Detecting urban black holes based on human mobility data

Many types of human mobility data, such as flows of taxicabs, card swiping data of subways, bike trip data and Call Details Records (CDR), can be modeled by a Spatio-Temporal Graph (STG). STG is a directed graph in which vertices and edges are associated with spatio-temporal properties (e.g. the traffic flow on a road and the geospatial location of an intersection). In this paper, we instantly detect interesting phenomena, entitled black holes and volcanos, from an STG. Specifically, a black hole is a subgraph (of an STG) that has the overall inflow greater than the overall outflow by a threshold, while a volcano is a subgraph with the overall outflow greater than the overall inflow by a threshold (detecting volcanos from an STG is proved to be equivalent to the detection of black holes). The online detection of black holes/volcanos can timely reflect anomalous events, such as disasters, catastrophic accidents, and therefore help keep public safety. The patterns of black holes/volcanos and the relations between them reveal human mobility patterns in a city, thus help formulate a better city planning or improve a system's operation efficiency. Based on a well-designed STG index, we propose a two-step black hole detection algorithm: The first step identifies a set of candidate grid cells to start from; the second step expands an initial edge in a candidate cell to a black hole and prunes other candidate cells after a black hole is detected. Then, we adapt this detection algorithm to a continuous black hole detection scenario. We evaluate our method based on Beijing taxicab data and the bike trip data in New York, finding urban anomalies and human mobility patterns.

[1]  Hui Xiong,et al.  Detecting Blackhole and Volcano Patterns in Directed Networks , 2010, 2010 IEEE International Conference on Data Mining.

[2]  Hong Cheng,et al.  Graph Clustering Based on Structural/Attribute Similarities , 2009, Proc. VLDB Endow..

[3]  Nicholas Jing Yuan,et al.  On discovery of gathering patterns from trajectories , 2013, 2013 IEEE 29th International Conference on Data Engineering (ICDE).

[4]  Nick Koudas,et al.  Identifying, attributing and describing spatial bursts , 2010, Proc. VLDB Endow..

[5]  Manuel Barbares.  Periodic subgraph mining in dynamic networks , 2015 .

[6]  Jing Yuan,et al.  On Discovery of Traveling Companions from Streaming Trajectories , 2012, 2012 IEEE 28th International Conference on Data Engineering.

[7]  Hui Xiong,et al.  Mining blackhole and volcano patterns in directed graphs: a general approach , 2012, Data Mining and Knowledge Discovery.

[8]  Stijn van Dongen,et al.  Graph Clustering Via a Discrete Uncoupling Process , 2008, SIAM J. Matrix Anal. Appl..

[9]  Yu Zheng,et al.  Trajectory Data Mining , 2015, ACM Trans. Intell. Syst. Technol..

[10]  Dimitrios Gunopulos,et al.  On The Spatiotemporal Burstiness of Terms , 2012, Proc. VLDB Endow..

[11]  Jiawei Han,et al.  gSpan: graph-based substructure pattern mining , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[12]  Jirí Síma,et al.  On the NP-Completeness of Some Graph Cluster Measures , 2005, SOFSEM.

[13]  Xi Chen,et al.  Learning Spatial-Temporal Varying Graphs with Applications to Climate Data Analysis , 2010, AAAI.

[14]  Christian S. Jensen,et al.  Discovery of convoys in trajectory databases , 2008, Proc. VLDB Endow..

[15]  Cyrus Shahabi,et al.  Crowd sensing of traffic anomalies based on human mobility and social media , 2013, SIGSPATIAL/GIS.

[16]  Xing Xie,et al.  An Interactive-Voting Based Map Matching Algorithm , 2010, 2010 Eleventh International Conference on Mobile Data Management.

[17]  Jeffrey Xu Yu,et al.  Spotting Significant Changing Subgraphs in Evolving Graphs , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[18]  Jiawei Han,et al.  Swarm: Mining Relaxed Temporal Moving Object Clusters , 2010, Proc. VLDB Endow..

[19]  Sari Haj Hussein.  Effective Density Queries on Continuously Moving Objects; in Slides , 2012 .

[20]  Licia Capra,et al.  Urban Computing: Concepts, Methodologies, and Applications , 2014, TIST.

[21]  Céline Robardet,et al.  Constraint-Based Pattern Mining in Dynamic Graphs , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[22]  Fei Wang,et al.  Graph-Based Substructure Pattern Mining Using CUDA Dynamic Parallelism , 2013, IDEAL.

[23]  Ulrik Brandes,et al.  On Modularity Clustering , 2008, IEEE Transactions on Knowledge and Data Engineering.

[24]  Dimitrios Gunopulos,et al.  On-Line Discovery of Dense Areas in Spatio-temporal Databases , 2003, SSTD.

[25]  Reynold Cheng,et al.  On querying historical evolving graph sequences , 2011, Proc. VLDB Endow..

[26]  Srinivasan Parthasarathy,et al.  Scalable graph clustering using stochastic flows: applications to community discovery , 2009, KDD.