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	Variograms and covariograms	Stationarity, variograms, covariograms, estimating variograms
5-6	Spatial prediciton	Kriging: ordinary and universal, Classical and Bayesian
7-8	Prediction, interpolation and regression	Spatial models for modeling and inference in geostatistical inference

Week	Topic
9-10	Lattice Models	Markov random fields, CAR and SAR models, spatial smoothing, regression
11-12	Bayesian methods	WinBugs and GeoBUGS; spatio-temporal disease mapping
13	Complete Spatial Randomness	Monte carlo tests, distance-based tests of CSR
14	Point processes	Poisson cluster and Cox processes, Nearest-neighbor distance distributions, K function
15	Final Project	To be discussed in class