The objective of this curricular unit is to teach the students the basic concepts of standard geostatistics and spatial statistics. Students will learn the main theoretical concepts related to the spatial interpolation of attributes using deterministic methods and geostatistics procedures, which are based on the spatial autocorrelation of the observed data. Students will also learn the theoretical background of spatial regression and how to set up and carry out standard spatial exploratory and regression analyses. The students will work on computer programs to practice the theoretical concepts. Students are expected to evaluate the potential of spatial statistics for their own research.

General characterization





Responsible teacher

Ana Cristina Marinho da Costa


Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English


Not applicable.


Tutorials and other material provided by the teachers.

Deutsch, C. V.; Journel, A. G., 1998. Geostatistical Software Library and User’s Guide. Oxford University Press, New York, USA

Goovaerts, P., 1997. Geostatistics for Natural Resources Evaluation. Oxford University Press, Inc, New York, USA

Isaaks, E. H.; Srivastava, R. M., 1989. An Introduction to Applied Geostatistics. Oxford University Press, Inc, New York, USA

Fotheringham A.S., Brunsdon C., Charlton M. (2002) Geographically Weighted Regression: the analysis of spatially varying relationships. Wiley, Chichester, UK.

Soares, A. 2000. Geoestatística para as Ciências da Terra e do Ambiente. Instituto Superior de Técnico, IST Press. Lisboa, Portugal

Teaching method

This curricular unit is lectured through the e-learning platform using synchronous tools (videoconference classes with the teacher) and asynchronous tools (forum, email, learning materials available in the e-learning platform). There will be a synchronous session at the end of each Learning Unit (LU). This corresponds to a 2-hour online class with the teacher, which will be dedicated to the content of each LU and to the solving of practical exercises described in the tutorials. A random set of self-evaluation exercises is available for each Learning Unit, in the e-learning platform. Students can try to answer to the self-evaluation exercises as many times as they wish.

Evaluation method

1. Exam (20% of final grade);

2. Individual report of the project (70% of final grade);

3. Oral presentation of the project (10% of final grade).

Subject matter

The curricular unit is organized in four Learning Units (LU):

LU1. Exploratory data analysis

LU2. Deterministic methods

LU3. Kriging

LU4. Geographically Weighted Regression