mining interesting locations and travel sequences from gps trajectories

ining Interesting Locations and Travel Sequences from GPS Trajectories

Yu Zheng, Lizhu Zhang, Xing Xie, Wei-Ying Ma Microsoft Research Asia

MJohnson Chin-Hui Chen 20090923 Seminar

Agenda! Introduction! Overview of the System! Modeling Location History! Location Interest Inference! Experiments! Related Work! Future Work

INTRODUCTION! GPS-enabled devices, like GPS-phones, are

changing the way people interact with the Web by using locations as contexts.

! Users record their outdoor movements because…! Travel experience sharing ! Life logging ! Sports activity analysis! Multimedia content management

INTRODUCTION

! Websites or forums: geo-related Web communities! Bikely

(www.bikely.com)! GPS exchange

(www.gpsxchange.com)! @trip

(www.a-trip.com)!

(map.answerbox.net)

INTRODUCTION! Although there are many raw GPS data…

! Without much understanding! It’s impossible to browse each GPS trajectory one by one

INTRODUCTION! Goal :

! Mine the top n interesting locations! Mine the top m classical travel sequences! Mine the most k experienced users in a geo-related

communityCulturally important places(Statue of Liberty in NY) Commonly frequented public areas (shopping streets)

INTRODUCTION! Difficulty :

! What is a location? (geographical scales)! The interest level of a location

! not only frequency or counts! but also lie in these users’ travel experiences

! How to determine a user’s travel experience?! The location interest and user travel

! are region-related(conditioned by the given geospatial region)

! are relative value (Ranking problem)(not reasonable to judge whether or not a location is interesting)

OVERVIEW OF THE SYSTEM! Preliminary

! Clarify some terms! Architecture! Application

! GeoLife 2.0 since Oct. 2007

OVERVIEW OF THE SYSTEM Preliminary

! GPS logs P and GPS trajectory

! Stay points S = {s1, s2,…, sn}.! P = {pm,pm+1,…,pn} is a group of consecutive GPS points

S.lat = avg lat of P S.arvT = pm.T S.lngt = avg lngt of P S.levT = pn.T

p4

p3

p5

p6

p7

A Stay Point S

p1

p2

Latitude , Longitude , Timep1: Lat1, Lngt 1, T1p2: Lat2, Lngt 2, T2

………...pn: Latn, Lngtn, Tn

OVERVIEW OF THE SYSTEM Preliminary! Location history :

! represented by a sequence of stay points! with transition intervals

! Tree-Based Hierarchy H :! H = (C,L)! L = {ʅ1 , ʅ2 , … , ʅn} ! C = {Cij| }

! jth cluster on level ʅi! Ci : level ʅi clusters

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Stands for a stay point SStands for a stay point cluster cij

{C }High

Low

Shared Hierarchical Framework

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OVERVIEW OF THE SYSTEM Preliminary! Tree-Based Hierarchical Graph (TBHG)

! TBHG = (H,G)! H = Tree-Based Hierarchy ! G={gi = (Ci,Ei), } l1

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G1

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OVERVIEW OF THE SYSTEM Architecture

Offline

Offline

OVERVIEW OF THE SYSTEM Application

MODELING LOCATION HISTORY

MODELING LOCATION HISTORYGPS Logs of

User 1GPS Logs of

User 2GPS Logs of

User nGPS Logs of

User iGPS Logs of

User i+1GPS Logs of

User n-1


{C }High

Low


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1. Stay point detection

2. Hierarchical clustering

l1

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G1

G2

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3.Graph Building

GPS Logs of User 1

GPS Logs of User 2

GPS Logs of User n

GPS Logs of User i

GPS Logs of User i+1

GPS Logs of User n-1


{C }High

Low


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1. Stay point detection

2. Hierarchical clustering

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3.Graph Building

MODELING LOCATION HISTORY

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LOCATION INTEREST INFERENCE

LOCATION INTEREST INFERENCE! 1. Basic concepts of HITS! 2. HITS-Based Inference Model! 3. Mining Classical Travel Sequences

LOCATION INTEREST INFERENCE Basic concepts of HITS! A search-query-dependent ranking algorithm.! query -> a list -> Hub/Authority ranking

LOCATION INTEREST INFERENCE HITS-Based Inference Model

l1

G3

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G2

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l3Users: Hub nodes

Locations: Authority nodes

Mutual reinforcement relationshipA user with rich travel knowledge are more likely to visit more interesting locations.A interesting location would be accessed by many users with rich travel knowledge.

LOCATION INTEREST INFERENCE HITS-Based Inference Model! Difficulty : region-related

! aligned with the query-dependent property of HITS! But online selection is time consuming …! Using the regions specified by their ascendant clusters

! a location have multiple authority scores based on the different region scales it falls in.

! a user have multiple hub scores conditioned by the regions of different clusters.

