Environmental Spatial Statistics, Spring 2017 1

ENVIRONMENTAL SPATIAL STATISTICS Spring, 2017 Washington State University SOILS/STAT 508 3 credits

(cooperatively available to University of Idaho students)

Instructor: David Brown, Assoc. Professor of Soil Geography, Dept. of Crop & Soil

Sciences; 405 Johnson Hall, david_brown@wsu.edu, (509) 335-1859

When and Where

Lecture/Disc: Tu/Th 9:10-10:25 AM, 204 Johnson Hall (off site via AMS) At least once every other week, we will meet online for R work Office Hours: by appointment Overview

This course is designed for students who want to employ inferential spatial statistical techniques in their research. The emphasis will be on geostatistics, though other spatial statistics will be briefly addressed. As I place a strong emphasis on formulating and testing hypotheses, this course will also provide you with an opportunity to strengthen your basic understanding of random variables and inferential statistics. To successfully use spatial statistics, you must develop a conceptual understanding of the underlying theory and have practical experience working with spatial data. In the lecture/discussion sessions I present the basic theory with a minimum of mathematical derivations and a maximum of conceptual explanations, examples and illustrations. You will have an opportunity to apply these concepts to real spatial data with weekly computer-based R homework assignments. I assume some prior use of R, but students willing to put in the time will be able to learn R in this course. Student Learning Outcomes (SLO’s)

All tests must be implemented using the R statistical package and/or interpretation of R output for in-class exams. Students are expected to be able to interpret associated data representations.

1. Point-Pattern analysis a) Test for clustering and regularity at a range of scales using quadrat analyses.

b) Test for clustering, regularity and interactions using xF , wG and tL functions. 2. Geostatistics

a) Construct empirical semi-variograms and fit appropriate models with uncertainties. b) Test for spatial correlation. c) Construct spatial interpolation models using (i) ordinary kriging; (ii) universal kriging;

and (iii) regression-kriging; (iv) co-kriging; and (v) geographically weighted regression. d) Explain and interpret block, indicator and co-kriging. Recognize when these approaches

are relevant. e) Use generalized least squares regression to make environmental inferences with spatially

correlated residuals. f) Evaluate model uncertainty using cross-validation and/or geostatistical simulation.

Environmental Spatial Statistics, Spring 2017 2

g) Grading Scheme

What % Description

Homework 30 Computer lab assignments, using R

Midterm Exam 20 In-class exam

Take-Home Exam 30 Take home in R (comprehensive), due 4/24

Final Exam 20 In-class exam (2nd half of course)

Computer laboratory exercises will be graded with the following scheme: 9-10 pts for complete assignment with only minor errors; 8-9 pts for complete assignment with one or more major errors; and 8 pts for incomplete assignments.

A take-home exam will posted on the Thursday of week 14. You will have 10 hours to complete the exam, at a time of your convenience, before Monday, 4/24.

Both the midterm and final exam will be designed with a target median of 85 points.

The final course grades will be assigned using a A=90-100%, B=80-90%, C= <80% scale with +/- grades for scores within 2% of the grade boundaries. Grades may be scaled up.

Texts:

Bivand, Roger, Edzer J. Pebesma and Virgilio Gómez-Rubio. Applied Spatial Data Analysis with R. 2nd ed. New York, NY: Springer, 2013. Print.

Notes and discussions on Blackboard. https://lms.wsu.edu/default.asp

Homework Assignments

Computer exercises will usually require substantial time outside of class to finish. All exercises will be done in the R environment using base and contributed packages. I will provide R script for all new techniques, but then expect you incorporate this code on your own in future assignments. A short homework assignment will be posted after each class and should be submitted online (pdf format) before the next class. A few longer assignments will have a week for return and count double. As I will often discuss the exercises in the following class, late assignments will not be accepted. Because life will sometimes intervene, only the top 80 %of approximately 25 assignments will count toward your grade.

To download R 3.3.2 freeware on your own computer go to: http://cran.r-project.org/, select your operating system under Download and Install R, then click on base to download and install the base package. The R CRAN sites contain help manuals for free downloading.

Assistance

Please feel free to email or phone the course instructor. You should also get to know your classmates, exchange contact information, and find “study buddies.”

Environmental Spatial Statistics, Spring 2017 3

Course Outline 2017: (I will adjust the lecture schedule as necessary during the semester.) Week Tuesday Thursday

01/10 Introduction to Spatial Stats & R Random variables, Significance testing

01/17 Visualizing Spatial Data SPP: Poisson Quadrat test

01/24 SPP: Poisson Lack of Fit SPP: Binomial Approximation

01/31 SPP: non-CSR processes SPP: Neighborhood G-hat

02/07 SPP: Neighborhood F-hat SPP: Neighborhood J-K-L-hat

02/14 SPP: Neighborhood Examples SPP: Bivariate Neighborhoods I

02/21 SPP: Bivariate Neighborhoods II SPP: T-squared statistics

02/28 SPP: Bivariate Neighborhoods II SPP: T-squared statistics

03/07 Mid-Term Exam Geo: Empirical Variogram I

03/14 Spring Break Spring Break

03/21 Geo: Empirical Variogram II Geo: Variogram Models

03/28 Geo: Variogram Uncertainty Geo: Variogram examples - interpret

04/04 Geo: Anisotropy Geo: Simple Kriging

04/11 Geo: Ordinary Kriging - Examples Geo: Kriging with drift

04/18 Geo: GLS regression Geo: Kriging examples

04/25 Geo: Sampling Design Geo: Co-Kriging, Block Kriging

05/01 Final Exam, Monday, May 1st, 10:10am-12:10pm

SPP: Spatial Point Patterns, Lat: Lattice Analysis, Geo: Geostatistics

General Policies

STUDENTS WITH DISABILITIES: Reasonable accommodations are available for students with a documented disability. If you have a disability and may need accommodations to fully participate in this class, please visit the Access Center (Washington Building 217) to schedule an appointment with an Access Advisor. All accommodations MUST be approved through the Access Center.

ACADEMIC DISHONESTY: Academic integrity is the cornerstone of the university. Any student who attempts to gain an unfair advantage over other students by cheating, will fail the exam or quiz and be reported to the Office Student Standards and Accountability. Cheating is defined in the Standards for Student Conduct WAC 504-26-010 (3). http://conduct.wsu.edu/default.asp?PageID=338

SAFETY: Classroom and campus safety are of paramount importance at Washington State University, and are the shared responsibility of the entire campus population. WSU urges students to follow the “Alert, Assess, Act” protocol for all types of emergencies and the “Run, Hide, Fight” response for an active shooter incident. Remain ALERT (through direct observation or emergency notification), ASSESS your specific situation, and ACT in the most appropriate way to assure your own safety (and the safety of others if you are able).

Please sign up for emergency alerts on your account at MyWSU. For more information on this subject, campus safety, and related topics, please view the FBI’s Run, Hide, Fight video and visit the WSU safety portal.

APPROPRIATE CLASSROOM CONDUCT: Behavior that persistently or flagrantly interferes with classroom activities is considered disruptive behavior and may be subject to disciplinary action. Such behavior inhibits other students’ ability to learn and an instructor’s ability to teach. A student responsible for disruptive behavior may be asked to leave class pending discussion and resolution of the problem and may be reported to the Office of Student Standards and Accountability.