451
• FEW nexus datasets are spatial and spatiotemporal in nature, and the key issues
such as autocorrelation, anisotropy, teleconnections, and heterogeneity should be
considered when working on these datasets. Specialized tools for spatial and
spatiotemporal datasets are topics of ongoing research and readers are encouraged to follow up the news regarding these.
• Stakeholders and decision makers need tools to interactively participate in the
decision-making process to have a joint solution for the FEW nexus related problems. Similarly, life cycle thinking is important for a successful FEW Nexus
research. Data availability, validity, and computational challenges are key
concerns.
• Visualizations of FEW nexus data along with future projections can serve as a
decision support system to decision makers and stakeholders with less knowledge about the underlying interconnected components. Thus, useful and intuitive
visualizations should be developed.
Discussion Points and Exercises
1. Discuss the differences between time and space complexity of computational
problems.
2. Which programming languages are better suited for FEW Nexus researchers?
Why?
3. What are the three V’s of Big Data? Give examples of Big Datasets from FEW
Nexus.
4. Among scientific methods, Data-Driven Empirical Models are getting much
interest in the data science community nowadays. What is the cause of this?
5. One particularly important component of FEW Nexus datasets is their spatial
and spatiotemporal nature. What properties of spatial and spatiotemporal datasets create new challenges in addition to the challenges of Big Data?
6. What are the key advantages of using mechanistic models and data-driven models? How can these be fused together to leverage their advantages?
7. What are the useful tools that can help guide the stakeholders build consensus?
Why are visualizations important for this particular task?
8. What are the concerns for Life Cycle Assessment from a computer science
perspective? How can these concerns be overcome?
9. Precision agriculture is a good example of using computational tools for
improving efficiencies as well as productivity. Which computing tools are used
in precision agriculture? Are there any emerging technologies that can be added
to this list?
10. What is collaborative geo-design? What other components can be added to the
geo-design concept? Discuss the similarities and dissimilarities between geodesign and life cycle assessment.
11. What data are available at the GEOGLAM website. How one can use these
datasets for the FEW Nexus research? Are there any other datasets that would
be useful for crop monitoring?
16 Computing
• FEW nexus datasets are spatial and spatiotemporal in nature, and the key issues
such as autocorrelation, anisotropy, teleconnections, and heterogeneity should be
considered when working on these datasets. Specialized tools for spatial and
spatiotemporal datasets are topics of ongoing research and readers are encouraged to follow up the news regarding these.
• Stakeholders and decision makers need tools to interactively participate in the
decision-making process to have a joint solution for the FEW nexus related problems. Similarly, life cycle thinking is important for a successful FEW Nexus
research. Data availability, validity, and computational challenges are key
concerns.
• Visualizations of FEW nexus data along with future projections can serve as a
decision support system to decision makers and stakeholders with less knowledge about the underlying interconnected components. Thus, useful and intuitive
visualizations should be developed.
Discussion Points and Exercises
1. Discuss the differences between time and space complexity of computational
problems.
2. Which programming languages are better suited for FEW Nexus researchers?
Why?
3. What are the three V’s of Big Data? Give examples of Big Datasets from FEW
Nexus.
4. Among scientific methods, Data-Driven Empirical Models are getting much
interest in the data science community nowadays. What is the cause of this?
5. One particularly important component of FEW Nexus datasets is their spatial
and spatiotemporal nature. What properties of spatial and spatiotemporal datasets create new challenges in addition to the challenges of Big Data?
6. What are the key advantages of using mechanistic models and data-driven models? How can these be fused together to leverage their advantages?
7. What are the useful tools that can help guide the stakeholders build consensus?
Why are visualizations important for this particular task?
8. What are the concerns for Life Cycle Assessment from a computer science
perspective? How can these concerns be overcome?
9. Precision agriculture is a good example of using computational tools for
improving efficiencies as well as productivity. Which computing tools are used
in precision agriculture? Are there any emerging technologies that can be added
to this list?
10. What is collaborative geo-design? What other components can be added to the
geo-design concept? Discuss the similarities and dissimilarities between geodesign and life cycle assessment.
11. What data are available at the GEOGLAM website. How one can use these
datasets for the FEW Nexus research? Are there any other datasets that would
be useful for crop monitoring?
16 Computing