Syllabus

GEOG 4023/5023
Quantitative Methods in Geography.
Tu/Th 12:30-1:45 in Gugg 205
Office Hours: Mon: 1:00-2:00;
Thu: 10:00-11:00 & by appointment
Office: Gugg 201B
Phone: 303-893-4519

Geog 4033/5033
Quantitative Methods Lab
Tu: 2:00-5:00 in KESDA

Teaching Assistant:
Bryan Jones
Office Hours: TBA

Prerequisite:
An introductory course in statistics or my permission. You should remember a little about t-tests and simple linear regression.

Course Content:

This course covers commonly used quantitative techniques in geographical analysis. The course begins with a review of elementary statistics and regression modeling, before moving on to a detailed critique of these methods from the geographer’s perspective, and continues through elementary methods for spatial, such as kriging, autoregressive function modeling and Geographically Weighted Regression, and methods for temporal data, such as ARMA modeling and spectral analysis. I commonly get asked about Markov Chain Monte Carlo methods. Unfortunately, we will not have time to cover this.

What is Statistics?

My working definition of (inferential) statistics is this:

Statistics is the art of applying probability theory to data, with the purpose of convincing others that your story is plausible.

Statistics involves looking at data through the lens of probability theory. This is the scariest sounding part. But I will work you through it, and there really isn’t that much to it. A useful metaphor here is that statistics is translation; you must translate between the language of your scientific knowledge of the world, and the language of probability theory (and then back again).

The ultimate goal of statistics is to convince others that you are right. In this regard, it is one of many rhetorical tools we have at our disposal, but in our modernist era, the logic of statistics is a powerful tool. Your story may be convincing only if others believe that your translation is reasonably faithful to the original.

Ultimate Course Objective

To understand how a statistical approach may be critically appraised through the comparison of the model’s assumptions with your scientific knowledge.

Course Readings

Option A

The required textbook is

Waller, Lance A. and Carol A. Gotway. Applied Spatial Statistics for Public Health Data. Wiley: Hoboken, NJ.

Crawley, Michael. The R Book

Don’t let the first title upset you. There is very little in the book that is specific to Public Health, and Carol Gotway is a first rate spatial statistician who tends to think like a geographer. I chose this book because each chapter begins easy enough that I can introduce it in one semester, yet it is referenced to such a degree that it will guide you toward more advanced topics after you finish this class.

Option B

If you are here to fulfill a requirement, and aren’t interested in making a textbook investment but will be selling your books the day class ends, I have asked for a few copies of an alternate, cheaper text to be made available at the bookstore.

This book is a little simpler, but nowhere near as comprehensive as the Waller and Gotway text, but it will do the job in this class most of the time. If you choose to go for this option, find a friend who is willing to let you borrow their Waller and Gotway text every so often to make sure you aren’t missing anything.

Grading in 4023/5023

Mid-term exam : 30%

Final exam: 30%

Homework: 30%

Paper Review: 10%

Late Policy: If you turn your assignment in on time, they will be graded and returned within one week. If you are late, they may not be returned until after “I get around to grading it” which may be during finals week.

If you must miss an exam, I will need to know about it by the end of the third week of class. The content of make-up exams is up to the instructor’s discretion, and may include oral and/or written components.

Grading in 4033/5033

Assignments: 100%

Late Policy:

Since assignments may be cumulative, I will not accept any late homework assignments.

Schedule

Week 1 – Tu: Introduction. Spatial Data, Vectors and Matrices

Lab 0: Introduction to R

Th: The t-test and its assumptions

Week 2 – Tu: “Variations on a Mean:” Bootstrapping and Bayesian Inference

Lab 1: “Variations on a Mean”

