Spatial Data Analysis

Code:

MEMG0012

Acronym:

ADVE

Keywords
Classification	Keyword
OFICIAL	Mathematics, Physics, Earth Sciences

Instance: 2008/2009 - 1S

Active?	Yes
Responsible unit:	Mining Engineering Department
Course/CS Responsible:	Master in Mining and Geo-Environmental Engineering

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MEMG	13	Plano de estudos oficial a partir de 2008/09	2	-	4	42	108

Teaching language

Portuguese

Objectives

Introduction to the descriptation, analysis and modeling of geospatial data, based on the main concepts and tools of Geostatistics applied, namely, to the mining, geo-environmental and geotechnical domains.
At the end of the course, the students should be able to:
- Perform statistical description/analysis of univariate and bivariate geospatial data, including graphical and analytical means of data evaluation.
- Perform a basic geostatistical analysis of geospatial data, including a variography study followed by various spatial estimation and simulation techniques.
- Understand the limits of the used tools and of their (geo)statistical knowledge.

Program

Overview of the concepts and techniques to be covered in the course: univariate and bivariate statistics; spatial continuity analysis; estimation; simulation.
Exploratory Data Analysis: statistical summarization, analysis; mapping of the data set, histogram and probability distribution, correlation in bivariate data, data transformations (logarithmic, indicator,
normal-score, rank-order); software use and applications, namely in mining (t-Sichel estimator and reserves calculation).
Quantification of Spatial Continuity: calculation of experimental variograms, fitting models to experimental variogram, concepts of anisotropy and nested structures in variography, other techniques for defining spatial variability (indicator, covariance), spatial co-variability of more than one variable; application of basic variogram analysis and modeling software.
Spatial Estimation (Kriging): review of techniques available for spatial estimation, explanation of the concepts of a 'best' linear unbiased estimator, (BLUE), introduction to the kriging system of equations, use and misuse of kriging variance, application of basic kriging software.
Simulation vs. kriging: an introduction.
Scaling and Sample Support: impacts of discrepancy between measurement and estimation scales; examples of the effects of scale, accounting for scale discrepancies with analytical techniques, numerical techniques for addressing scale issues (block kriging, averaging techniques).
Application of Analysis of Uncertainty: concepts of probability of exceeding a threshold value and probability mapping, incorporation of spatial uncertainty into predicted outcomes of physical processes and human activities; creating probability maps through estimation versus simulation.

Mandatory literature

Soares, Amílcar; Geoestatística. ISBN: 972-8469-10-1
Isaaks, Edward H.; Applied Geostatistics. ISBN: 0-19-505013-4
David, Michel; Handbook of applied advanced geostatistical ore reserve estimation. ISBN: 0-444-42918-2

Complementary Bibliography

Textos e outro material de apoio a fornecer pelo docente

Teaching methods and learning activities

The course is formally set in theoretical-applied an applied lectures.
Some will be mainly used for a theoretical introduction of the different matters with sporadic examples of application. In others there will be successive development of application examples using geospatial data sets, with the help of charts, Excel spreadsheet, MatLab and Surfer environment and specific software.
One or more geospatial data sets will be used as “raw -material” for the developments resulting from the application of the concepts previously introduced. The referred data will have associated an integrated set of objectives to be gradually attained. The conceptual framework will be growing and structured, as much as possible, in intone with practical needs, felt in data treatment process leading to attaining the referred objectives.

Software

Matlab 6
Surfer
Variowin; GeoEas; 3Plot98; Krigame

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Description	Type	Time (hours)	Weight (%)	End date
Subject Classes	Participação presencial	54,00
	Teste	20,00
	Trabalho escrito	30,00
	Exame	6,00
	Total:	-	0,00

Amount of time allocated to each course unit

Description	Type	Time (hours)	End date
	Estudo autónomo	50
	Total:	50,00

Eligibility for exams

Conditions for attaining “frequency”: valid registration on the course, regular attendance and weighted mean of grades obtained during the semester equal or exceeding 5,5 points, in the various performed tasks.
The referred conditions are also valid for students under the conditions referring to items a), b) and c), number 3, article 4 of "Normas Gerais de Avaliação", except those related to attendance and mid-term tests.

Calculation formula of final grade

Frequency mark:
35%: first test,
45%: second test.
20%: other tasks and performance.
At the end of semester students will be graded according to their score on 2 mid-term exams, all the other performed tasks, namely a modular homework assignment work leading to a final report, and their overall performance.
First final exam: substituted by frequency score if positive; in this case students may only access “exame de recurso” if requiring an improvement of score.
Students having a frequency score between 5,5 and 9,5 points can access final exam.
Students without frequency can’t access final exam.

Examinations or Special Assignments

Not foreseen.

Special assessment (TE, DA, ...)

Those foressen in "Normas Gerais de Avaliação".

Classification improvement

Final "Exame de Recurso", oral examination.