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| SEHL7333 | Spatial Statistics | 3+0+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Third Cycle | | Status | Elective | | Department | DEPARTMENT of URBAN and REGIONAL PLANNING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Prof. Dr. Aygün ERDOĞAN | | Co-Lecturer | | | Language of instruction | | | Professional practise ( internship ) | None | | | | The aim of the course: | | This course will equip students with advanced concepts of quantitative analysis of geographical data and with the ability of describing and identifying the geographical pattern of any spatial data represented by point, line and area in different scales for the purpose of researching the possible spatial relationships and causalities that result in those patterns. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | have the knowledge of basics of spatial statistics | 1,6,8 | 1,3, | | PO - 2 : | differentiate between different types and distributions of spatial data | 1,6,8 | 1,3,5,6, | | PO - 3 : | to conduct descriptive and inferential spatial statistical methods by ESDA | 1,6,8 | 1,3,5,6, | | PO - 4 : | identify the spatial relationships and causalities | 1,6,8 | 1,3,5,6, | | 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 | | |
| Geographical data and concepts in their quantitative analysis; methods for spatial sampling and estimation; discrete vs. continuous spatial data and their probability distributions; global and local scale properties of spatial patterns/distributions; descriptive and inferential statistics for spatial patterns of points, lines, and discontinuous and continuous areal data using exploratory spatial data analysis (ESDA) approach; spatial autocorrelation and correlation; spatial regression; the application of those techniques to geographical data and examples |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction; geographical data and descriptive vs. inferential spatial statistics | | | Week 2 | Discrete vs. continuous spatial data and their probability distributions | | | Week 3 | Methods for spatial sampling and estimation | | | Week 4 | Global and local scale properties of spatial patterns/distributions | | | Week 5 | Descriptive stat. for spatial patterns of points, lines, discont./cont. areal data | | | Week 6 | Inferential stat. for point patterns to assess their global & local scale properties | | | Week 7 | Cont. | | | Week 8 | Cont. | | | Week 9 | MID-TERM EXAM (Term paper interim submission) | | | Week 10 | Inferential statistical analysis of line data | | | Week 11 | Inferential statistical analysis of areal data | | | Week 12 | Spatial autocorrelation (global and local techniques) and correlation | | | Week 13 | Spatial regression (global and local techniques) | | | Week 14 | Student presentations | | | Week 15 | Cont. | | | Week 16 | FINAL EXAM | | | |
| 1 | Bailey, T.C. and Gatrell, A.C. (1996) Interactive Spatial Data Analysis, England: Longman Group Limited | | | 2 | Ebdon, D. (1981) Statistics in Geography: A Practical Approach, Oxford: Basil Blackwell | | | 3 | Walford, N. (1995) Geographical Data Analysis, John Wiley and Sons | | | 4 | Ripley, Brian D. (2004) Spatial Statistics, Hoboken, NJ: Wiley-Interscience (QA 278.2 .R56 2004 k.1) | | | |
| 1 | Çubukçu, K. M. (2015) Planlamada ve Coğrafyada Temel İstatistik ve Mekansal İstatistik, Ankara: Nobel Yayıncılık | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Presentation | 14 | | 3 | 15 | | Homework/Assignment/Term-paper | 3
5
7
9
11
13 | 14/10/2021
28/10/2021
11/11/2021
25/11/2021
09/12/2021
23/12/2021 | 1
1
1
1
1 | 35 | | End-of-term exam | 16 | 13/01/2021 | 3 | 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 | 15 | 45 | | Sınıf dışı çalışma | 3 | 15 | 45 | | Ödev | 15 | 4 | 60 | | Proje | 8 | 2 | 16 | | Dönem sonu sınavı için hazırlık | 15 | 3 | 45 | | Dönem sonu sınavı | 3 | 1 | 3 | | Total work load | | | 214 |
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