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School of Engineering
- Undergraduate major in Mechanical Engineering
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- Common courses
- School of Materials and Chemical Technology
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- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
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School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Graduate major in Global Engineering for Development, Environment and Society
- Instructor(s)
- Abe Naoya
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- Day/Period(Room No.)
- Fri7-8(I4 B02-05)
- Group
- -
- Course number
- GEG.S412
- Credits
- 1
- Academic year
- 2023
- Offered quarter
- 1Q
- Syllabus updated
- 2023/3/22
- Lecture notes updated
- -
- Language used
- English
- Access Index
-
Course description and aims
This course aims to equip the enrolled students to have the basic understandings of the socioeconomic and environmental data as well as the skills to conduct several analytical methods by themselves. The course will be combined with online lectures and the hands-on exercise by using R.
Student learning outcomes
Enrolled students will have:
1) the basic knowledge of the meaning, significance and structure of basic socioeconomic and environmental data
2) the skills to conduct basic quantitative and qualitative analyses by utilizing the data above and,
3) the skills to understand/interpret properly and to present the results of those analyses to others.
Keywords
Socioeconomic data, environmental data, quantitative analysis, qualitative analysis, multivariate analysis, R
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
This course consists of both lectures and hands-on excises.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Introduction to this course: meaning, significance and basics structure of socioeconomic and environmental data、Basic commands of R | Brief assignment |
| Class 2 | Measurement of the intensity of relationship between socioeconomic and environmental variables (correlation coefficients and scales) | Brief assignment |
| Class 3 | Analysis of a relationship between socioeconomic and environmental aspects (regression analysis for quantitative variables) | Brief assignment |
| Class 4 | Discreteness of our decisions for socioeconomic and environmental activities (discrete choice model) | Brief assignment |
| Class 5 | Discreteness of our decisions for socioeconomic and environmental activities (multi-nominal discrete choice model, continued) | Brief assignment |
| Class 6 | Quantitative analysis for socioeconomic and environmental data (principal component analysis and clustering analysis)Measurement of performance efficiency of a decision-making unit when there are multiple inputs and outputs (basics of data envelopment analysis) | Brief assignment |
| Class 7 | Qualitative analysis for socioeconomic and environmental categorical data (correspondence analysis) | Final report |
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.
Textbook(s)
None (necessary materials will be distributed.)
Reference books, course materials, etc.
None (necessary materials will be distributed.)
Assessment criteria and methods
- Individual final report: about 60%
- Brief report for each session: about 40% in total sessions
Related courses
- GEG.E413 ： Geospatial data analysis for environment studies
- GEG.E501 ： Environmental Impact Assessment
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Students should have basic understanding and experience in statistics and multivariate analysis. There could be registration quota if the number of the registered students exceed more than 40.
Other
Content of each session may change and be adjusted, depending on the progress of lectures. Enrolled students need to prepare a laptop PC or Mac (either windows or mac) and to be ready to use R. For the installation of R, please check the following site and install it.
https://www.r-project.org/
