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- School of Science
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School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- 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
-
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)
- Varquez Alvin Christopher Galang
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- Day/Period(Room No.)
- Fri3-4(M-157(H1102))
- Group
- -
- Course number
- GEG.E413
- Credits
- 1
- Academic year
- 2023
- Offered quarter
- 1Q
- Syllabus updated
- 2023/3/20
- Lecture notes updated
- -
- Language used
- English
- Access Index
-
Course description and aims
With increasing resources for geospatial dataset and advances in computing technology, conducting environmental-related research can now be accelerated. In this course, students will learn the importance and advanced yet simple methods to conduct geospatial analysis. Using GIS tools and programming, students can explore environmental and socio-demographic conditions such as land reclamation, population growth, and even the monitoring of spread of diseases (e.g. COVID-19 virus)
Student learning outcomes
By the end of this course, students will be able to:
(1) Learn the basic concepts and modern techniques of geospatial analysis.
(2) Investigate and visualize an issue using GIS and programming.
(3) Be more aware and resourceful of up-to-date and widely available information or datasets.
Keywords
Geographic Information System (GIS); Geospatial Analysis; Cloud Computing; Programming; Visualization
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | ✔ Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Hands-on lecture style with discussion. The lecture is conducted in two parts. The concepts are introduced in the first part. The hands-on demonstration will be conducted in the second part.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Course Introduction: Overview, purpose, and definitions | Students will understand the importance of acquiring the skill of conducting geospatial analyses in both their researches and future career. The purpose of geospatial analyses and basic definitions and terminology will be discussed. |
| Class 2 | GIS mapping: Vectors and Rasters | Students will experience QGIS and its features for visualizing geospatial datasets. QGIS is an open-source tool for visualizing and analyzing geospatial data. |
| Class 3 | Quantifying urban population and changes from raster data. | Students will learn how to acquire geospatial information from multiple raster files using QGIS and python programming. |
| Class 4 | Visualizing, resampling, and processing DEMs through QGIS and programming. | Students will learn how to visualize and process Digital Elevation Model (DEM) dataset and learn the importance of DEM in urban planning and environmental studies. |
| Class 5 | Geospatial analyses using Google Earth Engine: reclamation trends in Jakarta | Students will explore the Google Earth Engine framework and learn how to process satellite information to display changes of the land surface. |
| Class 6 | Visualizing tabular data through Pandas: day-to-day cases of COVID-19 by country from the initial to the current stage | Students will learn how to use Pandas, a powerful module for processing tabular data. They will learn how to automatically construct time-series from table and publicly available online dataset, such as the daily cases of COVID-19. |
| Class 7 | Global mapping of COVID-19 cases and other tabular data | Continuing from the previous lecture, students will learn how to map the day-to-day changes in COVID-19 globally. |
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.
Communications will be conducted through T2Schola.
Textbook(s)
Handouts will be distributed through T2Schola.
Reference books, course materials, etc.
Manuals:
QGIS: https://docs.qgis.org/2.18/en/docs/user_manual/
Python: https://www.python.org/about/gettingstarted/
Conda: https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html
Installation processes:
QGIS for Windows (https://youtu.be/Cj1OrCP4ld0)
QGIS for MacOS (https://youtu.be/JsMBLjB7rig)
Python conda (https://youtu.be/R6Unq5jvAFY)
Pyton modules conda (https://youtu.be/DTxfQ3jiu2E)
Registering to cloud tools:
Kaggle: https://www.kaggle.com/
Getting started with Google Colab: https://towardsdatascience.com/getting-started-with-google-colab-f2fff97f594c
Signup for Google Earth Engine: https://signup.earthengine.google.com/
Assessment criteria and methods
Individual tasks are given after each class. (100%)
Related courses
- GEG.S412 ： Methods of Analysis for Socioeconomic and Environmental Data
Prerequisites (i.e., required knowledge, skills, courses, etc.)
None
Other
Students must utilize Windows 10 OS PC, macOS, or Ubuntu Computer during the lecture.
Additional instructions for set-up may be provided during or outside the lecture.
Meetings will be held in a PC room.
Under certain conditions, a hybrid-style may be implemented.
