Geowanking mailing list
> Geowanking at geowanking.org
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