COURSE UNIT TITLE

: SPATIAL STATISTICS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
ERA 3004 SPATIAL STATISTICS ELECTIVE 2 0 0 3

Offered By

City and Regional Planning

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR KEMAL MERT ÇUBUKÇU

Offered to

Architecture
City and Regional Planning

Course Objective

The main objective of this course is to introduce the basic concepts of spatial statistics used in planning and spatial analysis. All the subjects covered in the course will be xplained through solved numerical examples and their relation to mathematics will be explained in detail.

Learning Outcomes of the Course Unit

1   Recognize basic spatial statistics concepts,
2   Comprehend basic techniques of spatial statistics,
3   Differentiate the basic concepts of spatial statistics used in planning and spatial analysis,
4   Solve numerical examples pertaining to the subjects covered in the class,
5   Apply the basic techniques of spatial statisticsto solve urban problems.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Descriptive Statistics: Measures of Central Tendency
2 Descriptive Statistics: Measures of Distribution and Dispersion
3 Descriptive Statistics: Relation
4 Point Data Analysis: What is a Point Measures of Central Tendency
5 Point Data Analysis: Measures of Distribution and Dispersion
6 Point Data Analysis: Patterns Analysis: Quadrat Analysis
7 Mid-term Exam
8 Point Data Analysis: Patterns Analysis: Nearest Neighbor Analysis
9 Point Data Analysis: Patterns Analysis: Spatial Autocorrelation
10 Point Data Analysis: Patterns Analysis: Spatial Autocorrelation
11 Data Manupulation
12 Data Application and Analysis
13 Interpretation of Outcomes
14 Final Examinations Week

Recomended or Required Reading

Lee, L., Wong, W.S. (2001) Statistical Analysis with ArcView GIS. John Wiley & Sons Inc.
Pindyck, R.S., Rubinfield, D.L. (1991) Econometric Models and Economic Forecasts. McGraw-Hill, Inc.
Stillwell, J., Clarke, G. (Eds.) (2004), Applied GIS and Spatial Analysis, John Wiley & Sons Inc.
Studenmund, A.H., Cassidy, H.J. (1987) Using Econometrics: A Practical Guide. Harper Collins Publishers.

Planned Learning Activities and Teaching Methods

Lectures, theoretical presentations and solved examples.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 FINS FINAL EXAM
3 FCG FINAL COURSE GRADE MTE * 0.50 + FINS * 0.50
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.50 + RST * 0.50


Further Notes About Assessment Methods

None

Assessment Criteria

Mid-term and final exams.

Language of Instruction

English

Course Policies and Rules

1. Attendance is required.
2. Plagiarism and all other means of cheating are strictly prohibited.

Contact Details for the Lecturer(s)

Dokuz Eylul University, Tinaztepe Campus
School of Architecture
Department of City and Regional Planning
Room #109
Buca/IZMIR 35160
TURKEY
mert.cubukcu@deu.edu.tr

Office Hours

Wdnesdays, 09.30-12.00

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 2 26
Preparations before/after weekly lectures 13 1 13
Preparation for midterm exam 1 15 15
Preparation for final exam 1 15 15
Midterm 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 73

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14PO.15PO.16PO.17
LO.11
LO.21
LO.311
LO.41
LO.511