This course introduces statistical concepts and basic tools involved in modelling and analyzing spatial data: Data that are correlated in ‘space’, arising in regional and urban economics, epidemiology and public health, environmental science, ecology, biology, geology and other disciplines. An outline is given to the three types of spatial data: geostatistical data, lattice data and point patterns. Emphasis is on the analysis of lattice data due to its close ties with regional science and economics. Exploratory spatial data analysis methods, including various plots and tests for spatial autocorrelation, are introduced. Some standard spatial linear regression models and spatial panel data models are also discussed. Quasi-maximum likelihood and GMM methods are used for model estimation and inferences. Real data analyses using R are presented throughout the course.
We will cover the following topics:
- Spatial Data in R: Vector and Raster
- Handling geometry operations and coordinate reference systems
- Making maps, a.k.a. the ancient art of cartography
- Spatial descriptive measures and point pattern analysis
- Areal data analysis and spatial models
- Engaging in geostatistical analysis