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G2

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LOCATION INTEREST INFERENCE HITS-Based Inference Model! Location Interest :

! Authority scores (cij)

: the auth scores of cij based on the region specified by its ascendant nodes on level ,where

! User Travel Experience :! Hub scores ( )


{C }High

Low


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c20 c21

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LOCATION INTEREST INFERENCE HITS-Based Inference Model

33

{C }Ascendant

Stands for a stay point cluster cij

{C }

Descendant

A region specified by a userStands for a cluster that covers the region specified by the user

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A) A region covering locations from single parent cluster

B) A region covering locations from multiple parent clusters

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Stands for a stay point cluster cij

{C }

Descendant

A region specified by a userStands for a cluster that covers the region specified by the user

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A) A region covering locations from single parent cluster

B) A region covering locations from multiple parent clusters

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LOCATION INTEREST INFERENCE HITS-Based Inference Model! Inference :

! Build adjacent matrix M

! ! : uk has visited cluster cij

LOCATION INTEREST INFERENCE HITS-Based Inference Model! Mutual reinforcement relationship (matrix form):

! Conditioned by the region of cluster C11

LOCATION INTEREST INFERENCE Mining Classical Travel Sequences

37

• Three factors determining the classical score :– Travel experiences (hub scores) of the users taking the sequence– The location interests (authority scores) weighted by – The probability that people would take a specific sequence

: Authority score of location A

: Authority score of location C

: User k’s hub score

LOCATION INTEREST INFERENCE Mining Classical Travel Sequences

: Authority score of location A

: Authority score of location C

: User k’s hub score

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The classical score of sequence A!C:

EXPERIMENTS! Settings! Evaluation Approaches! Result! Discussions

EXPERIMENTSSetting! GPS Devices

! Coordinates every two seconds.! 107 users (M:F = 58:49) from May 2007 to Oct 2008.

EXPERIMENTSSetting! GPS Data – most parts were created in Beijing! 166,372 km! 5,081,369 GPS points

EXPERIMENTSSetting! Parameter Selection

! Stay point detection : ! Tthreh = 20 mins! Dthreh = 200m! Extract 10,354 stay points

! Clustering :! Use OPTICS (Ordering Points To Identify the Clustering

Structure)Capable of detecting irregular structures

EXPERIMENTSEvaluation Approaches! User study : 29 subjects (M:F = 15:14) , who have

been in Beijing for more than 6 years! Location : the fourth ring road of Beijing ( )

EXPERIMENTSEvaluation Approaches! 2 aspects of evaluations

! Presentation (ability of the retrieved interesting locations)! Representative : How many locations in this retrieved set are

representative of the given region (0-10) ?! Comprehensive : Do these locations offer a comprehensive view

of the given region (1-5) ?! Novelty : How many locations in this retrieved set have

interested you even though they only appeared recently(0-10) ? ! Rank (ranking performance)

EXPERIMENTSEvaluation Approaches

! Select the mode of ratings

EXPERIMENTSEvaluation Approaches! Baselines :

! Mining interesting locations : ! Rank-by-count! Rank-by-frequency

! Mining classical travel sequences : ! Rank-by-count! Rank-by-interests

Consider interests of the locations in a sequence! Rank-by-experience

Consider experiences of the users who have taken this sequence

EXPERIMENTSEvaluation Approaches

EXPERIMENTS Result! Results Related to Interesting Locations

! Presentation ability

only 2.4>2.2 doesn’t pass T-test (p>0.2).! Ranking ability

! There are 60% overlaping (ours vs rank-by-count) , but show effectively ranking.

EXPERIMENTS Result! Results Related to Classical Sequences

! Classical rate : the ratio of sequences with a score of 2 in the set.

! Combine …! user’s travel experiences + rank-by-counts : improved! locations interests + rank-by-counts : improved

EXPERIMENTS Discussions! About Interesting Locations

! Why Rank-by-count is bad ? ! Why Rank-by-frequency is bad ?

! About Classical Sequences! Only Rank-by-counts ?! Only individuals’ travel experiences ?! Only location interest ?

RELATED WORK! Mining Location History

! Mining individual location history ! Mining multiple users’ location histories1. Detecting significant locations of a user. [2004]2. Predicting the user’s movement among these

locations. [2005]3. Recognizing user-specific activities at each

location. [2003]

1. Mining similar sequences from users’ moving trajectories. [2007]

2. Propose a framework for retrieving maximum periodic patterns. [2004]

3. Predict where a driver may be going as a trip progresses. [2003]

4. Recognizing the social pattern in daily user activity. [2005]

RELATED WORK! Location Recommenders

! Recommenders based on real-time location! Recommender based on location history

1. Problem: Without understanding the individual and the nearby locations.

2. Filter away from the returned results the invisible entities occluded by building. [2007]

1. Recommend geographic locations like shops to users. [2006]

2. Proposed an enhanced collaborative filtering solution. [2006]

FUTURE WORK! Grouping users based on their histories.! Clustering locations in terms of people’s visits.

mining interesting locations and travel sequences from gps trajectories

Data & Analytics