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Viewing: GIS 713 : Geospatial Data Mining

Last approved: Mon, 12 Mar 2018 08:00:44 GMT

Last edit: Wed, 07 Mar 2018 15:28:23 GMT

Catalog Pages referencing this course
Change Type
GIS (Geographic Information Systems)
713
032570
Dual-Level Course
Cross-listed Course
No
Geospatial Data Mining
Geospatial Data Mining
College of Natural Resources
Parks, Recr & Tourism Mgmt (15PRT)
Term Offering
Fall and Spring
Offered Every Year
Fall 2018
Previously taught as Special Topics?
Yes
1
 
Course Prefix/NumberSemester/Term OfferedEnrollment
GIS 595Fall 1712
Course Delivery
Face-to-Face (On Campus)

Grading Method
Graded/Audit
3
16
Contact Hours
(Per Week)
Component TypeContact Hours
Lecture3
Course Attribute(s)


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

Course Is Repeatable for Credit
No
 
 
Josh Gray
Assistant Professor
full

Open when course_delivery = campus OR course_delivery = blended OR course_delivery = flip
Enrollment ComponentPer SemesterPer SectionMultiple Sections?Comments
Lecture2020NoNA
Open when course_delivery = distance OR course_delivery = online OR course_delivery = remote


Is the course required or an elective for a Curriculum?
Yes
SIS Program CodeProgram TitleRequired or Elective?
15GAPHDPhD in Geospatial AnalyticsRequired
This course equips students with the theoretical background and practical computational skills required to use data mining methodologies, including clustering, PCA, spatial autocorrelation, neural networks, classification and regression trees, and high performance, open source geocomputation. The course is designed around, and pays particular attention to, approaches for data with spatial components. Students are expected to have a working knowledge of basic geographic principles, statistical principles, GIS, and remote sensing. Some experience with R programming would also be beneficial.

This course is part of the core requirements for the new PhD in Geospatial Analytics


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.
 

College(s)Contact NameStatement Summary
College of SciencesBrian Reich and Joseph GuinessThere are a couple of topics that overlap but the bulk of the focus is different than their current offerings. Approve of the new course.
College of SciencesWenbin LuI have reviewed the syllabus of the course. I think some of our stat students may also be interested in the class.
College of EngineeringGeorge RouskasI just spoke with Raju, who teaches a similar special topics course, and I understand that while his special topics course and GIS 713 will cover similar topics, the focus is quite distinct. Therefore, I have no objections to GIS 713.
No new resources needed, this is part of the normal teaching load of the faculty

This course is designed to equip future geospatial data scientists with the analytical and computational skills necessary to conduct their work. Geospatial data mining is a diverse and rapidly growing field. Students will be introduced to the field and its considerable breadth by surveying a variety of methods and their application to particular ecological problems. Additionally, students will gain experience analyzing and visualizing geospatial data with open source programming languages.


Student Learning Outcomes

Students will be able to: 



  • Implement various geospatial data mining techniques (e.g. principal components, point-pattern analysis, machine learning-based classification), and demonstrate an understanding of their underlying statistical theory through application to novel scientific problems and datasets.

  • Read, summarize, and critique important contributions in the contemporary geospatial data mining literature.

  • Create computer codes for geospatial data analysis and visualization in R and Python.

  • Develop and refine oral and written data presentation skills through in-class presentations and a written final project.


Evaluation MethodWeighting/Points for EachDetails
Homework30%There will be 3 homework assignments. The deliverable is a writeup including computer code, any required statistics and plots, and a brief narrative where necessary. Grade will reflect: completeness, accuracy, and style.
Midterm25%The midterm will be cumulative. It may include take-home data analysis in addition to in-class components.
Participation10%Students are expected to attend class, participate in discussions in-class, and in online forums.
Project35%The final project will apply concepts developed throughout the semester to a topic relevant to student’s research. A journal-style manuscript, associated analysis code, and an in-class presentation are the required deliverables. Several check-points throughout the semester will track progress and will also count towards the final project grade.
TopicTime Devoted to Each TopicActivity
See syllabusSee syllabusSee syllabus
We are proposing to offer this course normally every Spring, however, we would like to go ahead and activate the course for Fall 18, because, due to instructor availability, one of our other new courses will not be taught this Fall, so we would like to offer this course in its place just this one Fall so the first cohort of PhD students will stay on track in the first year.

mlnosbis 2/5/2018:
1) If you want this offered in Fall 2018, you will need to change the offering on the CIM form to Fall and Spring. Then, after it is approved and scheduled for Fall 2018, you can enter a minor course action to change the offering back to Spring only.
2) Syllabus notes:
- Include the course number
- Include more explanation of homework assignments and the final project/paper

cohen 2/05/2018:
I would suggest being a little more explicit with what it means to have "some familiarity with programming fundamentals."
The course was previously taught as a 500-level special topics course. The program may wish to submit a course action for a 700-level special topics shell (to use to pilot 700-level courses for the new PhD program).

esmoney 2/07/2018:
1. Updated the CIM record to reflect Fall and Spring temporarily
2. Added Course Number to Syllabus
3. Added an attached supplemental document with more detail on homeworks and final project.
4. New version of syllabus uploaded (v3)
5. Adjusted the catalog description to say 'Some experience with R programming would also be beneficial' really the intent is just some experience working in R
6. Thanks for the suggestion and we will definitely look into creating a 700 special topics shell for future PhD courses beyond these Core courses that were already vetted as part of the PhD proposal process.

ABGS Reviewer Comments 2/19/2018:
-No concerns.
Key: 23465