Using GPS to learn significant locations and predict movement across multiple users Daniel Ashbrook, Thad Starner College Of Computing, Georgia Institute of Technology, From Personal and Ubiquitous Computing, vol 7, no 5,

Preface Early work in this area Very simple method with 324 citation!! Both authors’ magnum opus And the papers cite it also have large number of citation 2

Outline Introduction Previous work The pilot study The Zürich study Application Future work Conclusion Comments 3

Outline Introduction Previous work The pilot study The Zürich study Application Future work Conclusion Comments 4

Introduction Wearable computers as intelligent agents assist the user in a variety of tasks Location is the most common context to determine the users’ tasks We present a system that automatically find the significant locations Create user models to predict movement 5

Outline Introduction Previous work The pilot study The Zürich study Application Future work Conclusion Comments 6

Previous Work Sparacino used infrared beacons to create individual models of museum visitors Liu and Maguire describe a generalized network architecture that incorporated prediction with the goal of supporting mobile computing Above using fixed sensors, but systems using GPS must determine which locations are significant 7

Previous Work Researches using GPS Wolf used stopping time to mark the starting and ending points of trip Marmasse and Schmandt used the loss of GPS signal to detect buildings 8

In This Research The goal: Construct a system to record and model an individual’s travel and predict on different scale – Answer the query like “Where is Daniel most likely to go after work?” Conducted two studies. – In 2001, a pilot study with single user in four months – In 2002, six users in seven months 9

Outline Introduction Previous work The pilot study The Zürich study Application Future work Conclusion Comments 10

The Pilot Study - data One user for a period of four months GPS receive rate is once per second – Valid signal and moving at one mile per hour at least The user traveled in and around Atlanta 11

The Pilot Study - methodology Find significant places Clustering places into locations Learning sublocations Prediction Definition: place = gps points which are significant location = clustered places 12

Finding significant places Latitude and longitude are useless “Home” and “Work” are meaningful The logical way to finds significant points is to look at – Where the user spends her/his time – Significant locations will be inside buildings(no GPS signal) We define a “place” with an interval of time t between it and the previous point 13

Finding significant places We need to decide what value of time t There is no clear point on the graph to choose Use 10 minutes as stopping time 14

Clustering places into locations Because of the erroneous GPS measurements, the logger won’t record exactly the same point even the user stops Use K-means to cluster the places – place list and a radius until no place remained Every cluster denotes a “location”, and is assigned a unique ID 15

16

Clustering places into locations So we need to decide the radius Too small or too large may cause problem We run our clustering algorithm several times with varying radius And find the “knee” point – Decide next n and threshold? Knee point: For each point on the graph, we find the average of it and the next n points on the right. If the current point exceeds the average by some threshold, we use it as the knee point. 17

18

Learning sublocations We subsume smaller-scale paths From city-wide scale to campus-wide scale Taking the points within each “location” and running the same clustering algorithm If a knee exists, it forms a sublocation 19

Prediction We substitute for each place the ID of the location it belongs to Markov model is created for each location with transition probability – Node is location, edge is transition probability First order and second order… nth order – Quantity of second order is small… 20

A BC Edge - transition It’s first order Markov model Compare the path’s relative frequency to the probability that the path was taken by chance (Monte Carlo simulation) 21

Outline Introduction Previous work The pilot study The Zürich study Application Future work Conclusion Comments 22

The Zürich study – data Conducted a second study in Zürich, Switzerland with multiple users Six users during seven months Unfortunately, one user broke the cabling for his unit and was unable to collect any data at the beginning Total 800,000 data points 23

The Zürich study – methodology Find places using time t – But now we register a place when signal is lost Cluster places to locations Markov model Place 1Place 2 Time spend between current point and previous point When signal is lost May contain park, gym, and building Only building Time t is hard to decideWe think people will tend to visit a few places often 24

Place 1 Place 2 Fewer places Place 2 Fewer places 25

The Zürich study – evaluation We present two results – The correlation between the names assigned to locations by users – Even in a different environment, the prediction generates consistent results 26

Naming across users Asked each user to give names to each location we found To see if the locations we found are common places to the users – Ex. If all user give location A the same name Of the five users, three had 11 locations within Zürich, one had 9 and one had 6 (it just express the time period they stay effect the number of locations) 27

Prediction User 1 User 2 Monte Carlo simulation result 28

Outline Introduction Previous work The pilot study The Zürich study Application Future work Conclusion Comments 29

Application Single-user application : – To-do list with location reminder – Detect situation Multi-user application (share user’s model) 1.Answer social query like “Will I see Daniel today?” 2.Schedule a meeting (time & location) for several people 3.Make a serendipitous meeting with friends 4.Do me a favor 30

Outline Introduction Previous work The pilot study The Zürich study Application Future work Conclusion Comments 31

Future Work Support time prediction Our model takes long time to update – Weighting update Online learning Suggest names for a location To-do application Combine two similar users’ location 32

Outline Introduction Previous work The pilot study The Zürich study Application Future work Conclusion Comments 33

Conclusion We develop an algorithm to extract significant places and locations Predictive model demonstrated patterns of movement that occurred much more frequently than chance 34

Outline Introduction Previous work The pilot study The Zürich study Application Future work Conclusion Comments 35

Comments Concept is simple Few experiment with poor evaluation The method they proposed becomes a standard scenario for location-based activity recognition The early bird catches the worm Give many application I can’t see any color 36

Recently research Most of them are devoted to extract significant locations using either GPS or other sensor then predict the movement or the activity Once we have the place and activity information, we can answer social query like “Who will I meet in lab on Friday morning?” But no more detail about users’ character Maybe we can classify the user into category and guess who have the same interest or in the same college or … It seems like an application rather than research… 37