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Viewing: ST 733 : Spatial Statistics

Last approved: Sat, 16 Sep 2017 08:02:21 GMT

Last edit: Sat, 16 Sep 2017 08:02:21 GMT

Catalog Pages referencing this course
Change Type
Minor
ST (Statistics)
733
020403
Dual-Level Course
Cross-listed Course
No
Spatial Statistics
Spatial Statistics
College of Sciences
Statistics (17ST)
Term Offering
Spring Only
Offered Alternate Even Years
Previously taught as Special Topics?
Yes
1
 
Course Prefix/NumberSemester/Term OfferedEnrollment
ST 810Fall 201213
Course Delivery
Face-to-Face (On Campus)

Grading Method
Graded/Audit
3
15
Contact Hours
(Per Week)
Component TypeContact Hours
Lecture3.0
Course Attribute(s)


If your course includes any of the following competencies, check all that apply.
University Competencies

Course Is Repeatable for Credit
No
 
 
Brian Reich
Associate Professor
Full

Open when course_delivery = campus OR course_delivery = blended OR course_delivery = flip
Enrollment ComponentPer SemesterPer SectionMultiple Sections?Comments
Lecture201No
Open when course_delivery = distance OR course_delivery = online OR course_delivery = remote
Prerequisite: ST 705
Is the course required or an elective for a Curriculum?
No
Introduction to the theory and methods of spatial data analysis including: visualization; Gaussian processes; spectral representation; variograms; kriging; computationally-efficient methods; nonstationary processes; spatiotemporal and multivariate models.

No

Is this a GEP Course?
GEP Categories

Humanities Open when gep_category = HUM
Each course in the Humanities category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

Mathematical Sciences Open when gep_category = MATH
Each course in the Mathematial Sciences category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

Natural Sciences Open when gep_category = NATSCI
Each course in the Natural Sciences category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

Social Sciences Open when gep_category = SOCSCI
Each course in the Social Sciences category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

Interdisciplinary Perspectives Open when gep_category = INTERDISC
Each course in the Interdisciplinary Perspectives category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

 
 

 
 

Visual & Performing Arts Open when gep_category = VPA
Each course in the Visual and Performing Arts category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
 

 
 

Health and Exercise Studies Open when gep_category = HES
Each course in the Health and Exercise Studies category of the General Education Program will provide instruction and guidance that help students to:
 
 

 
 

 
 

 
 

 
&
 

 
 

 
 

 
 

Global Knowledge Open when gep_category = GLOBAL
Each course in the Global Knowledge category of the General Education Program will provide instruction and guidance that help students to achieve objective #1 plus at least one of objectives 2, 3, and 4:
 
 

 
 

 
Please complete at least 1 of the following student objectives.
 

 
 

 
 

 
 

 
 

 
 

US Diversity Open when gep_category = USDIV
Each course in the US Diversity category of the General Education Program will provide instruction and guidance that help students to achieve at least 2 of the following objectives:
Please complete at least 2 of the following student objectives.
 
 

 
 

 
 

 
 

 
 

 
 

 
 

 
 

Requisites and Scheduling
 
a. If seats are restricted, describe the restrictions being applied.
 

 
b. Is this restriction listed in the course catalog description for the course?
 

 
List all course pre-requisites, co-requisites, and restrictive statements (ex: Jr standing; Chemistry majors only). If none, state none.
 

 
List any discipline specific background or skills that a student is expected to have prior to taking this course. If none, state none. (ex: ability to analyze historical text; prepare a lesson plan)
 

Additional Information
Complete the following 3 questions or attach a syllabus that includes this information. If a 400-level or dual level course, a syllabus is required.
 
Title and author of any required text or publications.
 

 
Major topics to be covered and required readings including laboratory and studio topics.
 

 
List any required field trips, out of class activities, and/or guest speakers.
 

This course has been taught as a special topics in the past every two to three years and has been part of a faculty member's regular course load.

Students will learn about the foundations of spatial statistics as well as some practical work with real data.  They will be able to use SAS to conduct most of the analyses .  This course will prepare them to do standard spatial analyses as well as prepare them to write a thesis in spatial statistics.


Student Learning Outcomes

By the end of the course, the students will be able to:


(1) Use statistical packages (SAS or R) to visualize spatial data; estimate model parameters; and perform spatial prediction


(2) Derive properties (covariance, smoothness, stationarity) of models for spatial and spatiotemporal data.


(3) Identify appropriate statistical model for complex spatial data, and use graphics and statistical tests to justify model choice.


(4) Clearly present the results of an independent research project applying the methods learned in class to real data.


Evaluation MethodWeighting/Points for EachDetails
Homework20%
Midterm40%
Project40%
TopicTime Devoted to Each TopicActivity
(1) Introduction to spatial data analysis (objectives; visualization; map projections)1 week
(2) Gaussian processes (definition; properties; representations such as spectral and convolution)1 week
(3) Stationary covariance functions (Matern covariance; smoothness properties; positive definiteness)1 week
(4) Estimation and prediction (variograms; maximum likelihood; kriging)1 week
(5) Bayesian methods (posterior distributions; MCMC)1 week
(6) Methods for large datasets (low-rank models; likelihood approximations)1 week
(7) Non-Gaussian data (hierarchical models)1 week
(8) Non-stationary covariance functions (deformations; locally-stationary processes)1 week
(9) Spatiotemporal data (separability; Markov models; non-separable models)1 week
(10) Multivariate data (separability; spatial factor analysis)1 week
(11) Areal spatial data (Brooks' Lemma; conditionally autoregressive models; autologistic models)1 week
(12) Special topics (selected by the instructor)3 weeks
We have converted the old applied version of ST 733 to ST 533 (in submission to CIM). The new ST 533 is aimed at graduate students outside the Statistics Department as well as Statistics masters students. This new version of ST 733 includes material previously taught in special topics courses and is aimed at Statistics PhD students.

ghodge 09/11/2015 This CAF should be considered with the new ST 533. Consultation should be provided in the CAF for ST 533 from programs impacted by this change. Hold until CAF for 533 is available, then send to ABGS reviewers.

update 2/17/2016: department still working on CAF for ST 533.

update 10/5/2016: college still working on the CAF for ST 433/533.

ABGS Reviewer Comments:
-No comments/concerns.
mlnosbis (Mon, 26 Jun 2017 11:46:30 GMT): Rollback: ST 552 is not an active course, so it should be removed from the Prerequisite list.
mlnosbis (Tue, 27 Jun 2017 12:09:46 GMT): Rollback: Rollback as requested by Dennis Boos.
mlnosbis (Tue, 27 Jun 2017 12:54:54 GMT): This is a minor action updating the prerequisites.
Key: 5138