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### Unit Overview

Description

Spatial data, information collected from spatial locations, demands high-level statistical methodology to explore, investigate and make scientific conclusions. Such data arises in a wide range of applied fields such as aerial image processing, astronomy, ecology, engineering, environmental sciences, epidemiology, forestry, mineral prospecting, spatial economics and transportation. Spatial dtatistics, the statistical basis for spatial data science, encompasses statistical analysis of three different types of spatial data: geostatistical data, lattice data and point pattern data. Modeling of these different types of data requires different probabilistic and statistical tools.

This unit begins with a basic introduction to the three types of spatial data and develops some of the statistical tools required to describe and model such data. Then it moves on to in-depth study of a number of topics from the list: spatial stochastic processes, one and higher dimensional point processes, random fields, spatial covariance, variograms, stationarity and non-stationarity, kriging and spatial interpolation, first- and second-order intensity functions, summary functions, spatial models and estimation theory, simulation, spatial regression, spatio-temporal modeling, Bayesian methods in spatial statistics, and analysis of events on linear networks.

The unit will cover real world examples from many different fields. For statistical analysis and simulation the freeware package R will be used.

Credit
6 points
Offering
(see Timetable)
AvailabilityLocationMode
Semester 2UWA (Perth)Face to face
• Level 3 option in the Mathematics; Mathematics; Statistics major sequences
• Level 3 elective
Outcomes

Students are able to (1) distinguish between three different types of spatial data and apply basic statistical tools required conducting inference from such data; (2) demonstrate skill in the use of some of the measures of dependence in spatial processes in analysing and modeling spatial data; (3) demonstrate in-depth knowledge in some of the important topics in spatial statistics; (4) demonstrate knowledge in determining spatial model for some of the spatial data; (5) show skill in simulating some of spatial model using the package R; and (6) demonstrate basic skill in using R for summarising and analysing spatial data.

Assessment

Indicative assessments in this unit are as follows: (1) in-class tests; (2) assignments; and (3) final examination. Further information is available in the unit outline.

Student may be offered supplementary assessment in this unit if they meet the eligibility criteria.

Unit Coordinator(s)
Dr Gopalan Nair
Unit rules
Prerequisites
Enrolment in MJD-MTHST Mathematics and Statistics and
STAT2062 Fundamentals of Probability with Applications
or STAT2063 Probabilistic Methods and their Applications