Keywords
Visualization tool; climate change; co-occurrence analysis
Start Date
5-7-2022 12:00 PM
End Date
8-7-2022 10:00 AM
Abstract
Currently, there is extensive usage of climate models and climate datasets aimed at predicting the future climate variability and its impacts on the environment, economy, and society. To gain insights into the landscape of interactive visual tools built to compare and analyze climate data, a study of publications over a period 1990-2021 is performed. The survey establishes the evolution of publications, most used keywords, evolution of citations, and the most productive research categories, authors, institutions, journals, and countries. Furthermore, a keyword co-occurrence analysis is conducted to reveal research trends as well as gaps that require further development. Particularly, it is found that analytical tools that visualize and compare climate modelling data with their historically observed values remain by far an underexplored area within the domain. It is expected that such tools would not only aid within meteorological research but could also be expanded to integrate, compare, and visualize other datasets related to agriculture, energy, land use, etc. for a better understanding of the linkages between climate variability and these areas. An example visualization tool for climate data analytics will be demonstrated in our conference talk.
Visualization tools for climate data analytics
Currently, there is extensive usage of climate models and climate datasets aimed at predicting the future climate variability and its impacts on the environment, economy, and society. To gain insights into the landscape of interactive visual tools built to compare and analyze climate data, a study of publications over a period 1990-2021 is performed. The survey establishes the evolution of publications, most used keywords, evolution of citations, and the most productive research categories, authors, institutions, journals, and countries. Furthermore, a keyword co-occurrence analysis is conducted to reveal research trends as well as gaps that require further development. Particularly, it is found that analytical tools that visualize and compare climate modelling data with their historically observed values remain by far an underexplored area within the domain. It is expected that such tools would not only aid within meteorological research but could also be expanded to integrate, compare, and visualize other datasets related to agriculture, energy, land use, etc. for a better understanding of the linkages between climate variability and these areas. An example visualization tool for climate data analytics will be demonstrated in our conference talk.
Stream and Session
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