Geo-Statistics

General characterization

Prerequisites

Teaching language: Portuguese.

Students must install in their personal computers the ArcGIS Desktop software with the Geostatistical Analyst and the Spatial Analyst extensions.

Bibliography

Goovaerts, P. (1997). Geostatistics for natural resources evaluation. New York: Oxford University.

Isaaks, E.H., Srivastava, R.M. (1989). An Introduction to Applied Geostatistics. New York: Oxford University.

Fotheringham A.S., Brunsdon C., Charlton M. (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Chichester: John Wiley & Sons, http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=75434 (full text available after login in NOVA IMS network or through VPN connection).

Extra reading bibliography:

Mitchel, A. (2005). The ESRI Guide to GIS analysis, Volume 2: Spatial Measurements and Statistics. Redlands, California: Esri Press. e-book, http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=1613807 (full text available after login in NOVA IMS network or through VPN connection).

Anselin, L., & Rey, S. J. (2009). Perspectives on Spatial Data Analysis. Springer, New York.

Chun, Y., & Griffith, D. A. (2013). Spatial Statistics and Geostatistics: Theory and Applications for Geographic Information Science and Technology. Sage.

Deutsch, C.V., Journel, A.G. (1998). GSLIB: geostatistical software library and user's guide. 2nd ed. New York : Oxford University.

Lloyd, C. D. (2010). Local Models for Spatial Analysis. CRC press.

Soares, A. (2014). Geoestatística para as ciências da terra e do ambiente. 3ª ed. Lisboa: IST.

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

REGULAR PERIOD (1st call):
1. Exam (20%);
2. Report of the project (70%);
3. Oral presentation of the project (10%).

 

RESIT PERIOD (2nd call): not applicable.

 

RULES:
- All the evaluation elements are mandatory for approval.
- The exam is solved on the e-learning platform on the day and time indicated in the study program¿s calendar.
- The report must be prepared individually, in Portuguese or English, as indicated on the e-learning platform.
- Reports submitted after the deadline will have a penalty of 0.5 points for each day of delay. The maximum delay allowed is 7 days.
- Reports not submitted on the e-learning platform will be rejected.

Subject matter

The curricular unit is organized in 4 Learning Units (LU):

LU1. EXPLORATORY DATA ANALYSIS

- Introduction

- General concepts on data description

- Exploratory Spatial Data Analysis (ESDA) tools

LU2. DETERMINISTIC METHODS

- General concepts on spatial interpolation

- Thiessen polygons (Voronoi maps)

- IDW - Inverse distance weighting

- Validation and cross-validation

LU3. KRIGING

- Spatial continuity analysis

- Variography

- Geostatistics estimation concepts

- Univariate kriging

LU4. GEOGRAPHICALLY WEIGHTED REGRESSION

- Concepts of statistical testing

- General concepts on regression analysis

- OLS - Ordinary Least Squares

- GWR - Geographically Weighted Regression

Programs

Programs where the course is taught: