Course Syllabus

Course Information：

Year-Semester Course Number Course Name Phase Credits Hours Required/Elective Instructor Class Student NO. Withdraw Note

Spring Semester
Year 2025 343357 Advanced Urban Data Analytics and Modeling 1 3.0 3 ★ Lin Yu-Ta MSAI 0 0 2週,先1樓城市科學實驗室【停開】

Year-Semester	Course Number	Course Name	Phase	Credits	Hours	Required/Elective	Instructor	Class	Student NO.	Withdraw	Note
Spring Semester Year 2025	343357	Advanced Urban Data Analytics and Modeling	1	3.0	3	★	Lin Yu-Ta	MSAI	0	0	2週,先1樓城市科學實驗室【停開】

Course Plan：

Instructor Lin Yu-Ta

Email Roylin918@gmail.com

Last Updated 2024-12-25 10:28:13

Course Objective 當代都市議題經常為跨領域、開放性的複雜問題，在資料蒐集與分析上，需要整合地理空間、時間的維度；在分析與對策上，須平衡公部門、私部門、民眾等多重利害關係者的，是一門相當複雜但重要的學問。本課程從真實的都市議題出發，教導學生掌握地理空間資訊、資料分析、資料建模、資訊視覺化、策略論述等綜合能力，培養學生對於都市複雜議題的觀察與剖析，進而對真實環境提出洞見與創意。 Contemporary urban issues are complex, requiring the integration of geographical, and temporal dimensions in data collection and analysis. Balancing the interests of stakeholders like the public, and private sectors is crucial. This course focuses on real urban issues, teaching skills in geographical information, data analysis, modeling, visualization, and strategic discourse. It aims to cultivate students' ability to observe, analyze urban issues, and propose insights for real environments. It's an advanced course, requiring basic programming and GIS concepts. Through group projects, students learn API usage, geographic computation, model building, and data visualization. The course includes one midterm report and one final project report.

Course Schedule
(Week 1-16) 第1週：都市數據應用概論第2週：地理空間資訊簡介與視覺化第3週：時空資料庫簡介與操作第4週：空間資料處理、地理運算第5週：機器學習簡介與模型實作第6-8週：分組實作第9週：期中分組報告第10-12週：模型調校第13-14週：模型解讀第15週：都市論述與策略建議第16-17週：分組實作第18週：期末分組報告 Week 1: Introduction to Urban Data Applications Week 2: Introduction to GIS and Visualization Week 3: Introduction to Spatiotemporal Database and ETL Week 4: Data Processing and Geoprocessing Week 5: Introduction to Machine Learning and Data Modeling Weeks 6-8: Group Projects Week 9: Midterm Group Report Weeks 10-12: Model Calibration Weeks 13-14: Model Interpretation Week 15: Urban Arguments and Strategies Weeks 16-17: Group Projects Week 18: Final Group Report

Evaluation and grading policy 個人課程參與 30%：平時上課出席率、討論參與度、每週進度等綜合評比。分組期中成果 30%：各組期中簡報的成果總體評比，同一組每個成員評分相同。分組期末成果 40%：各組期中簡報的成果總體評比，同一組每個成員評分相同。 Individual Course Participation 30%: Comprehensive assessment of regular class attendance, participation in discussions, weekly progress, etc. Group Midterm Results 30%: Overall evaluation of each group's midterm presentation results, with the same score given to each member within the same group. Group Final Results 40%: Overall evaluation of each group's final presentation results, with the same score given to each member within the same group.

Materials All right reserved. Do not reproduce without permission.

