This lecture will introduce data scraping techniques to collect web-based data and teach existing statistical methods for
analyzing these data with a focus on urban environment data analysis. It will introduce students to analytical approaches
in urban planning research. Computer-based analysis techniques using R-programming will also be used.
This course develops the modeling skills essential for theoretical research in urban planning. It is aimed at students entering
into research, and introduces the approach of solving real urban planning problems through the use of models and spatial analysis.
Majority of the classes will include computer-based hands-on work and lectures to introduce the concepts and theories.
| Goals and objectives | Course Outcomes | |
|---|---|---|
| 1. | Students will learn statistical methods |
A-1
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| 2. | Students will learn the application of spatial analysis in urban planning |
A-1
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| 3. | Students will learn to find and solve urban planning problems through the application of spatial analyses |
A-1
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| Class schedule | HW assignments (Including preparation and review of the class.) | Amount of Time Required | |
|---|---|---|---|
| 1. | Introduction to course | Relevant readings for next class | 120分 |
| 2. | Introduction to types of research in urban planning. Why is data-oriented research important for urban planning? | Relevant readings for next class | 120分 |
| 3. | What is big-data? How can we get data? -Hands on session on data-scraping | Do Tutorial sheets 1 | 150分 |
| 4. | Introduction to Statistical Methods: Tabulation and representation of data; Measures of Central Tendency and Variances. | Relevant readings for next class | 120分 |
| 5. | Exploring the data in Rstudio: -Hands on sesssion | Do Tutorial sheets 2 | 150分 |
| Relevant readings for next class | 120分 | ||
| 6. | Prepare for Mid-term presentation | Presentation preparation | 120分 |
| 7. | Mid-term presentation | Relevant readings for next class | 120分 |
| 8. | Correlation Analysis and Regression Analysis: methods and application. | Relevant readings for next class | 120分 |
| 9. | Regression analysis in Rstudio: -Hands on sesssion | Do Tutorial sheets 3 | 150分 |
| Relevant readings for next class | 120分 | ||
| 10. | Regression analysis in GIS (Geographically Weighted Regression): -Hands on sesssion | Do Tutorial sheets 4 | 150分 |
| Relevant reading | 120分 | ||
| 11. | Forecasting and Time series analysis. Introduction to Non-linear trends. Discuss final report and presentation requirements. | Do Tutorial sheets 5 | 150分 |
| Decide on report subject | 30分 | ||
| Relevant reading | 120分 | ||
| 12. | Preparation for Final Presentation | Report writing | 180分 |
| Presentation preparation | 120分 | ||
| 13. | Preparation for Final Presentation | Report writing | 180分 |
| Presentation preparation | 120分 | ||
| 14. | Final Presentation and Report submission | Report writing and submission | 120分 |
| Total. | - | - | 2700分 |
| Class Discussions | Tutorial/Assignments | Mid-term Presentation | Final Presentation | Report | Total. | |
|---|---|---|---|---|---|---|
| 1. | 10% | 15% | 5% | 30% | ||
| 2. | 10% | 10% | 5% | 25% | ||
| 3. | 15% | 10% | 10% | 10% | 45% | |
| 4. | 0% | |||||
| 5. | 0% | |||||
| Total. | 20% | 40% | 10% | 10% | 20% | - |
Your attendance will be checked according to the ID card checking machine. Being absent for more than 5 classes will result
in D grade.
The evaluation scores will be weighted as:
Class Discussions (20%) - active participation in discussions is expected. Class discussions will be based on readings related to the lecture given before class.
Tutorial/Assignments (25%) (individual) -students should complete all tutorial sheets and hand-in their results.
Mid-term and Final presentation (25%) (group or individual) -students are expected to conduct and present a sample analysis of their problem.
Reports (30%) (individual) -compile a report regarding the problem of your choice and explain how the proposed problem may be solved through spatial analysis.
The evaluation scores will be weighted as:
Class Discussions (20%) - active participation in discussions is expected. Class discussions will be based on readings related to the lecture given before class.
Tutorial/Assignments (25%) (individual) -students should complete all tutorial sheets and hand-in their results.
Mid-term and Final presentation (25%) (group or individual) -students are expected to conduct and present a sample analysis of their problem.
Reports (30%) (individual) -compile a report regarding the problem of your choice and explain how the proposed problem may be solved through spatial analysis.
There is no set textbook and readings will be provided from different sources.
Interest in urban planning, data analysis and modeling is desired.
Basic Datascraping, R, GIS skills would be advantageous.
Basic Datascraping, R, GIS skills would be advantageous.
- Office Hours: Wednesday = 12:00 – 13:00
- Questions by email can be accepted at any time. E-mail: yasmin@shibaura-it.ac.jp
- Course that cultivates an ability for utilizing knowledge
- Course that cultivates a basic interpersonal skills
- Course that cultivates a basic problem-solving skills
| Work experience | Work experience and relevance to the course content if applicable |
|---|---|
| N/A | N/A |
- 4.QUALITY EDUCATION
- 11.SUSTAINABLE CITIES AND COMMUNITIES
Last modified : Sun Mar 21 17:16:13 JST 2021