Skip to main content Link Search Menu Expand Document (external link)

Analysis Across Datacubes

Causemos supports several types of quantitative analyses across datacubes, including:

  • Overlay - Correlation Among Variables: Determine whether an increase in x usually led to an increase or a decrease in y historically. When indicator x was particularly high/low historically, find the values of other indicators of interest (drivers/impacts). Overlay timeseries for drought, water stress, displacement and food price indicators. Historically, drought and water stress are correlated (red arrows). In Jan 2020 (blue vertical line), they also pushed commodity prices to a new peak, which was followed by the highest level of displacement.
  • Overlay - Similar Models/Data Comparison: Determine the similarity of the output of alternative models for comparable scenarios. Determine how similar the data is from different sources for a particular variable. Compare simulated maize production from DSSAT, APSIM-Cropping and another crop production model for the baseline conditions.
  • Region Ranking: Find which regions are currently best/worst with respect to variable(s) of interest. Rank regions based on current needs assessment, considering poverty, malnutrition, likelihood of locust presence and water stress.