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| JDZ7202 | Spatial Data Models and Data Str. For Gis | 3+0+0 | ECTS:7.5 | | Year / Semester | Spring Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of GEOMATICS ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Prof. Dr. Çetin CÖMERT | | Co-Lecturer | None | | 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 Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Spatial data models for GIS will be learned | 10,11 | 1 | | PO - 2 : | Spatial data structures for GIS will be learned | 10,11 | 1 | | PO - 3 : | Relational and object-relational data models will be learned | 10,11 | 1 | | PO - 4 : | A related software will be used | 12 | 4 | | 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 | | |
| 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. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | An introduction to spatial data models and data structures for GIS; fundamentals, course contents. | | | Week 2 | Relational and object based data models | | | Week 3 | Modelling Dynamical Phenomena; Eulerian and Lagrangian views. Coventional and Machine Learning (ML) based models. Discrete and continuous models; Celular Automata (CA) | | | Week 4 | Computational Fluid Dynamics (CFD) and its applications in GIS; Flood modelling, air pollution modelling. | | | Week 5 | Lab: Flood modelling using flow-2d qgis plug in | | | Week 6 | Modelling urban development; conventional and ML models | | | Week 7 | Modelling moving objects; trajectory modelling | | | Week 8 | Mid-term exam | | | Week 9 | Lab : trajectory modelling and spatial SQL in Postgis | | | Week 10 | An introduction to Artificial Neural Networks (ANN); fundamentals | | | Week 11 | Artificial Neural Networks (ANN); hyper-parameters | | | Week 12 | Lab: ANN uygulaması ; Python Keras | | | Week 13 | a comparison of conventional and ANN methods in a case study; Land slide assessment | | | Week 14 | Term Project presentations and oral exam on term projects | | | Week 15 | Term Project presentations and oral exam on term projects | | | Week 16 | Final exam | | | |
| 1 | Samet, H., 1990;The design and Analyses of Spatial Data Structures, Addison Wesley, New York. | | | 2 | Worboys, M, Duckham M., 2004,GIS, A computing Perspective, CRC Press, second edition. | | | |
| 1 | Oosterom, P.,V., 1993; Reactive Data Structures for GIS, Oxford University Press. | | | |
| Method of Assessment | | Type of assessment | Week No | Date | 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 work | Duration (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 load | | | 108 |
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