2024 Faculty Courses School of Environment and Society Department of Transdisciplinary Science and Engineering Graduate major in Global Engineering for Development, Environment and Society
Methods of Analysis for Socioeconomic and Environmental Data
- Academic unit or major
- Graduate major in Global Engineering for Development, Environment and Society
- Instructor(s)
- Yoshie Sakamoto / Naoya Abe
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 7-8 Fri
- Class
- -
- Course Code
- GEG.S412
- Number of credits
- 100
- Course offered
- 2024
- Offered quarter
- 1Q
- Syllabus updated
- Mar 14, 2025
- Language
- English
Syllabus
Course overview and goals
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.
Course description and aims
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
- 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/Objectives
| Course schedule | Objectives | |
|---|---|---|
| Class 1 | Introduction to this course: meaning, significance and basics structure of socioeconomic and environmental data、Basic commands of R | Brief assignment to understand and to implement what this session covers. |
| Class 2 | Measurement of the intensity of relationship between socioeconomic and environmental variables (correlation coefficients and scales) | Brief assignment to understand and to implement what this session covers. |
| Class 3 | Analysis of a relationship between socioeconomic and environmental aspects (regression analysis for quantitative variables) | Brief assignment to understand and to implement what this session covers. |
| Class 4 | Discreteness of our decisions for socioeconomic and environmental activities (discrete choice model) | Brief assignment to understand and to implement what this session covers. |
| Class 5 | 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 to understand and to implement what this session covers. |
| Class 6 | Analysis for socioeconomic and environmental data (Factor Analysis) | Brief assignment to understand and to implement what this session covers. |
| Class 7 | Qualitative analysis for socioeconomic and environmental categorical data (correspondence analysis) | Brief assignment to understand and to implement what this session covers. |
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 (necessary materials will be distributed.)
Reference books, course materials, etc.
None (necessary materials will be distributed.)
Evaluation methods and criteria
- Individual final report: about 60%
- Brief report for each session: about 40% in total sessions
Weights are subject to adjustment.
Related courses
- GEG.E413 ： Geospatial data analysis for environment studies
- GEG.E501 ： Environmental Impact Assessment
Prerequisites
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/