Week	Topic
1	Intro to types of spatial data	Description of geostatistical, lattice and point process data with examples, Exploratory data analysis with simple descriptive statistics and graphing tools.
2	Spatial Autocorrelation	why worry about it, how to test for it, how to measure global autocorrelation- Moran's I, join count statistics.
3-4	Measuring autocorrelation at different lags	Stationarity, variograms, covariograms, estimating variograms
5-6	Spatial prediciton at unsampled location	Kriging: ordinary and universal, Cross-validation
7-8	Regression with spatially correlated errors	Estimated generalized least squares, spatial autoregressive models.
9-10	Mapping rates for regions	Spatial smoothing, regression
11-12	Complete spatial randomness	Monte Carlo tests, distance-based tests of CSR Tests of CSR
13-14	More point processes	Poisson cluster and Cox processes, Nearest-neighbor distance distributions, K function