• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

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

1. Grasp spatial analysis and query functions in GIS, implement in related working area after revealing differences.

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Geodesy and Photogrammetry Engineering
   2. Formulate and solve advanced engineering problems
   3. Design and conduct research projects independently
   4. Work effectively in multi-disciplinary research teams
   5. Find out new methods to improve his/her knowledge.

   Method of assessment

   1. Written exam
   2. Homework assignment
   3. Laboratory exercise/exam
2. Identify how to manage linear engineering structures in GIS environment, build network data model, analyse in the applications as determining the best route and resource service area.

   Contribution to Program Outcomes

   1. Formulate and solve advanced engineering problems
   2. Recognize, analyze and solve engineering problems in surveying, planning, GIS and remote sensing fields
   3. Operate modern equipments and hardwares, and use related technical skills in the field of Geodesy and Photogrammetry Engineering.
   4. Design and conduct research projects independently
   5. Work effectively in multi-disciplinary research teams
   6. Develop an awareness of continuous learning in relation with modern technology
   7. Find out new methods to improve his/her knowledge.
   8. Effectively express his/her research ideas and findings both orally and in writing

   Method of assessment

   1. Written exam
   2. Homework assignment
   3. Laboratory exercise/exam
3. Analyse functions on producing digital terrain model, differences of interpolation and density analysis functions as to data sources.

   Contribution to Program Outcomes

   1. Define and manipulate advanced concepts of Geodesy and Photogrammetry Engineering
   2. Recognize, analyze and solve engineering problems in surveying, planning, GIS and remote sensing fields
   3. Operate modern equipments and hardwares, and use related technical skills in the field of Geodesy and Photogrammetry Engineering.
   4. Design and conduct research projects independently
   5. Work effectively in multi-disciplinary research teams
   6. Find out new methods to improve his/her knowledge.
   7. Effectively express his/her research ideas and findings both orally and in writing

   Method of assessment

   1. Written exam
   2. Homework assignment
   3. Laboratory exercise/exam
4. Decide which spatial analysis functions are used to solve spatial problems such as site selection and risk analysis, determine processing steps for the application.

   Contribution to Program Outcomes

   1. Formulate and solve advanced engineering problems
   2. Review the literature critically pertaining to his/her research projects, and connect the earlier literature to his/her own results
   3. Recognize, analyze and solve engineering problems in surveying, planning, GIS and remote sensing fields
   4. Operate modern equipments and hardwares, and use related technical skills in the field of Geodesy and Photogrammetry Engineering.
   5. Design and conduct research projects independently
   6. Develop an awareness of continuous learning in relation with modern technology
   7. Find out new methods to improve his/her knowledge.
   8. Effectively express his/her research ideas and findings both orally and in writing

   Method of assessment

   1. Written exam
   2. Homework assignment
   3. Laboratory exercise/exam
5. Document and report GIS analysis results, design result maps according to application need.

   Contribution to Program Outcomes

   1. Recognize, analyze and solve engineering problems in surveying, planning, GIS and remote sensing fields
   2. Operate modern equipments and hardwares, and use related technical skills in the field of Geodesy and Photogrammetry Engineering.
   3. Support his/her ideas with various arguments and present them clearly to a range of audience, formally and informally through a variety of techniques
   4. Write progress reports clearly on the basis of published documents, thesis, etc

   Method of assessment

   1. Homework assignment
   2. Laboratory exercise/exam
   3. Seminar/presentation

Contents
Week 1:	Introduction to spatial analysis concept and applications
Week 2:	Space geometry, spatial data structures and raster/vector representation
Week 3:	Topology concept and rule refinition
Week 4:	The set theory and boolean algebra, Vector overlay analysis such as union, intersect, difference, etc.
Week 5:	Application for Vector-based site selection analysis
Week 6:	Graph Theory and management of network data structure in GIS
Week 7:	Network Analysis- connectivity, shortest path, and service area
Week 8:	Midterm Exam
Week 9:	Density Analysis- lineer, kernel, etc.
Week 10:	Spatial Interpolation Methods- IDW, Spline, Natural Neighbor, etc.
Week 11:	The structure of Triangular Irregular Network (TIN) and the production of Digital Terrain Model
Week 12:	Surface Analysis- slope, aspect, viewshed, hillshade, etc.
Week 13:	Map Algebra
Week 14:	Raster-based calculations and analysis algorithm
Week 15*:	Application for Problem solving with geo-processing model
Week 16*:	Final Exam
Textbooks and materials:	ders slaytları ve notları / course slides and textbooks (hazırlayan / prepared by: A.Ç. Aydınoğlu)
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
Method of assessment	Week number	Weight (%)
Mid-terms:	8	30
Other in-term studies:		0
Project:		0
Homework:	4, 12	30
Quiz:		0
Final exam:	16	40
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	14
Own studies outside class:	4	15
Practice, Recitation:	0	0
Homework:	5	12
Term project:	0	0
Term project presentation:	0	0
Quiz:	0	0
Own study for mid-term exam:	5	2
Mid-term:	1	2
Personal studies for final exam:	6	2
Final exam:	1	2
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)