GEOG 204 – Spatial Analysis

Section 001, 4 Credits

Fall 2008

 

Instructor: Dr. Arthur J. Lembo, Jr.

Office: Henson Hall 157H

Office Hours: M,W,F 9:00-11:00am; (also by appointment)

Phone: 410-677-0140

E-mail: ajlembo@salisbury.edu

Class Meetings: MWF 11 – 11:50 a.m., F 12 – 1:40 p.m.; Henson Hall 153

 

Text: McGrew, Jr., J. Chapman, and Charles B. Monroe.  An Introduction to Statistical Problem Solving in Geography, 2nd Edition. McGraw Hill, 2000. ISBN 0-697-22971-8

 

Course Description: This course will introduce the basics of statistics and their applications in geographic research.  You will be exposed to both descriptive and inferential statistics, with an emphasis on geographic applications.  This course focuses on statistical analysis and spatial statistics, since these methods are crucial to anyone having to deal with spatially-oriented problems.  Applications from both human and physical geography will be used for in-class examples and out-of-class exercises.  In class, problems will be presented and output interpreted using Minitab statistical software, but you may use any software system you wish.  After finishing this class, students are expected to be able to know how to collect data, choose the appropriate statistical techniques and analyze the data according to their research questions.

 

Exams: This course has a total of three exams during the semester as well as a final exam.  Each exam is worth 60 points and the final exam is worth 120 points (a total of 300 points for all exams).  Each exam will be administered as scheduled.  All exams count for a portion of the final grade; none can be dropped.  Very few if any numerical calculations are required in the exams.  Questions are a mix of objective (multiple choice, matching, fill in, simple graphics) and essays.  Questions involve: (1) basic knowledge of the characteristics and factual information associated with a statistical technique or concept; (2) understanding and interpretation of the purposes and objectives of a technique; (3) explanation of why a technique is important, and the limitations of the technique; (4) creative identification of geographic problems that can be solved by a technique; and (5) the ability to decide which statistical technique is most appropriate, when presented with a geographic data set of a research problem. 

 

Make-up Exams: Any student missing an exam must supply the instructor with a written excuse.  It is the student’s responsibility to inform the instructor of the missed exam within one class day after the original exam is given in order to schedule a make-up exam.  Anyone failing to comply with this policy will receive a zero for the missed exam.

 

Exercises: This course has a total of 10 exercises worth 20 points each (a total of 200 points for all exercises).  The exercises are designed to reinforce the lecture material.  Most exercises are due one week after being introduced.  If an exercise is turned in late, the penalty is 5 points per school day late. Additional decisions to alter exercise assignments or points may have to be made during the semester as conditions warrant, and the instructor reserves the right to make these decisions.

 

Grades: This course has a total of 500 points.  Each student’s grade for this course will be determined by a percentage based on the total points accumulated by that individual, divided by the total number of points possible (500).  Letter grades will be assigned as follows:

 

Letter Grade

Percentage of Points

Total Points

A

90.00 – 100%

450 – 500

B

80.00 – 89.99%

400 – 449

C

70.00 – 79.99%

350 – 399

D

60.00 – 69.99%

300 – 349

F

0.00 – 59.99%

Below 300

 

Attendance: Attending class is important.  Coming to class, paying attention and taking notes is the best way to learn the course material.  Most lectures will come from the textbook, but some material will only be presented in class. 

 

Classroom Environment: Students are expected to contribute to an environment appropriate for learnin  g that considers and respects the needs and rights of others.  Any academic misconduct will be confronted and handled accordingly.  Please silence all electronic devices while in class.  Do not arrive late and do not leave early.

 

Academic Integrity: Cheating, plagiarism and other forms of academic dishonesty will not be tolerated in this course.  Students should pay special attention to the expectations discussed in the 2005-2006 Student Handbook and 2005-2007 University Catalog.  Violating these rules will result in significant grade penalties up to and including a failing grade for the course.  Extreme cases of academic misconduct can result in expulsion from the University.

 

Writing Across the Curriculum: All writing assignments, both formal and informal, are in support of Salisbury University’s Writing Across the Curriculum Program.

 

Important University Dates for Fall

Last day to drop/add –  September 8

Last day to withdraw from course to receive a “W” – November 7

 

 

Changes to Syllabus: This syllabus may be modified or changed by the instructor as necessary. Students will be notified of the changes in class.

