Geog 492/520: Geographic Information Analysis

 Spring 2007

 

Instructor: Dr. Jun Yan

Room: IEB Lab 301

Office: EST 333

Time: Thursday, 2:20 - 5:15 pm

E-mail: jun.yan@wku.edu

Office Hour: TR TR 9:00 am – 10:00 am or by appointment

Office Tel: (270)745-8952

Prerequisite: GEOG 391 and 417, or special permission

 

Course website: http://www.wku.edu/~jun.yan/gia/

Required Texts:

O’Sullivan, David and Unwin, David J., 2002, Geographic Information Analysis. John Wiley & Sons, Inc, ISBN: 0-471-21176-1

 

Supplemental Texts:

Bailey, Trevor and Anthony Gatrell. 1996. Interactive Spatial Data Analysis.  Prentice Hall.

Haining, Robert. 2003. Spatial Data Analysis: Theory and practice. Cambridge University Press.

Rogerson, Peter. 2001. Statistical Methods for Geography. SAGE Publications.

Goodchild, Michael. 2004. Spatially Integrated Social Science. Oxford University Press.

Fotheringham, Stewart, Chris Brunsdon, and Martin Charlton. Geographically Weighted Regression: The analysis of spatially varying relationships. John Wiley & Sons, Inc.

Fotheringham, Stewart and Michael Wegener. 2000. Spatial Models and GIS: New potential and new models. Taylor & Francis.

Miller, Harvey J. and Jiawei Han. 2001. Geographic Data Mining and Knowledge Discovery. Taylor & Francis.

Openshaw, Stan and Robert. 2000. Abrahart. GeoComputation. Taylor & Francis.

 

Other Required Materials:

2 CD-RWs.

 

Course Description:

This course introduces a variety of spatial quantitative methods commonly used in the subjects of Geocience. The goal of this course is to provide an overview of and an introduction to a range of statistical techniques used in the analysis of spatial (geographic) data. The emphasis is on gaining insight into the overall framework for analysis and developing an understanding of the various concepts, rather than an in-depth technical treatment of specific statistical techniques. The methods are mainly discussed within the context of GIS technology. Students are required to complete a number of lab exercises and projects.

 

Course syllabus

 

Course Objectives:

1.     Understand the basic concepts and principles of geographic information analysis;

2.     Know how to implement a variety of spatial quantitative methods within GIS context (e.g. ArcGIS);

 

Class Format and Policies:

Class meetings will contain both a lecture and lab component.  Lecture will focus on the conceptual basis of spatial quantitative methods.  Students should take notes during the lecture component of the class.  The lab component provides students with opportunities to get familiar with how to fulfill specific methods in GIS or other statistical packages.  Note that many project assignments will require time outside of class to complete. Auditing of this course is not allowed.

 

Attendance:

Class attendance is required.  Roll will be taken at the start of every class period. If a student enters class late it is his or her responsibility to see me at the end of the class period and make sure I have marked them as present.  Student who has absence record will have a 2-point deduction for each day that he/she misses. The student is responsible for all lecture notes, materials, etc.

 

 Grading:

The evaluation of your performance in this course will be derived from (i) two exams covering topics related to geographic information analysis, (ii) the completion of lab exercise and project assignments, (iii) a term paper in the area of chosen by a student.

 

You will earn points toward your final grade according to the following schedule:

 

Item

Points

Midterm

20

Lab projects (4)

40

Lab exercises

20

Final project (report)

20

 

Grading will follow the below scale:

 

Average Score

Grade

90 – 100%

A

80 – 89.9%

B

70 – 79.9%

C

60 – 69.9%

D

< 60%

F

 

Make-up Exams:

Make-up exams will be given only for the following special circumstances: (1) a university-sponsored event, and (2) illness with a doctor’s written excuse. Justifying documentation has to be presented to (and accepted by) the instructor BEFORE the date of the exam/quiz.

 

GIS Lab Policies:

The GIS lab is available for use any time during the day when a class is not scheduled (schedule posted on lab door). Evening hours will be made available and announced as soon as the lab monitor schedule is finalized. The lab is only to be used, however, only for work related to GIS and remote sensing classes. Work such as term papers for other classes should be done in one of the universities general computer labs (for locations, see http://stech.wku.edu/lablocations.html). Do not print papers for other classes or material downloaded from the Internet on the printer in the GIS lab. Food, drink and tobacco products are strictly prohibited from the lab to protect the university's investment in computer equipment and keep the facility looking nice. The lab is monitored with cameras to enhance security.

 

Course Withdrawal:

Students who find it necessary to withdraw completely from the university (WKU) or from this course should report to the Office of Registrar in Potter Hall to initiate Withdrawal procedures before the last Withdrawal date. Students who cease attending class without and official Withdrawal will receive a Failing grade.

