2021 Theories in Urban Analysis and Planning II

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Academic unit or major
Graduate major in Architecture and Building Engineering
Osaragi Toshihiro  Tagashira Maki 
Course component(s)
Lecture    (ZOOM)
Day/Period(Room No.)
Course number
Academic year
Offered quarter
Syllabus updated
Lecture notes updated
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Course description and aims

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.

Student learning outcomes

Students will be able to understand how to create, save, manage, display, and analyze the various spatial data.


Spatial Data, Geographical Information Systems, Raster Data, Vector Data, Algorithm, Data Storage, Digital Elevation Model, Triangulated Irregular Network model, Data Error

Competencies that will be developed

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/Required learning

  Course schedule Required learning
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.

Out-of-Class Study Time (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.


None required.

Reference books, course materials, etc.

Course materials are provided during class

Assessment criteria and methods

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 (i.e., required knowledge, skills, courses, etc.)

No prerequisites.

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