Spatial and or space-time predictions will eventually form the basis for most of the products identified in step 1 above.
  • Identify, obtain, and collate gridded spatial data to use as covariates for spatial predictions and as mask files to identify areas for inclusion or exclusion from analysis (e.g.
  • Identify how to generate and evaluate spatial predictions and maps with the relevant model training, stacking, validation and testing procedures.
  • Decide on how to update the spatial predictions as additional data become available over time.
  • Practice reproducibility - write literate prediction code that people can modify and track over time. Generally, there is no use in writing something with software that no one can afford to buy, contribute to and/or modify.
  • If possible use a venue such as Kaggle to ensure that all predictions receive “the best possible” attention from professional data scientists.
  • Version all code in Git or Subversion, and revert to step 1 frequently.
Time: 1-3 months
Cost: 15-25k USD