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 Course module: 201700364
 201700364Spatial Statistics
 Course info
Course module201700364
Credits (ECTS)5
Course typeCourse
Language of instructionEnglish
Contact personprof.dr. M.N.M. van Lieshout
E-mailm.n.m.vanlieshout@utwente.nl
Lecturer(s)
 Examiner prof.dr. M.N.M. van Lieshout Contactperson for the course prof.dr. M.N.M. van Lieshout Examiner dr. F.B. Osei Examiner prof.dr.ir. A. Stein
Starting block
 2B
Application procedureYou apply via OSIRIS Student
Registration using OSIRISYes
 Aims
 body { font-size: 9pt; font-family: Arial } table { font-size: 9pt; font-family: Arial } After the course the student masters the basic principles of spatial statistics and is able to use R-packages (gstat and spatstat) to carry out statistical inference including interpolation, regression and model fitting for spatial data. In particular, the student is able to: design an optimal sampling scheme, distinguish between design-based and model-based sampling, estimate and interpret semi-variograms, carry out kriging interpolation with and without co-variables, validate kriging and spatial regression models, estimate the first and second order moment measures of a point process and interpret them, assess stationarity and isotropy, calculate elementary characteristics of Poisson and binomial point processes, simulate finite point processes, test for complete spatial randomness, estimate model parameters by maximum likelihood or maximum pseudo-likelihood, validate fitted point process models.
 Content
 body { font-size: 9pt; font-family: Arial } table { font-size: 9pt; font-family: Arial } Spatial data may come in various forms. Geostatistical data consist of a list of random measurements taken at fixed locations. In point pattern data the locations themselves are random. Examples of the former include weather and air quality data collected at monitoring stations. Optimal sampling is an important issue. Examples of point patterns include catalogues of the epicentres and magnitudes of earthquakes.   Specific topics that will be addressed include: spatial data handling in R spatial sampling theory geostatistical modeling and interpolation point process modeling statistical inference for Poisson processes
Assumed previous knowledge
 Statistics (e.g. AM module 5)Linear structures (e.g. 202001325)
 Participating study
 Participating study
 Master Computer Science
 Participating study
 Master Applied Mathematics
 Participating study
 Master Electrical Engineering
Required materials
Course material
 Hand outs during the lectures
Recommended materials
-
Instructional modes
Assignment
 Presence duty Yes

Lecture
 Presence duty Yes

Tests
 Written Exam
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