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GEOG41090

Academic Year 2024/2025

Urban Data Analytics (GEOG41090)

Subject:
Geography
College:
Social Sciences & Law
School:
Geography
Level:
4 (Masters)
Credits:
10
Module Coordinator:
Dr Ye Tian
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

Urban Data Analytics is an increasingly vital discipline in understanding and addressing the complex challenges faced by cities worldwide. In this course, we delve into the intersection of data science, spatial analysis, and urban planning to provide students with a comprehensive understanding of urban data analytics. Whether you are a professional or new to the field, this course is designed to enhance your expertise and proficiency in utilizing data to inform urban decision-making. By the end of the course, students will emerge with a solid foundation in urban data analytics, capable of leveraging advanced data science techniques to derive actionable insights and drive positive change in urban environments.

About this Module

Learning Outcomes:

This course is mainly comprised of four modules: cluster analysis, network analysis, distance analysis, and GeoAI. It is designed to achieve the following learning outcomes:
• Familiarize yourself with various data sources, collection methods, and preprocessing techniques specific to urban data.
• Develop a comprehensive understanding of fundamental concepts and methodologies in spatial and temporal pattern detection.
• Explore the urban network and implications associated with urban environment.
• Cultivate critical thinking and problem-solving skills through practical exercises and case studies in distance analysis.
• Acquire proficiency in utilizing advanced GeoAI tools to derive insights from urban datasets.
• Collaborate with peers on projects that address urban issues and propose data-driven solutions.

Indicative Module Content:

Materials will be provided online.

Student Effort Hours:
Student Effort Type Hours
Lectures

12
Laboratories

13
Specified Learning Activities

50
Autonomous Student Learning

200
Total

275

Approaches to Teaching and Learning:
Learning with lab materials and lectures

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Participation in Learning Activities: Class attendance Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 9, Week 10, Week 11, Week 12, Week 14, Week 15 Graded No

10

 No
Assignment(Including Essay): Lab assignment Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 9, Week 10, Week 11, Week 12 Graded No

55

 No
Group Work Assignment: Final group project Week 14, Week 15 Graded No

10

 No
Exam (In-person): Final Exam End of trimester
Duration:
2 hr(s)
 Graded No

25

 No

Carry forward of passed components
Yes
 

Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

Not yet recorded.

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