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ST-COPOT: Spatio-temporal Clustering with Contour Polygon Trees

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SIGSPATIAL '17: Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Article No.: 84, Pages 1 - 4

https://doi.org/10.1145/3139958.3140051

Published: 07 November 2017 Publication History

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Abstract

Nowadays, growing effort has been put to develop spatio-temporal clustering approaches that are capable of discovering interesting patterns in large spatio-temporal data streams. In this paper, we propose a 3-phase serial, density-contour based clustering algorithm called ST-COPOT, which can identify spatio-temporal cluster at multiple levels of density granularity. ST-COPOT takes the point cloud data as input and divides it into batches, next, it employs a non-parametric kernel density estimation approach and contouring algorithms to obtain spatial clusters; at last, spatio-temporal clusters are formed by identifying continuing relationships between spatial clusters in consecutive batches. Moreover, a novel data structure called contour polygon tree is introduced as a compact representation of the spatial clusters obtained for each batch for different density thresholds, and a family of novel distance functions that operate on contour polygon trees are proposed to identify continuing clusters. The experimental results on NYC taxi trips data show that ST-COPOT can effectively discover interesting spatio-temporal patterns in taxi pickup location streams.

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Cited By

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Elgarroussi KWang SBanerjee REick C(2018)AconcaguaProceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery10.1145/3281548.3281552(54-61)Online publication date: 6-Nov-2018
https://dl.acm.org/doi/10.1145/3281548.3281552
Banerjee RElgarroussi KWang SZhang YF. Eick C(2018)Tweet Emotion Mapping: Understanding US Emotions in Time and Space2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)10.1109/AIKE.2018.00021(93-100)Online publication date: Sep-2018
https://doi.org/10.1109/AIKE.2018.00021
Zhang YEick C(2018)A Novel Two-Stage System for Detecting and Tracking Events in Twitter2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)10.1109/AIKE.2018.00019(77-84)Online publication date: Sep-2018
https://doi.org/10.1109/AIKE.2018.00019

Index Terms

ST-COPOT: Spatio-temporal Clustering with Contour Polygon Trees
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Clustering
    2. Spatial-temporal systems
      1. Geographic information systems

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Published In

SIGSPATIAL '17: Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2017

677 pages

ISBN:9781450354905

DOI:10.1145/3139958

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 07 November 2017

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SIGSPATIAL

SIGSPATIAL'17: 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 7 - 10, 2017

CA, Redondo Beach, USA

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SIGSPATIAL '17 Paper Acceptance Rate 39 of 193 submissions, 20%;

Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Cited By

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Elgarroussi KWang SBanerjee REick C(2018)AconcaguaProceedings of the 2nd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery10.1145/3281548.3281552(54-61)Online publication date: 6-Nov-2018
https://dl.acm.org/doi/10.1145/3281548.3281552
Banerjee RElgarroussi KWang SZhang YF. Eick C(2018)Tweet Emotion Mapping: Understanding US Emotions in Time and Space2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)10.1109/AIKE.2018.00021(93-100)Online publication date: Sep-2018
https://doi.org/10.1109/AIKE.2018.00021
Zhang YEick C(2018)A Novel Two-Stage System for Detecting and Tracking Events in Twitter2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)10.1109/AIKE.2018.00019(77-84)Online publication date: Sep-2018
https://doi.org/10.1109/AIKE.2018.00019

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