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GRADUATE INSTITUTE of NATURAL and APPLIED SCIENCES / DEPARTMENT of GEOMATICS ENGINEERING
Doctorate
Course Catalog
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FBE
GRADUATE INSTITUTE of NATURAL and APPLIED SCIENCES / DEPARTMENT of GEOMATICS ENGINEERING / Doctorate
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JDZ7202Spatial Data Models and Data Str. For Gis3+0+0ECTS:7.5
Year / SemesterSpring Semester
Level of CourseThird Cycle
Status Elective
DepartmentDEPARTMENT of GEOMATICS ENGINEERING
Prerequisites and co-requisitesNone
Mode of Delivery
Contact Hours14 weeks - 3 hours of lectures per week
LecturerProf. Dr. Çetin CÖMERT
Co-LecturerNone
Language of instruction
Professional practise ( internship ) None
 
The aim of the course:
To establish an understanding of spatial data modelling in comparison with Machine Learning techniques.
 
Programme OutcomesCTPOTOA
Upon successful completion of the course, the students will be able to :
PO - 1 : Spatial data models for GIS will be learned10,111
PO - 2 : Spatial data structures for GIS will be learned10,111
PO - 3 : Relational and object-relational data models will be learned10,111
PO - 4 : A related software will be used124
CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), PO : Learning Outcome

 
Contents of the Course
An introduction to spatial data models; Relational and Object-Oriented data models. Modelling Dynamical Phenomena; Eulerian and Lagrangian views. Coventional and Machine Learning (ML) based models. Discrete and continuous models; Celular Automata (CA). Computational Fluid Dynamics (CFD) and its applications in GIS; Flood modelling, air pollution modelling. Modelling moving objects; trajectory modelling, Artificial Neural Networks (ANN), omparison of conventional and ANN methods, term project.
 
Course Syllabus
 WeekSubjectRelated Notes / Files
 Week 1An introduction to spatial data models and data structures for GIS; fundamentals, course contents.
 Week 2Relational and object based data models
 Week 3Modelling Dynamical Phenomena; Eulerian and Lagrangian views. Coventional and Machine Learning (ML) based models. Discrete and continuous models; Celular Automata (CA)
 Week 4Computational Fluid Dynamics (CFD) and its applications in GIS; Flood modelling, air pollution modelling.
 Week 5Lab: Flood modelling using flow-2d qgis plug in
 Week 6Modelling urban development; conventional and ML models
 Week 7Modelling moving objects; trajectory modelling
 Week 8Mid-term exam
 Week 9Lab : trajectory modelling and spatial SQL in Postgis
 Week 10An introduction to Artificial Neural Networks (ANN); fundamentals
 Week 11Artificial Neural Networks (ANN); hyper-parameters
 Week 12Lab: ANN uygulaması ; Python Keras
 Week 13a comparison of conventional and ANN methods in a case study; Land slide assessment
 Week 14Term Project presentations and oral exam on term projects
 Week 15Term Project presentations and oral exam on term projects
 Week 16Final exam
 
Textbook / Material
1Samet, H., 1990;The design and Analyses of Spatial Data Structures, Addison Wesley, New York.
2Worboys, M, Duckham M., 2004,GIS, A computing Perspective, CRC Press, second edition.
 
Recommended Reading
1Oosterom, P.,V., 1993; Reactive Data Structures for GIS, Oxford University Press.
 
Method of Assessment
Type of assessmentWeek NoDate

Duration (hours)Weight (%)
Mid-term exam 8 1,5 30
Laboratory exam 15 2 20
End-of-term exam 16 1,5 50
 
Student Work Load and its Distribution
Type of workDuration (hours pw)

No of weeks / Number of activity

Hours in total per term
Yüz yüze eğitim 3 14 42
Sınıf dışı çalışma 2 14 28
Arasınav için hazırlık 6 1 6
Arasınav 1 1 1
Proje 3 6 18
Kısa sınav 2 3 6
Dönem sonu sınavı için hazırlık 6 1 6
Dönem sonu sınavı 1 1 1
Total work load108