GeoSparkViz in action: A data system with built-in support for geospatial visualization

Jia Yu, Anique Tahir, Mohamed Elsayed

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Visualizing data on maps is deemed a powerful tool for data scientists to make sense of geospatial data. The geospatial map visualization (abbr. MapViz) process first loads the designated geospatial data, processes the data and then applies the map visualization effect. Guaranteeing detailed and accurate geospatial MapViz (e.g., at multiple zoom levels) requires extremely high-resolution maps. Classic solutions suffer from limited computation resources while scalable MapViz system architectures are not able to co-optimize the data management and visualization phases in the same system. This paper demonstrates GeoSparkViz, a full-fledged system that allows the user to load, prepare, integrate and execute MapViz tasks in the same system. For demonstration purpose, we implemented a web interface using a node.js web server, Baidu echarts library, and MapBox on top of GeoSparkViz to visually explore patterns in the New York City Taxi Trips dataset. The demonstration scenarios show how the data preparation and map visualization phases are combined in GeoSparkViz.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019
PublisherIEEE Computer Society
Pages1992-1995
Number of pages4
ISBN (Electronic)9781538674741
DOIs
StatePublished - Apr 1 2019
Event35th IEEE International Conference on Data Engineering, ICDE 2019 - Macau, China
Duration: Apr 8 2019Apr 11 2019

Publication series

NameProceedings - International Conference on Data Engineering
Volume2019-April
ISSN (Print)1084-4627

Conference

Conference35th IEEE International Conference on Data Engineering, ICDE 2019
CountryChina
CityMacau
Period4/8/194/11/19

Fingerprint

Visualization
Demonstrations
Data visualization
Information management
Interfaces (computer)
Servers

Keywords

  • Distributed computing
  • Geospatial
  • Visualization

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

Cite this

Yu, J., Tahir, A., & Elsayed, M. (2019). GeoSparkViz in action: A data system with built-in support for geospatial visualization. In Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019 (pp. 1992-1995). [8731471] (Proceedings - International Conference on Data Engineering; Vol. 2019-April). IEEE Computer Society. https://doi.org/10.1109/ICDE.2019.00222

GeoSparkViz in action : A data system with built-in support for geospatial visualization. / Yu, Jia; Tahir, Anique; Elsayed, Mohamed.

Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019. IEEE Computer Society, 2019. p. 1992-1995 8731471 (Proceedings - International Conference on Data Engineering; Vol. 2019-April).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yu, J, Tahir, A & Elsayed, M 2019, GeoSparkViz in action: A data system with built-in support for geospatial visualization. in Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019., 8731471, Proceedings - International Conference on Data Engineering, vol. 2019-April, IEEE Computer Society, pp. 1992-1995, 35th IEEE International Conference on Data Engineering, ICDE 2019, Macau, China, 4/8/19. https://doi.org/10.1109/ICDE.2019.00222
Yu J, Tahir A, Elsayed M. GeoSparkViz in action: A data system with built-in support for geospatial visualization. In Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019. IEEE Computer Society. 2019. p. 1992-1995. 8731471. (Proceedings - International Conference on Data Engineering). https://doi.org/10.1109/ICDE.2019.00222
Yu, Jia ; Tahir, Anique ; Elsayed, Mohamed. / GeoSparkViz in action : A data system with built-in support for geospatial visualization. Proceedings - 2019 IEEE 35th International Conference on Data Engineering, ICDE 2019. IEEE Computer Society, 2019. pp. 1992-1995 (Proceedings - International Conference on Data Engineering).
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