Outline.
Statistics 222/Geography 292: Spatial Statistics, Prof. Paik Schoenberg.
Text:
Cressie, Noel A. C. Statistics for spatial data, revised
edition. Wiley, New York.
The text is on reserve in the SEL-EMS library.
There is also an optional supplementary text:
Ripley, Brian D. Spatial Statistics. Wiley, NY.
Office hours: Wednesdays, 3 to 4:30pm, MS 6167.
email: frederic@stat.ucla.edu
Statistics 222 will explore basic methods used in the analysis of
spatial data. The course will be both theoretical and
applied. Students will learn standard concepts in spatial data analysis,
point process data analysis, and image analysis.
Examples from geography, epidemiology, environmental science,
neurology, and biology, will be provided throughout the
instruction of the course, and students will implement the techniques
discussed in class using real data sets.
Dedicated students should come away with an in-depth understanding
of statistical concepts related to spatial statistics, as well as a thorough
comprehension of how and when
to implement various techniques in practice.
The course is designed for graduate students in any discipline
with solid mathematical
backgrounds and some knowledge of basic statistics.
A preliminary outline of the class is given below.
1. Simple Stochastic Models for Spatial Data
a. Basic definitions.
b. White noise.
c. Boolean schemes.
d. Poisson processes.
e. Tessellations.
2. Spatial autocorrelation, regression, and spectra
a. Definitions.
b. Spatial autoregression.
c. Spatial spectral analysis.
3. Spatial point processes.
a. Definitions.
b. Conditional rates and compensators.
c. Martingale techniques and residual analysis.
4. Spatial sampling.
a. Experimental design.
b. Sampling error estimation.
c. Optimal design.
5. Smoothing and interpolation.
a. Moving averages.
b. Trend surfaces.
c. Contouring.
d. Prediction.
6. Nonparametric methods.
a. Kernels.
b. Splines.
c. Wavelets.