ECTS @ IUE ECTS @ IUE ECTS @ IUE ECTS @ IUE ECTS @ IUE ECTS @ IUE ECTS @ IUE

Syllabus ( GEO 308 )


   Basic information
Course title: Geographic Analysis Techniques
Course code: GEO 308
Lecturer: Prof. Dr. Arif Çağdaş AYDINOĞLU
ECTS credits: 4
GTU credits: 3 ()
Year, Semester: 3, Spring
Level of course: First Cycle (Undergraduate)
Type of course: Compulsory
Language of instruction: Turkish
Mode of delivery: Face to face , Lab work
Pre- and co-requisites: none
Professional practice: No
Purpose of the course: In geographic information science, analysis techniques can be done in two ways, as vector-based and raster-based analysis. GIS provides a very effective tool for generating maps and statistical reports from a database. The aim is to determine geographic processing analysis tools to solve spatial problems in various GIS application areas from disaster risk management and surface analysis to appropriate site selection and source optimization.
   Learning outcomes Up

Upon successful completion of this course, students will be able to:

  1. Make geographic data available in GIS, understand geographic data distribution, analyze data with basic query and vector analysis functions, and interpret the results.

    Contribution to Program Outcomes

    1. Obtain basic knowledge of Geomatics Engineering
    2. Design and develop hardware and/or software-based systems, components or processes in order to solve the defined problems,
    3. Recognize, analyze and solve engineering problems in surveying, planning, GIS and remote sensing fields

    Method of assessment

    1. Written exam
    2. Homework assignment
  2. Identify how to manage linear engineering structures in a GIS environment, build a network data model, analyze in the applications for determining the best route and resource service area, and interpret the results.

    Contribution to Program Outcomes

    1. Design and develop hardware and/or software-based systems, components or processes in order to solve the defined problems,
    2. Recognize, analyze and solve engineering problems in surveying, planning, GIS and remote sensing fields
    3. Use modern equipment and software packages related to Geomatics Engineering

    Method of assessment

    1. Written exam
    2. Homework assignment
  3. Understand the digital surface model production functions, reveals the difference of interpolation, density and clustering analysis functions according to the data source, analyze them on the related application area, and interpret the results.

    Contribution to Program Outcomes

    1. Design and develop hardware and/or software-based systems, components or processes in order to solve the defined problems,
    2. Recognize, analyze and solve engineering problems in surveying, planning, GIS and remote sensing fields
    3. Use modern equipment and software packages related to Geomatics Engineering
    4. Support his/her ideas with various arguments and present them clearly to a range of audience, formally and informally through a variety of techniques

    Method of assessment

    1. Written exam
    2. Homework assignment
  4. Performs applications such as site selection and risk analysis with various surface analysis and map algebra techniques, and interprets the results.

    Contribution to Program Outcomes

    1. Design and develop hardware and/or software-based systems, components or processes in order to solve the defined problems,
    2. Recognize, analyze and solve engineering problems in surveying, planning, GIS and remote sensing fields
    3. Use modern equipment and software packages related to Geomatics Engineering
    4. Support his/her ideas with various arguments and present them clearly to a range of audience, formally and informally through a variety of techniques

    Method of assessment

    1. Written exam
    2. Homework assignment
  5. Decide which spatial analysis functions to use in solving spatial problems, and analyze in the GIS application environment.

    Contribution to Program Outcomes

    1. Support his/her ideas with various arguments and present them clearly to a range of audience, formally and informally through a variety of techniques

    Method of assessment

    1. Written exam
    2. Homework assignment
    3. Laboratory exercise/exam
   Contents Up
Week 1: Introduction to geographic analysis concept.
Week 2: Space geometry, geographic data structures and raster/vector representation.
Week 3: Topology concept and geographic relations (4IM, 9IM).
Week 4: Application for Problem solving with geographic processing model. Homework.
Week 5: The set theory and boolean algebra, vector overlay analysis such as union, intersect, difference.
Week 6: Graph Theory and management of network data structure in GIS. Homework.
Week 7: Network Analysis- connectivity, shortest path, and service area.
Week 8: Measurement of spatial distributions- mean center, median center, central feature, standard distance, weighted standard distances. Midterm Examination. Homework.
Week 9: Density Analysis- lineer, kernel.
Week 10: The structure of Triangular Irregular Network (TIN) and the production of Digital Terrain Model. Homework.
Week 11: Interpolation Methods- IDW, Spline, Natural Neighbor.
Week 12: Surface Analysis- slope, aspect, viewshed, hillshade.
Homework.
Week 13: Map algebra, geographic weights and neighborhood analysis.
Week 14: General evaluation of analysis and statistical techniques.
Homework and presentation.
Week 15*: -
Week 16*: Final Examination.
Textbooks and materials: Ders eğitim platformları ( http://arifcagdas.com/dersler/ )
- Ders online eğitimi ve materyalleri (MS Teams)
- Ders materyalleri ve değerlendirme ortamı (Moodle)


Değerlendirme Notu: 6 ödev/uygulamanın en az 3'ünü teslim etmek ders yükümlülüğüdür /(VF) ve derse katılım esaslarına uyulmalıdır (NA)
Evaluation Note: Submitting at least 3 of 6 assignments/applications is a course obligation (VF) and class participation principles must be followed (NA).
Recommended readings: - Worboys, M, Duckham, M., GIS : A Computing Perspective, Second Edition, CRC Press, 2004.Diestel, R., 2006. Graph Theory- Graduate Texts in Mathematics, Springer, ISBN-10 3-540-26183-4, NY, USA.
- Kainz, W., The Mathematics of GIS, V.2.1, Textbook, University of Vienna, 2010.
- Smith, M. J de, Goodchild, M.F., Longley, P.A., Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools, Second Edition, British Library Catalogue, 2007.
- Anselin, L., Rey, S. J., Perspectives on Spatial Data Analysis, Springer, ISBN: 978-3642019753, 2010.
- Lloyd, C., Spatial Data Analysis: An Introduction to GIS Users, Oxford University Press, ISBN: 978-0199554324, 2010.
- Robert P. Haining, Spatial data analysis: theory and practice.
- Sullivan, D., Unwin, D. J., 2010. Geographic Information Analysis, Wiley, ISBN: 978-0470288573
  * Between 15th and 16th weeks is there a free week for students to prepare for final exam.
Assessment Up
Method of assessment Week number Weight (%)
Mid-terms: 0
Other in-term studies: 0
Project: 0
Homework: 4-6-8-10-12-14 60
Quiz: 0
Final exam: 16 40
  Total weight:
(%)
   Workload Up
Activity Duration (Hours per week) Total number of weeks Total hours in term
Courses (Face-to-face teaching): 2 14
Own studies outside class: 1.5 14
Practice, Recitation: 1 14
Homework: 5 6
Term project: 0 0
Term project presentation: 0 0
Quiz: 0 0
Own study for mid-term exam: 0 0
Mid-term: 0 0
Personal studies for final exam: 9 1
Final exam: 2 1
    Total workload:
    Total ECTS credits:
*
  * ECTS credit is calculated by dividing total workload by 25.
(1 ECTS = 25 work hours)
-->