Data Science in Earth and Space Sciences

Recommended Prerequisites

Basic training in Data Engineering and Science, Geography, Exact, and/or Natural Sciences

Teaching Methods

All classes will be theoretical-practical, with a brief theoretical introduction to each thematic block adapted to the skills, knowledge and motivations of the student population. In the remainder, special emphasis will be placed on the use and, where necessary, development of software focused on problem solving. Each student should develop a small personal georeferenced data analysis project, which will be presented in one of the last classes.

Learning Outcomes

a) Understanding the principles and techniques for building, reading and interpreting maps;
b) Knowing the providers of spatial information in order to select the appropriate data sources to solve specific problems;
c) Manipulating planetary data in a GIS (Geographic Information System) environment for the purpose of modeling and extraction of information.

Work Placement(s)

No

Syllabus

1. Cartography and GIS in planetary geosciences; geodesy and digital terrain models; Google Earth, Moon and Mars; ESRI ArcGIS; QGIS. Observatory data and remote sensing data: observation of the Sun, Earth and space.
2. Data providers in planetary geosciences: WDC; NOAA; NASA; ESA; JAXA; Roscosmos. Access and cost of data.
3. Earth data: data from the solid earth, the oceans and the atmosphere. Seismic, magnetic, and climatologic data. 1, 2, 3 and 4-D data. Global measurements and global changes.
4. Planetary data: remote sensing from Earth and local satellites and probes. Planetary Data System (PDS - NASA) and Planetary Science Archive (PSA - ESA).
5. Mapping of data in planetary geosciences: operations on large sets of geolocated data. Directional statistics; geostatistics, regionalized variables and kriging; interpolation. Case studies and applications.

Head Lecturer(s)

Eduardo Ivo Cruzes do Paço Ribeiro Alves

Assessment Methods

Assessment
Resolution Problems: 50.0%
Project: 50.0%

Bibliography

Huisman, O., & De By, R. A. (2009). Principles of geographic information systems. ITC Educational Textbook Series, 1, 17.
Lahoz, W., Khattatov, B. & Menard, R. (2010). Data Assimilation: Making Sense of Observations. Springer.
Mardia, K. V., & Jupp, P. E. (2009). Directional statistics (Vol. 494). John Wiley & Sons.
Webster, R., & Oliver, M. A. (2007). Geostatistics for environmental scientists. John Wiley & Sons.
Yuan, M., Buttenfield, B., Gahegan, M. N., and Miller, H. (2019). Geospatial data mining and knowledge discovery. In Research Challenges in Geographic Information Science, R. McMaster and L Usery eds. John Wiley & Sons
Zender, J., & Grayzeck, E. (2006). Lessons learned from planetary science archiving. Advances in Space Research, 38(9), 2013-2022.