2021 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
Media-enhanced courses: -
Day of week/Period (Classrooms): 1-4 Mon
Class: -
Course Code: ARC.P442
Number of credits: 200
Course offered: 2021
Offered quarter: 4Q
Syllabus updated: Jul 10, 2025
Language: English

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.