Anomalous Region Detection on the Mobility Data

Mobility data records the change of location and time about the crowd activities, reflecting a large amount of semantic knowledge about human mobility and hot regions. From the perspective of regional semantic knowledge, mining anomalous regions of overcrowded area is essential for disaster-aware resilience system scheme. This paper studies how to discover anomalous regions of moving crowds over the mobility data. From the perspective of spatial information analysis about the location sequence of moving crowds, the paper introduces grid structure to index activity space and proposes a density calculation method of grid cells based on kernel function. By adopting Top-k sorting method, the algorithm determines the density thresholds to detect the anomalous regions. Finally, experimental results validate the feasibility and effectiveness of the above method on practical data sets.

[1]  Jian Dai.  A Novel Moving Object Trajectories Clustering Approach for Very Large Datasets , 2013 .

[2]  George Kollios,et al.  Mining, indexing, and querying historical spatiotemporal data , 2004, KDD.

[3]  Nikos Mamoulis,et al.  Density-based place clustering in geo-social networks , 2014, SIGMOD Conference.

[4]  Jiong Yang,et al.  STING: A Statistical Information Grid Approach to Spatial Data Mining , 1997, VLDB.

[5]  Siyuan Liu,et al.  Towards mobility-based clustering , 2010, KDD.

[6]  Zhaohui Wu,et al.  Trace analysis and mining for smart cities: issues, methods, and applications , 2013, IEEE Communications Magazine.

[7]  Wen-Jing Hsu,et al.  Predictability of individuals' mobility with high-resolution positioning data , 2012, UbiComp.

[8]  Véronique Berge-Cherfaoui,et al.  Vehicle trajectory prediction based on motion model and maneuver recognition , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Bing Li,et al.  Efficient Clustering Aggregation Based on Data Fragments , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Shaojie Qiao,et al.  A Self-Adaptive Parameter Selection Trajectory Prediction Approach via Hidden Markov Models , 2015, IEEE Transactions on Intelligent Transportation Systems.

[11]  Gavin Smith,et al.  A refined limit on the predictability of human mobility , 2014, 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[12]  B. Worton.  Kernel methods for estimating the utilization distribution in home-range studies , 1989 .

[13]  Jignesh M. Patel,et al.  STRIPES: an efficient index for predicted trajectories , 2004, SIGMOD '04.

[14]  Ming-Shu Li,et al.  Discovery of Hot Region in Trajectory Databases: Discovery of Hot Region in Trajectory Databases , 2014 .

[15]  Dirk Grunwald,et al.  AnchorMF: towards effective event context identification , 2013, CIKM.

[16]  Farahnaz Sadoughi,et al.  Ranked k-medoids: A fast and accurate rank-based partitioning algorithm for clustering large datasets , 2013, Knowl. Based Syst..

[17]  Beng Chin Ooi,et al.  Effective Density Queries on ContinuouslyMoving Objects , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[18]  Shaojie Qiao,et al.  TraPlan: An Effective Three-in-One Trajectory-Prediction Model in Transportation Networks , 2015, IEEE Transactions on Intelligent Transportation Systems.

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

[20]  Xing Xie,et al.  Discovering regions of different functions in a city using human mobility and POIs , 2012, KDD.