Course overview
Data collected across varying spatial locations require specialist statistical techniques to explore. Spatially autocorrelated data appears across a vast array of fields and industries including aerial image processing, astronomy, ecology, engineering, environmental sciences, epidemiology, forestry, energy, spatial economics and transportation, making this course particularly useful for anyone working, or planning across these areas. Spatial data tends to appear in one of three forms: geostatistical data, lattice data and point pattern data. This course will introduce the varying probabilistic and statistical methods used to analyse all three.
Course learning outcomes
- Understand and identify different types of spatial data, spatial autocorrelation, spatial autocorrelation indices and definitions of distance
- Visualise spatial data appropriately to identify patterns, select appropriate methods for analysis and communicate results to end users in an accessible way
- Identify and recommend appropriate sampling methods for spatial data and design a sampling scheme for spatial data
- Effectively analyse both sparse and big spatial and spatio-temporal data
- Critically evaluate and discuss relevant assumptions for spatial analysis to ensure appropriate use
- Produce professional statistical reports which summarise spatial analysis for both specialist and non-specialist audiences
Degree list
The following degrees include this course