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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS VYA 5020 SPATIAL DATA ANALYSIS ELECTIVE 3 0 0 4

Offered By

DATA MANAGEMENT AND ANALYSIS

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ÖZLEM KIREN GÜRLER

Offered to

DATA MANAGEMENT AND ANALYSIS

Course Objective

The main aim of this course to be able to apply traditional statistical methods in spatial dimensions such as lenght, area, proximity and orientation

Learning Outcomes of the Course Unit

1   To be able to understand differences between of visual analysis of spatial data and statistical analysis 2   The ability to sort data types 3   Statistical methods to analyze data 4   To be able to compare different methods of analysis, 5   To be able to find for solving the problem in any spatial data collection and analysis methods

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Introduction to Spatial Econometrics: Definition of spatial econometrics, the differences that distinguish it from classical econometrics 2 Spatial econometrics terms 3 Spatial Impact, Spatial Dependence and Heterogeneity concepts 4 Spatial Sampling 5 Spatial Interactions and Spatial Autocorrelation 6 Spatial Autoregressive Models 7 Spatial Regression Models 8 Midterm Exam 9 Prediction Methods for Spatial Models: Estimating and Interpreting SAR, SDM, SEM and SAC Models 10 Estimation Methods for Spatial Models: Estimating and Interpreting SAR, SDM, SEM and SAC Models -continue 11 Spatial Specification Tests 12 Tests for Spatial Error Correlation 13 Application of Spatial Models-1 14 Application of Spatial Models-2

Recomended or Required Reading

1- Spatial Econometrics, Statistical Foundation and Application to Regional Converge, Advances in Spatial Science, 2009. Giuseppe Arbia 2. Spatial Econometrics: Methods and Models, 1988, Luc Anselin 3. Intorduction to Spatial Econometrics, 2009, LeSage J., Pace R. K.,Stata press. Third Edition. 4. Long, J. S. (1997). Regression Models for Categorical and Limited Dependent Variables. USA: SAGE Publications.

Planned Learning Activities and Teaching Methods

Discussing, Problem solving, Lecture Method, Question-Answer Method

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 STT TERM WORK (SEMESTER) 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE * 0.30 + STT * 0.30 + FIN* 0.40 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + STT * 0.30 + RST* 0.40

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

To be announced.

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 14 3 42 Preparations before/after weekly lectures 10 2 20 Preparation for midterm exam 1 10 10 Preparation for final exam 1 10 10 Preparing assignments 5 3 15 Midterm 1 3 3 Final 1 3 3 TOTAL WORKLOAD (hours) 103

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 LO.1 1 1 LO.2 1 1 LO.3 1 1 LO.4 LO.5 1 1 1 1 1 1