demonstration

Stinuum: A Holistic Visual Analysis of Moving Objects with Open Source Software

Authors:

Kyoung-Sook Kim,

Dongmin Kim,

Hyemi Jeong,

Hirotaka OgawaAuthors Info & Claims

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

Article No.: 85, Pages 1 - 4

https://doi.org/10.1145/3139958.3140011

Published: 07 November 2017 Publication History

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Abstract

With the development of position tracking technologies and the increasing usage of mobile devices, the analysis of moving objects, such as pedestrians, vehicles, drones, and hurricanes has become an important topic in various applications including intelligent transportation, disaster management, and urban planning. Many of existing studies have focused on managing and analyzing only time-varying locations of point-based objects. However, real-world moving phenomena are space-time continua occupying volumes, having an area at a time; even more, they contain dynamic attributes depending on time and space, such as the velocity of vehicles or the average of wind speed of hurricanes. In this demonstration, we introduce a comprehensive data format to represent various types of temporal geometries and dynamic properties of moving objects based on OGC® Moving Features. Moreover, we present a visual extension of Cesium to visualize moving objects in a space-time cube by cooperating with a data server that manages moving objects in a Cassandra database via RESTful APIs. This demonstration presents how to analyze a correlation between typhoon trajectories and geo-tagged Twitter messages with our systems.

References

[1]

Ralf Hartmut Güting, Michael H. Böhlen, Martin Erwig, Christian S. Jensen, Nikos A. Lorentzos, Markus Schneider, and Michalis Vazirgiannis. A foundation for representing and querying moving objects. ACM Trans. Database Syst., 25(1):1--42, March 2000.

Digital Library

Google Scholar

[2]

Nikos Pelekis and Yannis Theodoridis. Mobility Data Management and Exploration. Springer New York, New York, 2014.

Digital Library

Google Scholar

[3]

Natalia Andrienko and Gennady Andrienko. Visual analytics of movement: An overview of methods, tools and procedures. Information Visualization, 12(1):3--24, January 2013.

Digital Library

Google Scholar

[4]

The Open Geospatial Consortium. Moving Features Encoding Part I: XML Core (OGC 14-083r2). http://www.opengeospatial.org/standards/movingfeatures, 2015. Online; accessed 19 January 2017.

Google Scholar

[5]

H. Butler, M. Daly, A. Doyle, S. Gillies, S. Hagen, and T. Schaub. IETF RFC 7946, The GeoJSON format. https://www.ietf.org/rfc/rfc7946.txt, 2016. Online; accessed 15 January 2017.

Google Scholar

[6]

Kyoung-Sook KIM and Hirotaka OGAWA. OGC Moving Features Encoding Extension-JSON (OGC 16-140r1). http://docs.opengeospatial.org/bp/16-140rl/16-140r1. html, 2017. Online; accessed 19 May 2017.

Google Scholar

[7]

Menno-Jan Kraak and Alexandra Koussoulakou. A visualization environment for the space-time-cube. In Proc. the 11th International Symposium on Spatial Data Handling, pages 189--200, 2005.

Crossref

Google Scholar

[8]

Tiago Gonçalves, Ana Paula Afonso, and Bruno Martins. Visualizing human trajectories: Comparing space-time cubes and static maps. In Proc. of the 28th International BCS Human Computer Interaction Conference on HCI 2014, pages 207--212, 2014.

Digital Library

Google Scholar

Cited By

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Wang SZhong EZhou QCui XLu HYun WHu ZCai WLong L(2018)An Integrated Visual Analytics Framework for Spatiotemporal DataProceedings of the 1st ACM SIGSPATIAL Workshop on Advances on Resilient and Intelligent Cities10.1145/3284566.3284574(41-45)Online publication date: 6-Nov-2018
https://dl.acm.org/doi/10.1145/3284566.3284574
Wang SZhong ECai WZhou QLu HGu YYun WHu ZLong L(2018)A Visual Analytics Framework for Big Spatiotemporal DataProceedings of the 2nd ACM SIGSPATIAL Workshop on Analytics for Local Events and News10.1145/3282866.3282869(1-5)Online publication date: 6-Nov-2018
https://dl.acm.org/doi/10.1145/3282866.3282869
Lee JKim KLee RLee SCatarci TNorman KMecella M(2018)Visual insight of spatiotemporal IoT-generated contentsProceedings of the 2018 International Conference on Advanced Visual Interfaces10.1145/3206505.3206575(1-3)Online publication date: 29-May-2018
https://dl.acm.org/doi/10.1145/3206505.3206575

Index Terms

Stinuum: A Holistic Visual Analysis of Moving Objects with Open Source Software
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
    2. Visualization systems and tools
      1. Visualization toolkits
2. Information systems
  1. Information systems applications
    1. 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

Publication History

Published: 07 November 2017

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SIGSPATIAL'17

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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 257 of 1,238 submissions, 21%

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Cited By

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Wang SZhong EZhou QCui XLu HYun WHu ZCai WLong L(2018)An Integrated Visual Analytics Framework for Spatiotemporal DataProceedings of the 1st ACM SIGSPATIAL Workshop on Advances on Resilient and Intelligent Cities10.1145/3284566.3284574(41-45)Online publication date: 6-Nov-2018
https://dl.acm.org/doi/10.1145/3284566.3284574
Wang SZhong ECai WZhou QLu HGu YYun WHu ZLong L(2018)A Visual Analytics Framework for Big Spatiotemporal DataProceedings of the 2nd ACM SIGSPATIAL Workshop on Analytics for Local Events and News10.1145/3282866.3282869(1-5)Online publication date: 6-Nov-2018
https://dl.acm.org/doi/10.1145/3282866.3282869
Lee JKim KLee RLee SCatarci TNorman KMecella M(2018)Visual insight of spatiotemporal IoT-generated contentsProceedings of the 2018 International Conference on Advanced Visual Interfaces10.1145/3206505.3206575(1-3)Online publication date: 29-May-2018
https://dl.acm.org/doi/10.1145/3206505.3206575

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