Th: The Linear Regression Model and its assumptions WG 9.1

HW 1 Due

Week 3 – Tu: The Confounded Linear Regression Model

Lab 2: The Linear Regression Model

Th: Design Effects in the Linear Regression Model

Week 4 – Tu: Spatial Random Fields & Variograms WG 8.1-8.2

Lab 3: Variograms

Th: Fitting a Variogram

HW 2 Due

Week 5 – Tu: Kriging WG 8.3

Lab: Kriging

Th: Getting Intimate with Kriging

Week 6 – Tu: Linear Spatial Regression Models & GLS WG 9.2

Lab: Stochastic Simulation

Th: Lattice Processes: Spatial weights and Global Moran’s I

WG – 7.4 also WG 4.4.1

HW 3 Due

Week 7 - Lattice Processes: Local Moran’s I WG 7.5

Lab: Moran’s I

Th: Mid-term

Week 8 – Tu: Lattice Processes: Spatial Autoregressive Models WG 9.3

Lab: CAR & SAR

Th: GWR and its assumptions

HW 4 Due

Week 9 – Tu: Time Series: Autocorrelation

Th: Time Series: ARMA Models

Week 10 – Tu: Time Series in the Frequency Domain

Lab: ARMA & Frequency Domain

Th: Bootstrapping temporal and spatial data

HW 5 Due

Week 11 – Generalized Linear Models

WG 9.4 also 2.6.1

Week 12 Tu: Spatial Point Patterns – Intensity

Th: Spatial Point Patterns – Clustering

WG 5.1-5.3

HW 6 Due

Week 13 Principal Components, EOFs and their assumptions

Week 14 Sample Design

Th: Sample Design

HW 7 Due

Week 15 Tu: You mean that wasn’t enough!? Well then, maybe I can lecture on Survival Analysis or something similarly interesting.

Th: Slack Day in anticipation of travel.

Campus Policies

Disabilities If you qualify for accommodations because of a disability, please submit to me a letter from Disability Services in a timely manner so that your needs may be addressed. Disability Services determines accommodations based on documented disabilities. Contact: 303-492-8671, Willard 322, and www.colorado.edu/disabilityservices Disability Services’ letters for students with disabilities indicate legally mandated reasonable accommodations. The syllabus statements and answers to Frequently Asked Questions can be found at www.colorado.edu/disabilityservices.

Religious Observances Campus policy regarding religious observances requires that faculty make every effort to deal reasonably and fairly with all students who, because of religious obligations, have conflicts with scheduled exams, assignments or required attendance. See full details at http://www.colorado.edu/policies/fac_relig.html

Classroom Behavior Students and faculty each have responsibility for maintaining an appropriate learning environment. Those who fail to adhere to such behavioral standards may be subject to discipline. Professional courtesy and sensitivity are especially important with respect to individuals and topics dealing with differences of race, culture, religion, politics, sexual orientation, gender, gender variance, and nationalities. Class rosters are provided to the instructor with the student’s legal name. I will gladly honor your request to address you by an alternate name or gender pronoun. Please advise me of this preference early in the semester so that I may make appropriate changes to my records. See policies at http://www.colorado.edu/policies/classbehavior.html and at http://www.colorado.edu/studentaffairs/judicialaffairs/code.html#student_code

Discrimination and Harassment The University of Colorado at Boulder policy on Discrimination and Harassment, the University of Colorado policy on Sexual Harassment and the University of Colorado policy on Amorous Relationships apply to all students, staff and faculty. Any student, staff or faculty member who believes s/he has been the subject of discrimination or harassment based upon race, color, national origin, sex, age, disability, religion, sexual orientation, or veteran status should contact the Office of Discrimination and Harassment (ODH) at 303-492-2127 or the Office of Judicial Affairs at 303-492-5550. Information about the ODH, the above referenced policies and the campus resources available to assist individuals regarding discrimination or harassment can be obtained at http://www.colorado.edu/odh

Honor Code All students of the University of Colorado at Boulder are responsible for knowing and adhering to the academic integrity policy of this institution. Violations of this policy may include: cheating, plagiarism, aid of academic dishonesty, fabrication, lying, bribery, and threatening behavior. All incidents of academic misconduct may be reported to the Honor Code Council (honor@colorado.edu; 303-725-2273). Students who are found to be in violation of the academic integrity policy will be subject to both academic sanctions from the faculty member and non-academic sanctions (including but not limited to university probation, suspension, or expulsion). Academic sanctions may include (but are not limited to) a failing grade for the assignment or for the entire course. Other information on the Honor Code can be found at http://www.colorado.edu/policies/honor.html and at http://www.colorado.edu/academics/honorcode/

Fine Print

The above schedule, policies, procedures, and assignments in this course are subject to change in the event of extenuating circumstances, by mutual agreement, and/or to ensure better student learning.