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| Course Description |
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| Course Name |
: |
Quantitative Methods in Landscape Planning |
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| Course Code |
: |
PM-568 |
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| Course Type |
: |
Optional |
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| Level of Course |
: |
Second Cycle |
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| Year of Study |
: |
1 |
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| Course Semester |
: |
Spring (16 Weeks) |
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| ECTS |
: |
6 |
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| Name of Lecturer(s) |
: |
Prof.Dr. SÜHA BERBEROĞLU
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| Learning Outcomes of the Course |
: |
Database and management
Descriptive statistic methods used in planning process
Fuzzy logic theory and its application in planning: Neural Networks as an example
Multi-criteria analysis
Geostatistics
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| Mode of Delivery |
: |
Face-to-Face |
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| Prerequisites and Co-Prerequisites |
: |
None |
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| Recommended Optional Programme Components |
: |
None |
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| Aim(s) of Course |
: |
Utilizes computer-based analytical tools for planning analysis, and descriptive and influence statistics as applied to public policies in planning and environmental policy. |
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| Course Contents |
: |
The major concept of the lecture is to introduce quantitative techniques in collecting and analysing spatial data for landscape planning. In this context the lecture covers, data sources used in landscape planning and Geographical Information Systems, data analysis, correlation and regression analysis, boolean, arithmetic statistical operations together with multiple regression, principal component analysis, fuzzy theory and artificial neural networks. |
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| Language of Instruction |
: |
Turkish |
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| Work Place |
: |
Classroom and laboratory |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
|
1 |
Introduction to database and its management |
none |
Lecture, discussion and demonstration |
|
2 |
Statistical methods in landscape planning |
Readings |
Lecture, discussion and demonstration |
|
3 |
Multi-variate statistics in land use and its usage in the analysis of environmental data |
Readings |
Lecture, discussion and demonstration |
|
4 |
Fuzzy logic theory and its application in planning |
Readings |
Lecture, discussion and demonstration |
|
5 |
Artificial Neural Networks |
Readings |
Lecture, discussion and demonstration |
|
6 |
Multi-criteria Analysis |
Readings |
Lecture, discussion and demonstration |
|
7 |
Decision support systems and multi-criteria analysis |
Readings |
Lecture, discussion and demonstration |
|
8 |
Mid-term exam |
Preparation for exam |
Evaluation |
|
9 |
Laboratory exercise |
Readings |
Lecture, discussion and demonstration, Laboratory exercise |
|
10 |
Interpolation methods |
Readings |
Lecture, discussion and demonstration, Laboratory exercise |
|
11 |
Geostatistics |
Readings |
Lecture, discussion and demonstration, Laboratory exercise |
|
12 |
Introduction to non-linear models |
Readings |
Lecture, discussion and demonstration |
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13 |
Laboratory exercise |
Readings |
Lecture, discussion and demonstration, Laboratory exercise |
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14 |
Homework, project |
Preparation for presentation of the project |
Evaluation, lecture, discussion and demonstration |
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15 |
Environmental modelling case studies |
Readings |
Lecture, discussion and demonstration |
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16/17 |
Final exam |
Preparation for exam |
Evaluation |
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 Quantitative Geography: Perspectives on Spatial Data Analysis (2000). Authors: A. Stewart Fotheringham , Chris Brunsdon, Martin Charlton.
Lecture notes and handouts
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| |
| Required Course Material(s) | |
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Assessment Methods and Assessment Criteria |
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Semester/Year Assessments |
Number |
Contribution Percentage |
|
Mid-term Exams (Written, Oral, etc.) |
1 |
50 |
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Homeworks/Projects/Others |
5 |
50 |
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Total |
100 |
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Rate of Semester/Year Assessments to Success |
40 |
| |
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Final Assessments
|
100 |
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Rate of Final Assessments to Success
|
60 |
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Total |
100 |
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
Develops and increases the level of expertise in the same or different fields, using undergraduate knowledge and competency |
4 |
|
2 |
Understands the interdisciplinary interaction related to profession. |
4 |
|
3 |
Has advanced knowledge and understanding based on the basis of competencies gained at the undergraduate level to conduct research in the field of landscape design and landscape planning |
1 |
|
4 |
Has critical awareness in the field of landscape planning and landscape design in terms of the nature and resources of knowledge as well as producing and testing knowledge |
4 |
|
5 |
Has the ability to use the theoretical and practical knowledge gained in the field. |
3 |
|
6 |
Creates new knowledge skillfully by integrating the knowledge gained in the field of landscaping with information from different disciplines; tackles with the problems which require expertise by using scientific reseach methods |
5 |
|
7 |
Has cognitive and practical skills which is required to gain competency in professional practice |
4 |
|
8 |
Applies the acquired knowledge, understanding and problem-solving skills in new but unusual environments as well as in broader, interdisciplinary and multidisciplinary contexts. |
2 |
|
9 |
Tackles with problem, develops a solution method, evaluates the results and applies when necessary. |
5 |
|
10 |
Takes responsibility to develop new strategical approaches and to create solutions in complex and unpredictable circumstances |
3 |
|
11 |
Evaluates information related to the field critically, directs learning and conducts advanced studies independently |
3 |
|
12 |
Presentswritten or orally his own studies or current developments in the field to people in or out of the field using visuals |
3 |
|
13 |
Examines social relations and the norms that direct these relations critically and develops the situation when necessary |
2 |
|
14 |
Has a good command of a foreign language, at least at B2 level of European Language Portfolio, to communicate orally or written |
3 |
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15 |
Uses advanced information and communication technologies with the required level by computer softwares. |
5 |
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16 |
Develops strategies, policies and implementation plans related to landscape planning and landscape design and evaluates the obtained results within the framework of quality processes |
4 |
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17 |
Collects data about landscape planning and landscape design and makes interpretation, teaches or announces the acquired knowledge in line with social, scientific and ethical values |
3 |
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18 |
Uses the knowledge of landscape planning and landscape design practice and problem solving skills in interdisciplinary studies |
4 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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| Student Workload - ECTS |
| Works | Number | Time (Hour) | Total Workload (Hour) |
| Course Related Works |
|
Class Time (Exam weeks are excluded) |
16 |
2 |
32 |
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Out of Class Study (Preliminary Work, Practice) |
1 |
8 |
8 |
| Assesment Related Works |
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Homeworks, Projects, Others |
5 |
16 |
80 |
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Mid-term Exams (Written, Oral, etc.) |
1 |
10 |
10 |
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Final Exam |
1 |
10 |
10 |
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Total Workload: | 140 |
| Total Workload / 25 (h): | 5.6 |
| ECTS Credit: | 6 |
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