GEOG 586 - Geographic Information Analysis
This is a sample syllabus.
This sample syllabus is a representative example of the information and materials included in this course. Information about course assignments, materials, and dates listed here is subject to change at any time. Definitive course details and materials will be available in the official course syllabus, in Canvas, when the course begins.
Overview
Geography 586 is a required course in the Penn State Professional Masters in Geographic Information Systems. The course is organized around six short weekly projects and a more substantial term project pursued through all ten weeks of the course.
This is a course in analytical methods for handling spatial data analysis. The techniques introduced are often mathematically complex, but while these aspects are covered in the course, the emphasis is on the choice and application of appropriate methods for the analysis of the spatial data often encountered in applied geography.
Objectives
Students who excel in this course are able to:
- Analyze methods of handling spatial data analysis.
Required Materials
The materials listed here represent those that may be included in this course. Students will find a definitive list in the course syllabus, in Canvas, when the course begins.
Required Textbook
Lloyd, Christopher D. 2011, Local Models for Spatial Analysis, Second Edition. (CRC Press, Boca Rotan, FL). ISBN 978-1-4398-2919-6.
Required Software
- R Studio and R the free statistical analysis software package.
- GeoDa, free exploratory spatial data analysis software available from Chicago University: The Center for Spatial Data Science. Once at the GeoDa homepage, click on the Software tab to visit the software download page.
- Esri's ArcGIS and/or ArcGISPro that includes the Geostatistical Analyst and Spatial Analyst extension. There is no fee for current Penn State students.
Prerequisites
GEOG 485 or GEOG 486 or GEOG 487 or equivalent experience
Expectations
We have worked hard to make this the most effective and convenient educational experience possible. How much and how well you learn is dependent on your attitude, diligence, and willingness to ask for clarifications or help when you need them. We are here to help you succeed. Please keep up with the class schedule and take advantage of opportunities to communicate with us and with your fellow students. You can expect to spend an average of 8 - 12 hours per week on class work.
Major Assignments
Your course grade will be based on four components:
Weekly Quizzes - 20% of course grade.
The primary purpose of the quizzes is to encourage you to do the reading and and to test your knowledge.
6 Weekly Project activities - 45% of course grade
- Week 1: Spatial Analysis and why it is special: Exploring the Modifiable Area Unit Problem.
- Week 2: Geospatial Data Science and Analysis: integrating the two and understanding what is in your data. An exercise using R and applying descriptive statistics.Week 3: Geospatial Data Science and Spatial Statistical Analysis: Point Patterns and why statistical analysis is important for quantifying what you see. An exercise using R to examine crime in St Louis using point pattern analysis.
- Week 4: Cluster Analysis & Spatial Autocorrelation: An exercise applying spatial autocorrelation measures to assessing ethnic segregation in Auckland, New Zealand using GeoDa.
- Week 6: Interpolation Methods simple to advanced: Practical experience with interpolation methods so that you develop a feel for the characteristics of the surfaces produced by different methods using ArcGIS/ArcGIS Pro.
- Week 7: Surface Analysis: Using surface analysis to determine potential sites for a new school in the Centre County region of Pennsylvania (around Penn State). This will show how complex analysis tasks can be performed by combining results from a series of relatively simple analysis steps using ArcGIS /ArcGIS Pro.
Weekly projects must be submitted by the stated deadline. Please see each week's rubric for details on the possible points and further guidance on the lesson expectations.
When submitting each project, you should use the drop box supplied in that week's lesson materials. You may submit in either PDF or Word doc formats.
1 Term Project - 30% of course grade
Throughout this course, a major ongoing activity is a personal GIS project that you will develop and research on your own (with lots of input from everyone else taking the course!). This is a more substantial piece of work than the weekly projects, where you are expected to apply concepts learned in the course. By prior agreement with me, ideas and methods not explicitly covered in the course, but which fall under the description 'spatial analysis' may also be used. Many students enrolled in the MGIS program find this project to be a great opportunity to explore an idea for their capstone project. You should expect to work on different activities in the term-long project each week. This is designed to keep you on track, so that you work consistently on the project throughout the term rather than leaving it all to the last minute. Waiting until the last minute is never a good practice in spatial statistics.
Participation - 5% of course grade
Class participation is expected throughout the course on discussion forums and in feedback to other students.
Course Schedule
| Week | Topic | Assignment |
|---|---|---|
| 0 | Orientation |
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| 1 | Why spatial data is special |
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| 2 | Geospatial Data Analysis: Dealing with data |
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| 3 | Point Pattern analysis and why statistical analysis is important |
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| 4 | Cluster Analysis & Spatial Autocorrelation |
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| 5 | Regression Analysis |
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| 6 | Interpolation: Simple to Advanced |
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| 7 | Surface Analysis |
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| 8 | Overlay analysis |
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| 9 | Project work time (no new content this week) |
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| 10 | Putting it all together: applied research using GIS |
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