COURSE UNIT TITLE

: SPATIAL DATA MANAGEMENT AND ANALYSIS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
YBS 5020 SPATIAL DATA MANAGEMENT AND ANALYSIS ELECTIVE 3 0 0 6

Offered By

Management Information Systems

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ÇIĞDEM TARHAN

Offered to

Management Information Systems

Course Objective

The course is aimed to be gained competence in spatial-based data collection, management and analysis.

Learning Outcomes of the Course Unit

1   To use spatial data collection methods.
2   To perform spatial data management.
3   To use spatial data analysis methods.
4   To use spatial data query methods.
5   To evaluate projects in terms of methodology, tools and tecnological.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction - What is data managemet and analysis Yeung ve Hall, Section 2.2
2 Data Storage in Spatial Database Management Systems (Spatial Data Servers - Data Stored Statements - Spatial Data Storage Methods) Yeung ve Hall, Section 2.3 - 2.4
3 Spatial Data Server Software (Arcs - Arc Spatial Database Engine) and spatial data query language operators (basic operator: IsEmpty, Envelope, topological operator: Disjoint, Contains, and spatial analysis operations: Distance, Intersection and SymmDiff) Rigaux, School ve Voisard, Section 2
4 Spatial Data Representation (object-oriented modeling - space-based modeling), and Spatial Data Geometry (Spaghetti model, network model and topological model) Rigaux, School ve Voisard, Section 2
5 Logical Models and Query Languages Rigaux, School ve Voisard, Section 3
6 Constraint data model (linear constraint model, the object-oriented modeling) Rigaux, School ve Voisard, Section 4
7 Digital Geometry (spatial data management algorithm) Rigaux, School ve Voisard, Section 5
8 Query and Analysis I (spatial join, artifical intelligence) Rigaux, School ve Voisard, Section 7
9 Query and Analysis II (analysis of the spatial distribution of points, interpolation and spatial statistics) Rigaux, School ve Voisard, Section 7
10 MidTerm Bivand, Pebesma ve Rubio, Section 2
11 Spatial Data Indexes (R-tree, B-tree) Bivand, Pebesma ve Rubio, Section 2
12 Spatial Data Indexes(Grid) Bivand, Pebesma ve Rubio, Section 10
13 Econometric approaches to spatial data management
14 Project presentation

Recomended or Required Reading

Spatial Databases with Application to GIS (2002) Philippe Rigaux, Michel Scholl ve Anges Voisard, Elsevier, ISBN: 978 1 55860 588 6
Spatial Database Systems Design, Implematation and Project Management (2007) Albert K.W. Yeung ve G. Brent Hall (Printed in: Springer), ISBN: 10 1 4020 5393 2
Applied Spatial Data Analysis with R (2008) Roger S. Bivand, Edzer J. Pebesma ve Virgilio Gomez-Rubio, ISBN: 978 0 387 78170 9

Planned Learning Activities and Teaching Methods

There will be a midterm and a final exam for this course. Additionally, the students have a responsibility for preparing and presenting a project.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 STT TERM WORK (SEMESTER)
3 FCGR FINAL COURSE GRADE
4 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.20 + STT * 0.20 + FN* 0.60
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.20 + STT * 0.20 + RST* 0.60


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

There will be a midterm and a final exam for this course. Additionally, the students have a responsibility for preparing and presenting a project.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Dokuz Eylül Üniversitesi - I.I.B.F. Yönetim Bilişim Sistemleri Bölümü - Dokuzçeşmeler Kampüsü - Buca - IZMIR 35160
Tel: 0 232 301 07 62
e-mail: cigdem.tarhan@deu.edu.tr

Office Hours

Wednesday 09.00-12.00 am

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparations before/after weekly lectures 13 5 65
Preparing assignments 1 10 10
Preparing presentations 1 10 10
Preparation for final exam 1 10 10
Preparation for midterm exam 1 10 10
Final 1 3 3
Midterm 1 3 3
TOTAL WORKLOAD (hours) 150

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11
LO.145555552555
LO.255555552555
LO.355555552555
LO.455555552555
LO.555555552555