GMT611 - STATISTICS of SPATIAL DATA
| Course Name | Code | Semester | Theory (hours/week) |
Application (hours/week) |
Credit | ECTS |
|---|---|---|---|---|---|---|
| STATISTICS of SPATIAL DATA | GMT611 | Any Semester/Year | 3 | 0 | 3 | 7 |
| Prequisites | ||||||
| Course language | Turkish | |||||
| Course type | Elective | |||||
| Mode of Delivery | Face-to-Face | |||||
| Learning and teaching strategies | Lecture Question and Answer | |||||
| Instructor (s) | Will be defined by Geomatics Engineering Department | |||||
| Course objective | The aim of course is to educate engineers with capabilities of advanced engineering skills and give detailed information on fundamentals, applications and methods of geostatistics theory in Geomatics. | |||||
| Learning outcomes |
| |||||
| Course Content | Introduction to geographic data statistics. Sampling for geographic data. Linear systems. Probability and statistics theory. Probabilistic and statistic models for geographic data. Discrete and continuous statistical probability distributions used in Geomatics. Variance and covariance. Parameter estimation, spatial correlation and regression analysis associated with statistical measures. Hypothesis testing and applications in Geomatics. The theory of least squares method and accuracy analysis. Kriging, Kalman, and Bayes techniques and their applications in Geomatics. | |||||
| References | - Hengl, T. (2009) A Practical Guide to Geostatistical Mapping, 270 p., - Ripley, B.D. (2004) Spatial Statistics, 260 p., - Bardossy, A. (2008) Introduction to Geostatistics, 134 p., - Cressie, N., Wikle, C.K. (2011) Statistics for spatio-temporal data, 571 p. | |||||
Course outline weekly
| Weeks | Topics |
|---|---|
| Week 1 | Introduction to geographic data statistics |
| Week 2 | Sampling for geographic data |
| Week 3 | Linear systems |
| Week 4 | Probability and statistics theory |
| Week 5 | Probabilistic and statistic models for geographic data |
| Week 6 | Midterm exam |
| Week 7 | Discrete and continuous statistical probability distributions used in geomatics |
| Week 8 | Variance and covariance |
| Week 9 | Parameter estimation, spatial correlation and regression analysis associated with statistical measures |
| Week 10 | Hypothesis testing and applications in Geomatics |
| Week 11 | Midterm exam |
| Week 12 | The theory of least squares method and accuracy analysis |
| Week 13 | The theory of least squares method and accuracy analysis |
| Week 14 | Kriging, Kalman, and Bayes techniques and their applications in Geomatics |
| Week 15 | Preparation for final exam |
| Week 16 | Final Exam |
Assesment methods
| Course activities | Number | Percentage |
|---|---|---|
| Attendance | 16 | 5 |
| Laboratory | 0 | 0 |
| Application | 0 | 0 |
| Field activities | 0 | 0 |
| Specific practical training | 0 | 0 |
| Assignments | 5 | 15 |
| Presentation | 0 | 0 |
| Project | 0 | 0 |
| Seminar | 0 | 0 |
| Midterms | 2 | 30 |
| Final exam | 1 | 50 |
| Total | 100 | |
| Percentage of semester activities contributing grade succes | 23 | 50 |
| Percentage of final exam contributing grade succes | 1 | 50 |
| Total | 100 | |
WORKLOAD AND ECTS CALCULATION
| Activities | Number | Duration (hour) | Total Work Load |
|---|---|---|---|
| Course Duration (x14) | 16 | 3 | 48 |
| Laboratory | 0 | 0 | 0 |
| Application | 0 | 0 | 0 |
| Specific practical training | 0 | 0 | 0 |
| Field activities | 0 | 0 | 0 |
| Study Hours Out of Class (Preliminary work, reinforcement, ect) | 16 | 5 | 80 |
| Presentation / Seminar Preparation | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Homework assignment | 5 | 8 | 40 |
| Midterms (Study duration) | 2 | 12 | 24 |
| Final Exam (Study duration) | 1 | 18 | 18 |
| Total Workload | 40 | 46 | 210 |
Matrix Of The Course Learning Outcomes Versus Program Outcomes
| D.9. Key Learning Outcomes | Contrubition level* | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |
| 1. Define problems in Geomatics Engineering and use Information Technology effectively in order to solve these problems. | X | ||||
| 2. Learn basic Mathematics, Science and Engineering formations and use them productively in professional life | X | ||||
| 3. Choose, use and improve recent technology and methods that needed for Geomatics Engineering applications | X | ||||
| 4. Earn the ability of producing new spatial products with data coming from international Geomatics application by using his/her qualification of obtaining, interpretation and analyzing of spatial data and by adding personal viewpoint | X | ||||
| 5. Estimate geodetic and geodynamic parameters with geodetic observations and use kinematic and dynamic functional models effectively in studies | X | ||||
| 6. Know advanced national and international applications in areas of Photogrammetry and Laser Scanning and contribute to the development processes of these applications | X | ||||
| 7. Develop strategies for data collection from space/aerial images and aerial/terrestrial laser scanning data; define the most appropriate methods for data extraction from collected data; process, analysis, integrate data with other spatial data, develop models; attend to field works and present results and outputs visually, statistically and thematically | X | ||||
| 8. Develop case / aim specific static or dynamic online systems, design spatial database management systems and produce visual products by following recent developments in GIS environment | X | ||||
| 9. Find solutions for aim relevant data obtainment by being familiar with working principle of scanning devices and sensors and their usage areas | X | ||||
| 10. Design systems which are considering scientific facts for more economically and more reliable management of industrial and infrastructure applications | X | ||||
| 11. Consider factors of social, environmental, economic, health and job security in professional life. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest