# Course detail

### LCE5700 - Geoestatistics

__Credit hours__

In-class work per week |
Practice per week |
Credits |
Duration |
Total |

9 |
6 |
8 |
5 weeks |
120 hours |

__Instructor__

Paulo Justiniano Ribeiro Junior

Sonia Maria de Stefano Piedade

__Objective__

Definition and characterization of spatial statistics problems with emphasis on geostatistics.

To present models and spatial data analysis techniques for spatially continuous processes aiming for both, theoretical basics for the subject and usage of computational resources for planning the data aquisition and the practice of the statistical modelling, data analysis and criticism. The course shall include principles os statistical inference relevant to the spatial data analysis such that likelihood and Bayesian inference.

__Content__

1. a) Motivation – examples of different areas of spatial statistics and charicterization of geostatistics, b) historical development of the field, applications areas and future directions. 2. Descriptive analysis: a)univariate, b) multivariate, c) spatial, d)computational aspects and data examples. 3. Elements of probability and statistics a) random variablas and uni and multivariate distributions, b) simulation, c) statistical inference – estimation and topics on likelihood analysis, d) linear models, e) computational aspects and illustrations. 4. Conventional geostatistics: a) variograms, b) spatial prediction, c) kriging I- basic methods, II- advanced methods, e) geostatistical simulation f) computational aspects and illustrations. 5. Univariate Gaussian geostatistical models: a) definition and properties, b) justifications c) inference, d) prediction, e) computational aspects and illustrations. 6. Bayesian geostatistics: a) elements of Bayesian inference, b) model specification, c) uncertainty levels, d) estimation, e) prediction, f) relations with conventional methods, g) computational aspects and illustrations.

__Bibliography__

Bailey, T. & Gattrel, (1996). Interactive spatial data analysis, Longman

Banerjee, S.; Carlin, B.P.; Gelfand, A.E. (2004) Hierarchical modeling and analysis for spatial data. Chapman Hall/CRC Press.

Blangiardo, M. and Cameletti, M. Spatial and spatial temporal Bayesian models with R-INLA. Wiley, 2015

Bivand, R.S., Pebesma, E.J., Gómez-Rubio, V. (2008) Applied Spatial Data Analysis with R. Series: Use R. Springer

Cressie, N. (1993). Statistics for spatial data - revised edition, Wiley.

Diggle, P.J. & Ribeiro Jr, P.J. (2007) Model-based Geostatistics. Springer.

Gelman, A Carlin, J., Stern, H. & Rubin, D. (2004). Bayesian data analysis, 2nd edn. Chapman and Hall.

Goovaerts, P. (1997). Geostatistcs for natural resources evaluation, Oxford University Press.

Isaaks, E. & Srisvastava, R. (1989). An introduction to apllied geostatistics, Oxford University Press.

Kitanidis, P. (1997). Introduction to geostatistics: applications in hydrogeology, Cambridge University Press.

Migon, H.S. ; Gamerman, D. (1999) Statistical Inference: an Integrated Approach. Arnold.

Venables, W. & Ripley, B. (2002). Modern applied statistics using S-PLUS, 4 edn, Springer.

Wackernagel, H. (2003). Multivariate geostatistics, 3rd edn. Springer.

Webster, R., Oliver, M.A. (2007) Geostatistics for Environmental Scientists. Wiley