 

 

Approximate Schedule – Spatial Analysis – Spring 2008

Week

Date

Subject

Chapter

1

Wed

Sept. 03

An Introduction to Spatial Analysis

 

 

Fri

 Sept. 05

The Role of Statistics in Geography Examples of Statistical Problem Solving in Geography

1.1

 

Mon

Sept. 08

Examples of Statistical Problem Solving in Geography (cont.) Basic Terms and Concepts in Statistics

1.2; 1.3

2

Wed

Sept. 10

Selected Dimensions of Geographic Data Levels of Measurement

2.1; 2.2

 

Fri

 Sept. 12

Measurement Concepts Basic Classification Methods Graphic Procedures & (Exercise #1)

2.3

 

Mon

Sept. 15

Measurement Concepts Basic Classification Methods Graphic Procedures Continued

2.3; 2.4; 2.5

3

Wed

Sept. 17

Measures of Central Tendency Measures of Dispersion and Variability

3.1; 3.2

 

Fri

 Sept. 19

Measures of Shape or Relative Position Spatial Data and Descriptive Statistics

(Exercise 2)

3.3; 3.4

4

Mon

Sept. 22

Descriptive Spatial Statistics

4

 

Wed

Sept. 24

Descriptive Spatial Statistics (cont.) – Exam Review

4

 

Fri

 Sept. 26

Exam #1

 

5

Mon

Sept. 29

Deterministic and Probabilistic Processes in Geography Basic Probability Terms and Concepts

5.1

5.2

 

Wed

Oct. 01

The Binomial Distribution

5.3

 

Fri

 Oct. 03

The Poisson Distribution The Normal Distribution &

(Exercise #3)

5.4

6

Mon

Oct. 06

The Normal Distribution (cont.) Probability Mapping

5.5

5.6

 

Wed

Oct. 08

Basic Concepts in Sampling

6.1

 

Fri

 Oct. 10

Types of Probability Sampling &

(Exercise #4)

6.2

7

Mon

Oct. 13

Spatial Sampling

6.3

 

Wed

Oct. 15

Exam Review

 

 

Fri

 Oct. 17

Exam #2

 

8

Mon

Oct. 20

Basic Concepts in Estimation

7.1

 

Wed

Oct. 22

Confidence Intervals and Estimation

7.2

 

Fri

 Oct. 24

Confidence Intervals and Estimation (cont.) Sample Size Selection &

(Exercise #5)

7.2

7.3

9

Mon

Oct. 27

Classical/Traditional Hypothesis Testing P-Value, or Prob-Value, Hypothesis Testing

8.1

 8.2

 

Wed

Oct. 29

One-Sample Difference of Means Test: Small Sample One-Sample Difference of Proportions Test Issues in Inferential Testing and Test Selection

8.3

 8.4

 8.5

 

Fri

 Oct. 31

Two-Sample Difference of Means Test Two-Sample Difference of Proportions Test &

(Exercise #6)

9.1

9.2

10

Mon

Nov. 03

Analysis of Variance (ANOVA)

10.1

 

Wed

Nov. 05

Krustal-Wallis Test

10.2

 

Fri

 Nov. 07

The Nature of Correlation Association of Interval/Ratio Variables

(Exercise 7)

13.1

11

Mon

Nov. 10

Mop up.

 

 

Wed

Nov. 12

Exam Review

 

12

Fri

 Nov. 14

Exam #3

 

 

Mon

Nov. 17

Association of Interval/Ratio Variables (cont.) Association of Ordinal Variables

13.2

 

Wed

Nov. 19

Form of Relationship in Bivariate Regression

14.1

13

Fri

 Nov. 21

Strength of Relationship in Bivariate Regression &

(Exercise #8)

14.2

 

Mon

Nov. 24

Residual or Error Analysis in Bivariate Regression Inferential Use of Regression Basic

14.3

14.4

14.5

 

Wed

Nov. 26

Concepts of Multivariate Regression Point Pattern Analysis

14.5

14

Fri

 Nov. 28

No CLASS

 

 

Mon

Dec. 01

Point Pattern Analysis

12.1

 

Wed

Dec. 03

Area Pattern Analysis &

 

12.2

15

Fri

 Dec. 05

Goodness-of-Fit Tests

(Exercise #9)

11.2

 

Mon

Dec. 08

Contingency Analysis

 

 

Wed

Dec. 10

Selecting the Proper Statistical Technique – Exam Review

 

 

Fri

 Dec. 12

Selecting the Proper Statistical Technique – Exam Review

 

FINAL EXAM:  Friday December 19.  1:30 – 4:00. (don’t shoot the messenger)