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

Learning outcomes

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

1. Understands basic spatial concepts and technical infrastructure, including GIS data formats, Euclidean space, topology space and network space.

   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. Understands the basis of network data structure and its usage areas in GIS. Develops applications for institutions that use network analysis algorithms and structural engineering structures.

   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. Use modern equipment and software packages related to Geomatics 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

   Method of assessment

   1. Written exam
3. Understands and applies spatial relationship analysis, multi-criteria decision support, prediction models and space-time relationship analysis techniques.

   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. 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. Term paper
4. Effectively applies spatial analysis techniques to solve complex spatial problems and make informed decisions in various application areas.

   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. Term paper
5. Specializes in requirement/gap analysis for a comprehensive GIS project design, creation of project implementation data, geographical analysis studies, production of project result outputs and effective project presentations.

   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. 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. Term paper

Contents
Week 1:	Basic Geographic/Spatial Concepts: GIS Data Formats, Space, Euclidean space, topology space, network compositions
Week 2:	Spatial Data Models (Spaghetti, Topological, Area-based models, object-based models) and Graph theory
Week 3:	Geographic Network concept, Network analysis, Network analysis techniques used in infrastructure facility management
Week 4:	Multi-Criteria Decision Support Systems (AHP, BWM and TOPSIS etc.) and GIS Supported Application
Week 5:	Suitability and Location Selection Analyzes with GIS
Week 6:	Advanced Spatial Analysis Techniques for Artificial Intelligence
Week 7:	Clustering and Outlier Analyzes, Spatial Autocorrelation and Relationship Pattern Analyzes
Week 8:	Forecasting Models and Geographically Weighted Regression
Week 9:	Midterm exam. Space-Time Cube Analysis
Week 10:	Requirements/gap analysis for term project design
Week 11:	Generation of term project application data
Week 12:	Geographic Analyses for term project
Week 13:	Producing Term Project Result Outputs
Week 14:	Term Project Presentations
Week 15*:	-
Week 16*:	Final exam
Textbooks and materials:	Ders sunumları ve ders notları / Course notes and slides
Recommended readings:	Dimitris Kavroudakis, Helen Briassoulis, Nikolaos Soulakellis, 2018. The Practice of Spatial Analysis. Springer International. Diptendu Sinha Roy, Harishchandra Dubey, Himansu Das, Rabindra K. Barik, 2019. CloudComputing for Geospatial Big Data Analytics. Springer. Jay Lee, 2023. Spatiotemporal Analytics. CRC Press. Norou Diawara, 2019. Modern Statistical Methods for Spatial and Multivariate Data. Springer. Praveen Kumar Rai, Varun Narayan Mishra, Prafull Singh, 2022. Geospatial Technology for Landscape and Environmental Management Sustainable Assessment and Planning. Springer.
	* 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:	9	30
Other in-term studies:	-	0
Project:	10-14	30
Homework:	-	0
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:	2	14
Practice, Recitation:	0	0
Homework:	0	0
Term project:	5	5
Term project presentation:	4	1
Quiz:	0	0
Own study for mid-term exam:	9	1
Mid-term:	1	1
Personal studies for final exam:	8	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)