Abstract

Scenario analysis has been widely applied in climate science to understand the impact of climate change on the future human environment, but intercomparison and similarity analysis of different climate scenarios based on multiple simulation runs remain challenging. Although spatial heterogeneity plays a key role in modeling climate and human systems, little research has been performed to understand the impact of spatial variations and scales on similarity analysis of climate scenarios. To address this issue, the authors developed a geovisual analytics framework that lets users perform similarity analysis of climate scenarios from the Global Change Assessment Model (GCAM) using a hierarchical clustering approach.

Original languageEnglish (US)
Article number8047458
Pages (from-to)40-49
Number of pages10
JournalIEEE Computer Graphics and Applications
Volume37
Issue number5
DOIs
StatePublished - Jan 1 2017

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Keywords

  • computer graphics
  • geographic data science
  • geographical visualization
  • hierarchical clustering
  • scenario analysis
  • spatial scale

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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