Foreign-language textbooks:No
必備軟體/語言：Python, QGIS 選用軟體/語言：R-studio, Tableau Public 數據來源：國內外開放資料 Required Software/Languages: Python, QGIS Optional Software/Languages: R-studio, Tableau Public Data Sources: Domestic and international open data

The access to curricular consultation 授課老師、助教一名
Email: roylin@mail.ntut.edu.tw

Expanding teaching and resources

The course corresponds to the SDGs SDG3：良好健康和福祉（Good Health and Well-being）
SDG4：優質教育（Quality Education）
SDG8：尊嚴就業與經濟發展（Decent Work and Economic Growth）
SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure）
SDG11：永續城市與社區（Sustainable Cities and Communities）
SDG16：和平正義與有力的制度（Peace, Justice and Strong Institutions）

Does the course incorporate AI

Note
　

Instructor	Lin Yu-Ta
Email	Roylin918@gmail.com
Last Updated	2024-12-25 10:28:13
Course Objective	當代都市議題經常為跨領域、開放性的複雜問題，在資料蒐集與分析上，需要整合地理空間、時間的維度；在分析與對策上，須平衡公部門、私部門、民眾等多重利害關係者的，是一門相當複雜但重要的學問。本課程從真實的都市議題出發，教導學生掌握地理空間資訊、資料分析、資料建模、資訊視覺化、策略論述等綜合能力，培養學生對於都市複雜議題的觀察與剖析，進而對真實環境提出洞見與創意。 Contemporary urban issues are complex, requiring the integration of geographical, and temporal dimensions in data collection and analysis. Balancing the interests of stakeholders like the public, and private sectors is crucial. This course focuses on real urban issues, teaching skills in geographical information, data analysis, modeling, visualization, and strategic discourse. It aims to cultivate students' ability to observe, analyze urban issues, and propose insights for real environments. It's an advanced course, requiring basic programming and GIS concepts. Through group projects, students learn API usage, geographic computation, model building, and data visualization. The course includes one midterm report and one final project report.
Course Schedule (Week 1-16)	第1週：都市數據應用概論第2週：地理空間資訊簡介與視覺化第3週：時空資料庫簡介與操作第4週：空間資料處理、地理運算第5週：機器學習簡介與模型實作第6-8週：分組實作第9週：期中分組報告第10-12週：模型調校第13-14週：模型解讀第15週：都市論述與策略建議第16-17週：分組實作第18週：期末分組報告 Week 1: Introduction to Urban Data Applications Week 2: Introduction to GIS and Visualization Week 3: Introduction to Spatiotemporal Database and ETL Week 4: Data Processing and Geoprocessing Week 5: Introduction to Machine Learning and Data Modeling Weeks 6-8: Group Projects Week 9: Midterm Group Report Weeks 10-12: Model Calibration Weeks 13-14: Model Interpretation Week 15: Urban Arguments and Strategies Weeks 16-17: Group Projects Week 18: Final Group Report
Evaluation and grading policy	個人課程參與 30%：平時上課出席率、討論參與度、每週進度等綜合評比。分組期中成果 30%：各組期中簡報的成果總體評比，同一組每個成員評分相同。分組期末成果 40%：各組期中簡報的成果總體評比，同一組每個成員評分相同。 Individual Course Participation 30%: Comprehensive assessment of regular class attendance, participation in discussions, weekly progress, etc. Group Midterm Results 30%: Overall evaluation of each group's midterm presentation results, with the same score given to each member within the same group. Group Final Results 40%: Overall evaluation of each group's final presentation results, with the same score given to each member within the same group.
Materials	All right reserved. Do not reproduce without permission. Foreign-language textbooks:No 必備軟體/語言：Python, QGIS 選用軟體/語言：R-studio, Tableau Public 數據來源：國內外開放資料 Required Software/Languages: Python, QGIS Optional Software/Languages: R-studio, Tableau Public Data Sources: Domestic and international open data
The access to curricular consultation	授課老師、助教一名 Email: roylin@mail.ntut.edu.tw
Expanding teaching and resources
The course corresponds to the SDGs	SDG3：良好健康和福祉（Good Health and Well-being） SDG4：優質教育（Quality Education） SDG8：尊嚴就業與經濟發展（Decent Work and Economic Growth） SDG9：產業創新與基礎設施（Industry, Innovation and Infrastructure） SDG11：永續城市與社區（Sustainable Cities and Communities） SDG16：和平正義與有力的制度（Peace, Justice and Strong Institutions）
Does the course incorporate AI
Note