2024 Faculty Courses School of Environment and Society Department of Architecture and Building Engineering Graduate major in Architecture and Building Engineering
Theories in Urban Analysis and Planning II
- Academic unit or major
- Graduate major in Architecture and Building Engineering
- Instructor(s)
- Toshihiro Osaragi / Maki Kishimoto
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 1-4 Mon
- Class
- -
- Course Code
- ARC.P442
- Number of credits
- 200
- Course offered
- 2024
- Offered quarter
- 4Q
- Syllabus updated
- Mar 14, 2025
- Language
- Japanese
Syllabus
Course overview and goals
In recent years, a variety of information relating to the national land, city and regions, has been being developed as a huge knowledge data base. This lecture overviews the theory and techniques to take advantage of this knowledge data base, and considers some applications by mathematical models.
Course description and aims
Students will be able to understand how to create, save, manage, display, and analyze the various spatial data.
Keywords
Spatial Data, Geographical Information Systems, Raster Data, Vector Data, Algorithm, Data Storage, Digital Elevation Model, Triangulated Irregular Network model, Data Error
Competencies
- Specialist skills
- Intercultural skills
- Communication skills
- Critical thinking skills
- Practical and/or problem-solving skills
Class flow
First, we discuss the basic technology related to spatial data, and then the case studies of urban analysis using spatial data will be introduced. Finally, every students will actually analyze the spatial data, and make a presentation about the results. Attendance is taken in every class.
Course schedule/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Raster Data Model ・data model ・creating raster ・map layer ・recoding ・overlay ・filtering ・buffering | Raster Data Model |
| Class 2 | Vector Data Model ・data structure ・topological overlay ・sliver polygon ・topology ・chain code ・comparison of raster and vector ・coordinate accuracy ・speed of computing | Vector Data Model |
| Class 3 | Simple Algorithm ・intersection of lines ・area of polygon ・point in polygon algorithm ・polygon overlay | Simple Algorithm |
| Class 4 | Data Storage ・run length code ・scan order | Data Storage |
| Class 5 | Algorithm for Data Storage ・hierarchical data structure (Quadtree, R-tree) and algorithm | Algorithm for Data Storage |
| Class 6 | DEM and TIN ・Digital Elevation Model ・Triangulated Irregular Network model ・spatial interpolation ・drainage networks | DEM and TIN |
| Class 7 | Data Error ・digitizing error ・topological error ・classification error | Data Error |
| Class 8 | Applications of GIS | Applications of GIS |
| Class 9 | spatial correlation analysis and its applications | spatial correlation analysis and its applications |
| Class 10 | Land use models and its applications | Land use models and its applications |
| Class 11 | Facility choice models and its applications | Facility choice models and its applications |
| Class 12 | Visualization of spatiotemporal data and its applicatios | Visualization of spatiotemporal data and its applicatios |
| Class 13 | Term paper submission | Term paper submission |
| Class 14 | Presentation_1 | Students will make a presentation of analysis using spatial data. |
| Class 15 | Presentation_2 | Students will make a presentation of analysis using spatial data. |
Study advice (preparation and review)
To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.
Textbook(s)
None required.
Reference books, course materials, etc.
Course materials are provided during class
Evaluation methods and criteria
Students will be assessed on their understanding of spatial data and its applications, and their ability to apply some mathematical models to analyze them.
Related courses
- UDE.E402 ： GIS and Digital Image Processing for Built Environment
Prerequisites
No prerequisites.