SOVAS: A Scalable Online Visual Analytic System for Big Climate Data Analysis

Most existing online processing and analytics systems for climate studies only support fixed user interface with predefined functions. These systems are often not scalable to handle massive climate data that could easily accumulate terabytes daily. To address the major limitations of existing online systems for climate studies, this paper presents a scalable online visual analytic system, known as SOVAS, to balance both usability and flexibility. SOVAS, enabled by a set of key techniques, supports large-scale climate data analytics and knowledge discovery in a scalable and sharable environment.

SOVAS Website: https://gidbusc.github.io/SCOVAS

SOVAS Portal: http://gis.cas.sc.edu/scovas

SOVAS

Publication:

Li Z., Huang Q., Jiang Y., Hu F., SOVAS: A Scalable Online Visual Analytic System for Big Climate Data Analysis, International Journal of Geographic Information Science 

Six types of User Defined Spatiotemporal Functions (UDSFs) and their syntax/usage examples

https://gidbusc.github.io/SCOVAS/#functions


Query analysis examples 

SOVAS generates a unique identifier for each query. Thus, each query can be shared and retrieved with a unique URL. The query examples explained in the manuscript can be accessed with following links.

  • Extreme heat for a city

http://gis.cas.sc.edu/scovas?id=8475969214283

  • Extreme heat map

http://gis.cas.sc.edu/scovas?id=1525959214081

  • Anomaly analysis

http://gis.cas.sc.edu/scovas?id=1525955312508

  • Correlation analysis

http://gis.cas.sc.edu/scovas?id=6024965312714

  • Multi-variables visualization

http://gis.cas.sc.edu/scovas?id=1520702402467

  • Zonal statistics: Meteorological measurements (daily precipitation) using US state boundary as zone input

http://gis.cas.sc.edu/scovas?id=1521379439700

  • Zonal statistics: MERRA data (hourly land surface temperature) using US climate division as zone input.

http://gis.cas.sc.edu/scovas?id=1520740465840

  • Explore the hourly local variability of the July land surface temperature by aggregating ten-year MERRA land data from 2001 to 2010.

http://gis.cas.sc.edu/scovas?id=1526171870734

  • Analyze the global land surface temperature anomalies in Jun-Jul-Aug (Northern Hemisphere summer) for each year between 1980 and 2015. This analysis illustrates how to build a query to obtain the local z-score (anomaly) maps. You can modify the spatiotemporal criteria and variable to create a new analysis.

http://gis.cas.sc.edu/scovas?id=1489418558789


Translate »