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Cursus: 201700364
201700364
Spatial Statistics
Cursus informatie
Cursus201700364
Studiepunten (ECTS)5
CursustypeCursus
VoertaalEngels
Contactpersoonprof.dr. M.N.M. van Lieshout
E-mailm.n.m.vanlieshout@utwente.nl
Docenten
Examinator
prof.dr. M.N.M. van Lieshout
Contactpersoon van de cursus
prof.dr. M.N.M. van Lieshout
Examinator
dr. F.B. Osei
Examinator
prof.dr.ir. A. Stein
Collegejaar2022
Aanvangsblok
2B
AanmeldingsprocedureZelf aanmelden via OSIRIS Student
Inschrijven via OSIRISJa
Cursusdoelen
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.
Inhoud
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
Voorkennis
Statistics (e.g. AM module 5)
Linear structures (e.g. 202001325)
Participating study
Master Geo-informatics
Participating study
Master Computer Science
Participating study
Master Applied Mathematics
Participating study
Master Electrical Engineering
Verplicht materiaal
Course material
Hand outs during the lectures
Aanbevolen materiaal
-
Werkvormen
Hoorcollege
AanwezigheidsplichtJa

Opdracht
AanwezigheidsplichtJa

Toetsen
Written Exam

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