TDMA'11 workshop overview

Feng Lu The State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China, 100101 luf@lreis.ac.cn Xing Xie Microsoft Research Asia, Microsoft Building 2, No. 5, Danling Street Beijing, China, 100080 xingx@microsoft.com Shih-Lung Shaw Department of Geography, The University of Tennessee, Knoxville, TN37996, USA The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China, 430072 sshaw@utk.edu

[1]  G. Madey,et al.  Uncovering individual and collective human dynamics from mobile phone records , 2007, 0710.2939.

[2]  Bin Jiang,et al.  Street hierarchies: a minority of streets account for a majority of traffic flow , 2008, Int. J. Geogr. Inf. Sci..

[3]  Hongbo Yu,et al.  A GIS-based time-geographic approach of studying individual activities and interactions in a hybrid physical–virtual space , 2009 .

[4]  Hongbo Yu,et al.  Potential effects of ICT on face-to-face meeting opportunities: a GIS-based time-geographic approach , 2011 .

[5]  Dino Pedreschi,et al.  Trajectory pattern mining , 2007, KDD '07.

[6]  Yun Jiang,et al.  Anchor Points Seeking of Large Urban Crowd Based on the Mobile Billing Data , 2010, ADMA.

[7]  Jae-Gil Lee,et al.  Traffic Density-Based Discovery of Hot Routes in Road Networks , 2007, SSTD.

[8]  Xing Xie,et al.  GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory , 2010, IEEE Data Eng. Bull..

[9]  Ryosuke Shibasaki,et al.  Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data , 2010, HBU.

[10]  Shih-Lung Shaw,et al.  Exploring potential human activities in physical and virtual spaces: a spatio‐temporal GIS approach , 2008, Int. J. Geogr. Inf. Sci..

[11]  Wei-Ying Ma,et al.  Recommending friends and locations based on individual location history , 2011, ACM Trans. Web.

[12]  Kyunghan Lee,et al.  On the Levy-Walk Nature of Human Mobility , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[13]  Jae-Gil Lee,et al.  Trajectory clustering: a partition-and-group framework , 2007, SIGMOD '07.

[14]  Jae-Gil Lee,et al.  Trajectory Outlier Detection: A Partition-and-Detect Framework , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[15]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[16]  T. Geisel,et al.  The scaling laws of human travel , 2006, Nature.

[17]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[18]  Jianliang Xu,et al.  Clustering Moving Objects in Spatial Networks , 2007, DASFAA.

[19]  Shih-Lung Shaw,et al.  Exploratory data analysis of activity diary data: a space-time GIS approach , 2011 .

[20]  Carlo Ratti,et al.  Mobile Landscapes: Using Location Data from Cell Phones for Urban Analysis , 2006 .

[21]  Vania Bogorny,et al.  ST‐DMQL: A Semantic Trajectory Data Mining Query Language , 2009, Int. J. Geogr. Inf. Sci..

[22]  Patrick Laube,et al.  Analyzing Relative Motion within Groups of Trackable Moving Point Objects , 2002, GIScience.

[23]  Torben Bach Pedersen,et al.  Mining Long, Sharable Patterns in Trajectories of Moving Objects , 2009, STDBM.

[24]  Yifan Li,et al.  Clustering moving objects , 2004, KDD.