Spatial visual analytics 1 is a critical aspect for big spatiotemporal data (BSTD) in exhibition the hidden spatiotemporal patterns. However, the real-time and dynamic characters of BSTD causes great challenges for the GIS domain and big data domain due to the limitation of the current visual analytics tools. Thus, we propose and implement a visual analytics framework. The framework integrates open source map library and visualization library to provide innovative visual capacity for BSTD. The framework uses GIScript and iDesktop Cross to support high performance BSTD spatial analytics. The application of the framework in global air traffic data proves its efficiency and utility in discovering the global flight patterns. The framework simplifies the visual analytics procedure for BSTD and can be adopted by various domains.

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Cited By

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Wang WLi XWang TWang SWang RXu DZhou J(2024)Spatial-Temporal Evolution Characteristics Analysis of Color Steel Buildings in Lanzhou CityISPRS International Journal of Geo-Information10.3390/ijgi1306017913:6(179)Online publication date: 29-May-2024
https://doi.org/10.3390/ijgi13060179
Silva DHolanda M(2021)Applications of geospatial big data in the Internet of ThingsTransactions in GIS10.1111/tgis.1284626:1(41-71)Online publication date: 24-Sep-2021
https://doi.org/10.1111/tgis.12846
Hamdi AShaban KErradi AMohamed ARumi SSalim F(2021)Spatiotemporal data mining: a survey on challenges and open problemsArtificial Intelligence Review10.1007/s10462-021-09994-yOnline publication date: 15-Apr-2021
https://doi.org/10.1007/s10462-021-09994-y
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A Visual Analytics Framework for Big Spatiotemporal Data
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
2. Information systems
  1. Information systems applications

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LENS'18: Proceedings of the 2nd ACM SIGSPATIAL Workshop on Analytics for Local Events and News

November 2018

49 pages

ISBN:9781450360357

DOI:10.1145/3282866

Editors:
Amr Magdy
University of California, Riverside, CA, USA
,
Xun Zhou
University of Iowa, IA, USA
,
Liang Zhao
George Mason University, VA, USA
,
Yan Huang
University of North Texas, TX, USA

Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 06 November 2018

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Wang WLi XWang TWang SWang RXu DZhou J(2024)Spatial-Temporal Evolution Characteristics Analysis of Color Steel Buildings in Lanzhou CityISPRS International Journal of Geo-Information10.3390/ijgi1306017913:6(179)Online publication date: 29-May-2024
https://doi.org/10.3390/ijgi13060179
Silva DHolanda M(2021)Applications of geospatial big data in the Internet of ThingsTransactions in GIS10.1111/tgis.1284626:1(41-71)Online publication date: 24-Sep-2021
https://doi.org/10.1111/tgis.12846
Hamdi AShaban KErradi AMohamed ARumi SSalim F(2021)Spatiotemporal data mining: a survey on challenges and open problemsArtificial Intelligence Review10.1007/s10462-021-09994-yOnline publication date: 15-Apr-2021
https://doi.org/10.1007/s10462-021-09994-y
Wang SSun YSun YGuan YFeng ZLu HCai WLong L(2019)A Hybrid Framework for High-Performance Modeling of Three-Dimensional Pipe NetworksISPRS International Journal of Geo-Information10.3390/ijgi81004418:10(441)Online publication date: 8-Oct-2019
https://doi.org/10.3390/ijgi8100441

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