 

Students with Disabilities:

Students with disabilities who require accommodations (academic adjustments and/or auxiliary aids or services) for this course must contact the Office for Student Disability Services, Room 445 in Potter Hall. The Office for Student Disability Services telephone number is (270) 745-5004 V/TDD. Please do not request accommodations without a letter of accommodation from the Office for Student Disability Services.

 

Other Policies:

The Department of Geography and Geology strictly adheres to university policies, procedures, and deadlines regarding student schedule changes. It is the sole responsibility of the student to meet all deadlines in regard to adding, dropping, or changing the status of a course. Only in exceptional cases will a deadline be waived. The Student Schedule Exception Form is used to initiate all waivers. This form requires a written description of the extenuating circumstances involved and the attachment of appropriate documentation. Poor academic performance, general malaise, or undocumented general stress factors are not considered as legitimate circumstance.

Tentative Course Outline: subject to revision as conditions warrant.

 

Week

Topics

Lecture notes

Lab exercises

Projects

1
  • Introduction
  • Pitfalls and potential of spatial data

Lecture 1

 

Ex1 (data);

Ex2;

Ex3;

Ex4;

 

2
  • Pitfalls and potential of spatial data
  • Fundamentals: maps as outcomes of processes

Lecture 2

Lecture 3

 

3
  • Point pattern analysis

Lecture 4

Ex5 (data);

Ex6;

 

4
  • Point pattern analysis
  • Project #1

Lecture 4

Lecture 5

Ex 7, Ex 8, Ex9 (data),

Ex10, Ex11 (data)

Project #1 (data)

5
  • Practical point pattern analysis
  • Area objects and spatial autocorrelation

Lecture 6

Lecture 7

Ex12, Ex13 (data)

 

6
  • Area objects and spatial autocorrelation
  • Project #2
  • Exam review

Lecture 7

z-score table

Ex14 (data), Ex 15

Project #2 (data)

7

Midterm exam

 

 

 

8
  • Spring Breaks; No Class

 

 

 

9
  • Describing and analyzing fields
  • The statistics of fields

Lecture 8

Lecture 9

Ex16 (data), Ex17, Ex18, Ex19

Project #3 (data)

10
  • The statistics of fields
  • Project #3

Lecture 9

Ex19, Ex20

Guidelines for final project

11
  • Multivariate methods
  • Project #4

Lecture 10

Ex21 (data), Ex22, Ex23

Project #4 (data)

12
  • Multivariate methods

 

 

 

13
  • Network analysis and Spatial interaction models

 

 

 

14
  • Professor attending AAG; No Class

 

 

 

15
  • New approaches to spatial analysis

 

 

 

16

Final project due

 

 

 

 

Web Resources:

 

o   Center for Spatially Integrated Social Science (CSISS) main site, especially its learning materials, syllabi and search engines:

http://www.csiss.org/

 

o   CSISS spatial tools clearinghouse site, with a specialized tools search engine, links to portals and selected links to specific software:

http://www.csiss.org/clearinghouse/index.php3

 

o   Geostatistical Software Library at Stanford University

http://www.gslib.com/

 

o   SpaceStat home site, with tutorials, downloadable data sets and other utilities:

http://www.terraseer.com/products/spacestat.html

 

o   TerraSeer home site, with tutorials on cluster analysis and boundary analysis:

http://www.terraseer.com/

 

o   GeoDa home site, with free software, tutorials, downloadable data sets and other utilities:

https://www.geoda.uiuc.edu/

 

o   The CrimeStat Spatial Statistics Program home site, with free software, sample data, tutorials, etc:

http://www.icpsr.umich.edu/NACJD/crimestat.html/

 

o   R Spatial Projects home site, an international open-source project (R) that provides an environment for statistics, including spatial data analysis:

http://sal.uiuc.edu/csiss/Rgeo/

 

o   SPATSTAT, a R library for spatial statistics

http://www.maths.uwa.edu.au/~adrian/spatstat/

 

o   STARS - Space Time Analysis of Regional Systems; an open-source project to develop space-time data analysis in Python

https://sourceforge.net/projects/stars-py/

 

o   ESRI home page, with links to resources for digital maps, data sets, utilities, courses, scripts, etc.:

http://www.esri.com/

 

o   ESRI ArcScripts Online:

http://arcscripts.esri.com/

 

o   Michael Goodchild (UC Santa Barbara) on Spatial Analysis and GIS: 2001 ESRI User’s conference pre-conference seminar course outline and materials:

http://www.csiss.org/learning_resources/content/good_sa/