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| JDZL7330 | Spatial Statistics in 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 | Face to face, Practical | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Prof. Dr. Hüsniye Ebru ÇOLAK | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | Understanding the assessment by statistical methods of spatial data in GIS |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Learn to spatial statistics methods in GIS | 5 | 1,3 | | PO - 2 : | Create to digital elevation model from spatial data variables | 5 | 1,3 | | 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 | | |
| What is statistic and basic statistic concepts. Spatial statistics. Types of spatial data for spatial statistics. Statistical methods for spatial data analysis. GIS and spatial analysis. Geostatistics analysis and applications in GIS. Exploratory spatial data analysis; analyze spatial data distribution, compare data distributions, examine distribution statistics, analyze data trends. Surface analysis techniques; Kriging, Inverse Distance Weighted. Determination and comparison for optimal prediction models. Clustering and density analysis. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | What is statistic and basic statistic concepts. Spatial statistics. | | | Week 2 | Types of spatial data for spatial statistics. | | | Week 3 | Statistical methods for spatial data analysis. | | | Week 4 | GIS and spatial analysis. | | | Week 5 | Geostatistics analysis and applications in GIS.. | | | Week 6 | Exploratory spatial data analysis; analyze spatial data distribution, compare data distributions, examine distribution statistics, analyze data trends. | | | Week 7 | Exploratory spatial data analysis; analyze spatial data distribution, compare data distributions, examine distribution statistics, analyze data trends. | | | Week 8 | Surface analysis techniques; Kriging, Inverse Distance Weighted. | | | Week 9 | Exam | | | Week 10 | Determination and comparison for optimal prediction models. | | | Week 11 | Clustering and density analysis. | | | Week 12 | Project | | | Week 13 | Project | | | Week 14 | Project | | | Week 15 | Project | | | Week 16 | Exam | | | |
| 1 | Öğretim üyesi ders notları (Ders sunumlarına ait pdf. dosyaları) | | | |
| 1 | K. Krivoruchkoa and C.A. Gotwayb, Using Spatial Statistics In GIS, article (.pdf) | | | 2 | Spatial Statistics (2008) book chapter (.pdf) | | | 3 | Lauren M. Scott and Mark V. Janikas, Spatial Statistics in ArcGIS, (.pdf) | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 1 | | | Practice | 9
10
11
12 | | 4 | 25 | | End-of-term exam | 15 | | 1 | 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 | 4 | 14 | 56 | | Laboratuar çalışması | 0 | 0 | 0 | | Arasınav için hazırlık | 10 | 2 | 20 | | Arasınav | 3 | 4 | 12 | | Uygulama | 3 | 5 | 15 | | Klinik Uygulama | 0 | 0 | 0 | | Ödev | 5 | 5 | 25 | | Proje | 0 | 0 | 0 | | Kısa sınav | 0 | 0 | 0 | | Dönem sonu sınavı için hazırlık | 10 | 3 | 30 | | Dönem sonu sınavı | 2 | 1 | 2 | | Diğer 1 | 0 | 0 | 0 | | Diğer 2 | 0 | 0 | 0 | | Total work load | | | 202 |
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