授業の目的 【日本語】
Goals of the Course(JPN) | | | このコースでは、地域開発における重要なトピックと研究方法を紹介します。 このコースは、最新の空間データサイエンスのフレームワークに基づいており、地理的思考、空間依存、地域の不平等、リモートセンシング、空間クラスター、空間の不均一性などの重要なトピックを紹介します。 このコースでは主に、科学計算と再現性のある研究のためのプログラミング言語としてPythonを使用しています。 |
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授業の目的 【英語】
Goals of the Course | | | This course provides an introduction to key topics and research methods in regional development. The course is based on a modern spatial data science framework and introduces key topics such as geographic thinking, spatial dependence, regional inequality, remote sensing, spatial clusters, and spatial heterogeneity. The course mostly uses Python as a programing language for scientific computing and reproducible research. |
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到達目標 【日本語】
Objectives of the Course(JPN) | | - 経済学、地理学、データサイエンスのアイデアと手法を統合して、地域開発のプロセスを理解し、情報を提供します。
- 空間データ手法を使用して、パターンを明らかにし、複数のタイプの地理データから洞察を抽出します。
- 空間計量経済学と空間データサイエンスの分野における最新の研究方法のいくつかを理解します。 |
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到達目標 【英語】
Objectives of the Course | | - Integrate ideas and methods from economics, geography, and data science to understand and inform the process of regional development.
- Use spatial data methods to uncover patterns and extract insights from multiple types of geographic data.
- Understand some of the latest research methods in the fields of spatial econometrics and spatial data science. |
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授業の内容や構成
Course Content / Plan | | The structure of the course consists of 15 sessions divided into 3 broad topics. After each session students are expected to complete short research tasks and problem sets related to the main topics discussed in class.
01. Introduction and overview
Part I ) Foundations of quantitative regional studies
02. Principles of geography and mapping
03. Geographic thinking for regional development
04. Computational tools for spatial data science
05. Spatial data and new geo-datasets
Part II ) Spatial data analysis
06. Mapping and contiguity weights
07. Mapping and distance weights
08. Global spatial dependence
09. Local spatial dependence
10. Point pattern analysis
Part III ) Advanced topics
11. Spatial inequality dynamics
12. Spatial spillovers and diffusion processes
13. Spatial heterogeneity and scale effects
14. Clustering and regionalization
15. Spatial feature engineering |
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履修条件・関連する科目
Course Prerequisites and Related Courses | | | There is no precondition to take this course. However, a basic understanding of statistics and python programing is recommended. |
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成績評価の方法と基準
Course Evaluation Method and Criteria | | | Research tasks and problem sets (60%), final video presentation (40%). To receive credit for this course, students are expected to achieve an overall evaluation equal or superior to C- or C (where applicable). |
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教科書・参考書
Textbook/Reference Book | | - Grekousis, G. (2020). Spatial Analysis Methods and Practice: Describe – Explore – Explain through GIS. Cambridge: Cambridge University Press. doi:10.1017/9781108614528. An E-book is available through Nagoya University internet: https://www.cambridge.org/core/books/spatial-analysis-methods-and-practice/4C135005A621335D06CC63EFF17E3913
The following open license ebooks and tutorials are available for free on the internet
- Heiss, F. and Brunner, D. (2020). Using Python for introductory econometrics. Available at http://www.upfie.net
- Rey, S., Arribas-Bel, D., and Wolf, L. (2022) Geographic Data Science with PySAL and the PyData Stack. Available at https://geographicdata.science/book/intro.html
- Mendez, C. (2022) Data science tutorials. Available at http://deepnote.com/@carlos-mendez |
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課外学習等（授業時間外学習の指示）
Study Load(Self-directed Learning Outside Course Hours) | | - Students should create a (free) account in DISCORD(https://discord.gg/NKb5GJJmkd). Learning materials, problems sets, and other resources will be distributed via DISCORD. The invitation link to discord will be issued in the first class.
- Students should create a (free) account in DEEPNOTE (https://deepnote.com/sign-in?token=ec5ee6cd). This computing environment will be used for some of the problem sets of the class. Here is a short overview of this powerful data science platform: https://pub.towardsai.net/introduction-to-deepnote-real-time-collaboration-on-jupyter-notebook-18509c95d62f
- Students should create a (free) student account in NOTION (https://www.notion.so/Notion-for-students-teachers-adc631df15ee4ab9a7a33dd50f4c16fe). The following video tutorial on how to use notion is highly recommended: https://youtu.be/ONG26-2mIHU
- Students should create a (free) student account in LOOM (https://www.loom.com/education). All students will explain the solutions to the problem sets through LOOM. |
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注意事項
Notice for Students | | | For further inquires about this course, send an email to carlos@gsid.nagoya-u.ac.jp or book an appointment with professor using this booking system https://carlos777.youcanbook.me |
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使用言語
Language(s) for Instruction & Discussion | | |
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授業開講形態等
Lecture format, etc. | | 原則として対面で行う。例外的に、遠隔授業（同時双方向）を行う。
遠隔授業は Teams、Zoom等で⾏う。
※履修登録後に授業形態等に変更がある場合には、NUCＴの授業サイトで案内します。
In principle, lecture and seminar course subjects are offered in-person.
Online participation in classes (interactive communication classes via Teams, Zoom, etc.) may be permitted by the instructor under exceptional circumstances.
*Guidance will be posted on NUCT if there are any changes in the class format, etc. after registration. |
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遠隔授業（オンデマンド型）で行う場合の追加措置
Additional measures for remote class (on-demand class) | | | - For online learning and communication purposes, we use the following discord server https://discord.gg/NKb5GJJmkd . Links to most of our learning materials are available on this website (access credentials to private channels are issued in the first week of each semester). |
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