Spatial Statistics

General characterization

Prerequisites

Teaching language: English.

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

The curricular unit is based on theoretical lectures and practical application of methods using software applications, such as Excel and ArcGIS. A variety of instructional strategies will be applied, including lectures, slide show demonstrations, step-by-step tutorials on using the tools and techniques in the ArcGIS software, questions and answers. The practical component is geared towards solving problems and exercises, including discussion and interpretation of results.

During the semester, students must also work in one sample set of interest with the spatial statistics concepts and techniques and present their work results to the professor, as well as to the other students. Finally, the students must write reports about these results.

Evaluation method

REGULAR PERIOD (1st call):
1. Two Assignments, i.e. individual reports with the answers to the proposed problems (15% of final grade the first one, and 10% the second one);
2. Exam (30%);
3. Oral presentation of the project (10%);
4. Project Report (35%).

 

RESIT PERIOD (2nd call):
1. Two Assignments, i.e. individual reports with the answers to the proposed problems (15% of final grade the first one, and 10% the second one) with the classification obtained in the 1st call;
2. Exam (30%);
3. Oral presentation of the project (10%) with the classification obtained in the 1st call;
4. Project Report (35%) with the classification obtained in the 1st call.

 

RULES:
- All the evaluation elements are mandatory for approval except the Assignments.
- Assignments submitted after the deadline will have a penalty of 0.5 points for each day of delay.
- The project can be prepared individually or (preferably) in groups of two students, in Portuguese or English, as indicated on the e-learning platform.
- Project Reports submitted after the deadline will have a penalty of 0.5 points for each day of delay. The maximum delay allowed is 3 days.
- Assignments and Project Reports not submitted on the e-learning platform will be rejected.
- In the Resit Period (2nd call) it is only possible to take the exam (weighting 30%), the final grade being calculated based on the classifications obtained previously for the remaining assessment elements.

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: