COURSE SYLLABUS FOR GEOG 586 - Fall 2008

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Course Overview | Objectives | Materials | Technical Requirements | Assignments | Course Schedule | Policies

Course Overview

GEOG 586: GEOGRAPHIC INFORMATION ANALYSIS. Choosing and applying analytical methods for geospatial data, including point pattern analysis, interpolation, surface analysis, overlay analysis, and spatial autocorrelation.
Prerequisite: GEOG 485 or GEOG 486 or GEOG 487

Geography 586 is a required course in the Penn State Professional Masters in Geographic Information Systems. This section is being offered to students around the globe through Penn State's World Campus. It is a "paced" course, which means that there is an established start and end date and that you will interact with other students throughout the course. The course is 10 weeks in length (plus a required "Orientation Week" preceding the start of the course), at a rate of 1 lesson per week. The course is organized around eight short weekly projects and a more substantial project pursued through the all ten weeks of the course, with milestones through the quarter. Weekly projects include associated readings, quizzes, and discussions about the analysis of spatial data.

This is a course in analytical methods for handling specifically spatial data, that is, data where the arrangement of observations in space is thought to be of significance. 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. Weekly projects are hands-on using geographic information systems or other appropriate computational tools, so that students appreciate the practical complexities involved, and the relative limitations of these methods in contemporary desktop GIS.

Through the weekly projects, students acquire familiarity with use of a single method or family of methods in standard desktop tools, so that they can focus on aspects of that method and develop a thorough understanding of its potential and of its limitations. Problem scenarios range across demographic, planning, crime analysis, landscape analysis, and other application areas. The quarter-long project is intended to allow students to formulate a research problem in a topic area of their own choosing, to gather and organize appropriate available datasets, and to understand how a variety of methods among those covered in the course can be applied in combination to thoroughly explore real questions. Students will be asked to engage with their peers' work during the project planning stage. They will also be encouraged to consider developing customized tools to automate repetitive analysis tasks, if they have previous programming experience.

The course materials consist of a textbook; ESRI ArcGIS with Spatial Analyst, and Geostatistical Analyst extensions; a modern spreadsheet program (Microsoft Excel, Openoffice.org Calc or similar); GeoDa a free exploratory spatial data analysis software available from University of Illinois at Urbana-Champaign Spatial Analysis Laboratory; and a required course Web site that contains the on-line lessons and communications tools, such as message boards and an e-mail system.

The course textbook that closely corresponds with much of the course content (while also covering further topics in this broad field) has been co-authored by the course developer:

O'Sullivan, D. and Unwin, D. J., 2002, Geographic Information Analysis, (Wiley, Hoboken, NJ).

What will be expected of you?

This course requires a minimum of 8-12 hours of student activity each week, depending on the speed at which you work. Included in the 8-12 hours each week is time to complete projects and related activities. Some weeks you may spend less time than that, so keep this in mind in the tougher weeks (when you'll be making up the difference!). You'll be glad to know that you don't have to show up for class at a certain time! All you need to do is complete each project and a quiz before the published deadline at the end of the week.

You will need to check out the course discussion forums regularly. That's where students and instructors share comments, pose questions, and suggest answers. I strongly encourage you to get in the habit of logging in to the course Web site every day to check in on the class. With only occasional exceptions, I check message boards six days a week. You can be sure that I will read, but not necessarily respond to, every single message. If I anticipate not logging in for more than a day, I will let you know and also clearly state when you can next expect to hear from me.

My colleagues and I have worked hard to make this the most effective and convenient educational experience possible. How much and how well you learn is ultimately up to you. You will succeed if you are diligent about keeping up with the class schedule, and if you take advantage of opportunities to communicate with me, as well as with your fellow students.

For a more detailed look at what will be covered in each lesson, as well as due dates for our assignments and activities, please refer to the semester-specific course schedule that is part of this syllabus (see "Course Schedule").

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Course Objectives

Lesson 1

1. differentiate point, line and area objects, and fields and give examples;
2. differentiate nominal, ordinal, interval and ratio attribute data and give examples;
3. distinguish between spatial objects and spatial fields and discuss the merits of each as a representation;
4. list four major problems in the statistical analysis of geographic information: autocorrelation, the modifiable areal unit problem, scale dependence, edge effects;
5. outline the concepts of distance, adjacency, interaction and neighborhood and show how these can be recorded using matrices;
6. explain how proximity polygons and Delaunay triangulation are developed for point objects, and relate this technique to concepts of distance, adjacency, interaction and neighborhood;
7. explain how point data sets can be converted into a set of areal units using proximity polygons;
8. describe how a set of areal units can be converted into a set of point data by assignment of values to centroids;
9. outline how point data may be converted to field data by density estimation;

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Lesson 2

1. explain basic concepts in inferential statistics including descriptive statistics, and samples and populations;
2. explain a variety of descriptive statistics (measures of central tendency and spread, the concept of outliers);
3. explain how mapping and visualization is 'descriptive statistics for spatial data,' particularly when the determination of class boundaries in choropleth mapping is used intelligently;
4. construct and interpret a variety of statistical displays (bar charts, histograms, box plots, scatter plots);
5. use a GIS to explore spatial patterns—for example by interactively adjusting map legends, using selection tools in attribute tables, and using the 'brushing' capabilities of desk-top GIS;
6. describe popular visualization methods for multivariate data;

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Lesson 3

1. describe and provide examples of simple deterministic and stochastic spatial processes;
2. list the two basic assumptions of the independent random process (i.e. no first or second order effects);
3. outline the logic behind derivation of long run expected outcomes of the independent random process using the quadrat counts for a point pattern as an example;
4. outline how the idea of a stochastic process might be applied to line, area and field objects;

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Lesson 4

1. define point pattern analysis and list the conditions necessary for it to work well;
2. explain how quadrat analysis of a point pattern is performed and distinguish between quadrat census and a quadrat sampling methods;
3. discuss relevant factors in determining an appropriate quadrat size for point pattern analysis;
4. describe in outline kernel density estimation and understand how it transforms point data into a field representation;
5. describe distance-based measures of point patterns (mean nearest neighbor distance and the G, F and K functions);
6. explain how distance-based methods of point pattern measurement are derived from a distance matrix;
7. describe how the independent random process and expected values of point pattern measures are used to evaluate point patterns, and to make statistical statements about point patterns;
8. explain how Monte Carlo methods are used when analytical results for spatial processes are difficult to derive;
9. justify the stochastic process approach to spatial statistical analysis;
10. discuss the merits of point pattern analysis versus cluster detection, and outline the issues involved in real world applications of these methods;

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Lesson 5

1. explain the concept of a spatial average and describe different ways of deciding on inclusion in a spatial average;
2. describe how spatial averages are refined by inverse distance weighting methods;
3. outline the basis of interpolation by spline-fitting, or piece-wise polynomial fitting;
4. explain why the above interpolation methods are somewhat arbitrary and must be treated with caution;
5. show how regression can be developed on spatial co-ordinates to produce the geographical technique known as trend surface analysis;
6. explain how a variogram cloud plot is constructed and, informally show how it sheds light on spatial dependence in a dataset;
7. outline how a model for the semi-variogram is used in kriging and list variations on the approach;
8. make a rational choice when interpolating field data between inverse distance weighting, trend surface analysis, and geostatistical interpolation by kriging;
9. explain the conceptual difference between interpolation and density estimation;

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Lesson 6

1. describe data models for field data: regular grid, triangulated irregular network, closed form mathematical function, control points; and discuss how the choice of model may affect subsequent analysis;
2. explain the map algebra concept and describe focal operations, local operations and between-map operations;
3. understand the idea of slope and aspect as a vector field;
4. explain how slope or gradient can be determined from a grid of height values;
5. describe how surface aspect may be derived from a grid of height values;
6. re-express these operations as local operations in map algebra;
7. describe how map algebra operations can be combined to develop complex functionality;

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Lesson 7

1. formally describe Boolean map overlay;
2. explain how this technique has been widely used in suitability mapping;
3. understand why co-registration of input maps in overlay is critical to the success of the analysis;
4. describe how co-registration is achieved by a combination of translation, rotation, and scaling transformations;
5. outline how overlay is implemented in vector and raster GIS;
6. outline approaches to overlay based on alternative ways of combining layers-additive and indexed schemes, fuzzy methods, and weights of evidence methods;
7. describe how model-based overlay approaches and regression are related to one another;

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Lesson 8

1. define autocorrelation with reference to Tobler's 'first law' of geography and distinguish between first and second order effects in a spatial distribution;
2. differentiate between isotropic and anisotropic spatial distributions;
3. justify, compute and test the significance of the joins count statistic for a pattern of area objects;
4. compute Moran's I and Geary's C for a pattern of attribute data measured on interval or ratio scales;
5. explain the importance of spatial weights matrices to the development of autocorrelation measures and variations of the approach, particularly lagged autocorrelation;
6. explain how autocorrelation measures can be generalized to compute and map Local Indices of Spatial Association (LISA);
7. describe how Monte Carlo methods may be used to determine significance for LISA;

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Required Course Materials

In order to take this course, you need to have the required course materials and an active Penn State Access Account user ID and password (used to access the on-line course resources). Approximately two weeks prior to the course start date the World Campus will mail a course Welcome Letter to you, which includes important information about the course and step-by-step directions for how to begin!

If you do not receive your Welcome Letter, please contact the World Campus' Student Services group immediately so that they can send you the information you need. They can be reached at 1-800-252-3592 in the US or internationally at 814-865-5403 (country code 1). You may reach them by e-mail at psuwd@psu.edu.

You will need to purchase course materials from MBS Direct (the bookstore used by Penn State's World Campus). For pricing and ordering information, please see MBS Direct. MBS Direct can also be contacted at 1-800-325-3252. Materials will be available at MBS Direct approximately three weeks before the course begins. Be sure to order early enough to allow for shipping and installation prior to the course start date.

The required materials you need to purchase from MBS Direct are:

• O'Sullivan, D. and D. J. Unwin. 2002. Geographic Information Analysis (Wiley: Hoboken, NJ).

You will also need to have access to the following software:

• A modern spreadsheet program such as Microsoft Excel, Openoffice.org Calc (this is free)or similar;
• GeoDa, free exploratory spatial data analysis software available from University of Illinois at Urbana-Champaign Spatial Analysis Laboratory

In addition to these course materials, you will be using ESRI's ArcGIS and Geostatistical Analyst software in this course. As a registered student in GEOG 586, you will be able to use this software using a Virtual Private Network (VPN) solution (served from the John A. Dutton e-Education Institute, College of Earth and Mineral Sciences). Specific instructions and points of contact for using this solution will be provided . NOTE: The point of contact for support on this is Marty Gutowski at mjg8@psu.edu.

NOTE: ArcGIS is a commercial software package that is restricted to personal use by the student. It is unlawful for anyone to use this software package without the appropriate commercial license from ESRI Inc. to generate personal or corporate profit or revenue

Using the Library

Many of Penn State's library resources can be utilized from a distance. Through the Library Resources and Services for World Campus and Distance Education site, you can...

• access electronic databases, and even full text articles, from the LIAS Fast Track,
• borrow materials and have them delivered to your doorstep...or even your desktop,
• access materials that your instructor has put on Electronic Reserve,
• talk to reference librarians in real time using the "Virtual Reference Service",
• ...and much more.

NOTE: You must be registered with the University Libraries in order to take full advantage of the Libraries' resources and services. Registration and services are free.

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Technical Requirements

For this course we recommend the minimum technical requirements outlined on our "Program Technical Requirements" page, located at
http://www.e-education.psu.edu/courses/gis/techspecs.html

Not sure if your computer is set up correctly? You can use the links below to test your settings:

1. Adobe Acrobat
2. Frames
3. Java [This may take a minute to load.]
4. JavaScript

If you need technical assistance at any point during the course, please contact the World Campus Help Desk.

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Assignments

Your course grade will be based on four components:

• Weekly Quizzes - 20% of course grade. Note that the primary purpose of the quizzes is to encourage you to do the reading, not to test your knowledge - you get as many goes as you need to score 100% on the quizzes, so this is intended for you to use as a way of reviewing the reading, not as a 'test'.
• 6 Weekly Project activities - 45% of grade (7.5% each):
  • Week 1: Spatial data conversion using census data to construct population surfaces.
  • Week 3: A spreadsheet exercise to help you understand the concept of a spatial process better.
  • Week 4: You will analyze some 1980s St Louis crime data, to demonstrate some of the point pattern analysis methods that have been discussed in this week's lesson.
  • Week 5: The aim of this week's project is to give you practical experience with interpolation methods, so that you develop a feel for the characteristics of the surfaces produced by different methods.
  • Week 6: In this project we will use surface analysis based 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.
  • Week 8: An exercise applying spatial autocorrelation measures to assessing ethnic segregation in Auckland, New Zealand.
• 1 Quarter Project - 30% of 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 ideas and methods 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. Each week, there are 'milestone' activities in the quarter-long project to keep you on track, so that you work consistently on the project throughout the quarter rather than leaving it all to the last minute.
• Participation - 5% of course grade: Class participation is expected throughout the course on message boards and in feedback to other students. While posting questions is participation, you should also try to help others out with their questions. Consistently constructive input will be the most valued!

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Course Schedule

Below you will find a summary of the learning activities for this course and the associated time frames. Specific details for each project can be found in each lesson.

• Orientation week begins October 1^st, 2008
• Course begins October 8^th, 2008
• Course ends December 16^th, 2008
• Course length: 10 weeks

Orientation Week

TIME FRAME: October 1^st to 7^th, 2008

ASSIGNMENTS and ACTIVITIES

• Complete the steps outlined in our site orientation. (Required!)

Lesson 1: Why spatial data is special

TIME FRAME: October 8^th to 14^th, 2008

ASSIGNMENTS and ACTIVITIES

• Complete the two self-test quizzes satisfactorily (you have an unlimited number of attempts and must score 90% or more).
• Complete the Project 1 activities. This includes creating two maps along with accompanying commentary, and inserting them into a write-up.
• Not a deliverable... but please take time to familiarize yourself with the schedule for the quarter-long project. In addition, please post a brief personal introduction to our course message board and then review those your peers have posted to learn more about them.

Lesson 2: A rough guide to descriptive statistics

TIME FRAME: October 15^th to 21^st, 2008

ASSIGNMENTS and ACTIVITIES

• Complete the self-test quiz satisfactorily (you have an unlimited number of attempts and must score 90% or more).
• Provide one or more suggested topics for your quarter-long project.

Lesson 3: Classical spatial analysis

TIME FRAME: October 22^nd to 28^th, 2008

ASSIGNMENTS and ACTIVITIES

• Complete the self-test quiz satisfactorily (you have an unlimited number of attempts and must score 90% or more).
• Complete the Project 3 activities. This includes working with a spreadsheet program to develop a better understanding of the concept of a spatial process.
• For the quarter-long project you should submit a brief project proposal.

Lesson 4: Point pattern analysis

TIME FRAME: October 29^th to November 5^th, 2008

ASSIGNMENTS and ACTIVITIES

• Complete the two self-test quizzes satisfactorily (you have an unlimited number of attempts and must score 90% or more).
• Complete the Project 4 activities.

Lesson 5: Interpolation--from simple to advanced

TIME FRAME: November 6^th to 12^th, 2008

ASSIGNMENTS and ACTIVITIES

• Complete the two self-test quizzes satisfactorily (you have an unlimited number of attempts and must score 90% or more).
• Complete the Project 5 activities.
• Peer-review of course proposals in an online forum.

Lesson 6: Surface analysis

TIME FRAME: November 13^th to 19^th, 2008

ASSIGNMENTS and ACTIVITIES

• Complete the self-test quiz satisfactorily (you have an unlimited number of attempts and must score 90% or more).
• Complete the Project 6 activities.
• A revised (final) project proposal for your quarter-long project is due this week (about 500-800 words).

Lesson 7: Overlay analysis

TIME FRAME: November 20^th to 26^th, 2008

ASSIGNMENTS and ACTIVITIES

• Complete the self-test quiz satisfactorily (you have an unlimited number of attempts and must score 90% or more).
• Continue the Quarter-long Project by posting evidence of your progress this week.

Lesson 8: Spatial autocorrelation

TIME FRAME: November 27^th to December 3^rd, 2008

ASSIGNMENTS and ACTIVITIES

• Complete the self-test quiz satisfactorily (you have an unlimited number of attempts and must score 90% or more).
• Complete the Project 8 activities.
• As in Week 7, you should post evidence of further progress with your quarter-long project.

Lesson 9: Project work time (no content this week)

TIME FRAME: December 4^th to 10^th, 2008

ASSIGNMENTS and ACTIVITIES

• This week you should complete your quarter-long project work and post a report with links to data, maps, charts etc. summarizing project activities and results. This should be suitable for anyone involved with the course to read and understand. Note that there are no other course activities at all this week, to give you plenty of time to work on completion of the project.

Lesson 10: Putting it all together: applied research using GIS

TIME FRAME: December 11^th to 16^th, 2008

ASSIGNMENTS and ACTIVITIES

• Submit/present your quarter-long project and get involved in the discussions around all the submitted projects!

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Course Policies

(Note: These links open in a new browser window)

• Assignments
• Grading
• Deferred Grades
• Academic Integrity
• Citation and Reference Style
• Accommodating Disabilities
• Use of Trade Names
• Netiquette
• Additional Course Policies

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Disclaimer: Please note that the specifics of this Course Syllabus can be changed at any time, and you will be responsible for abiding by any such changes. Changes will be posted to the course message board.

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