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GEOG41090

Academic Year 2026/2027

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.

Use of AI:

The School of Geography does not allow the use of content generative AI.  Any use of AI (for example grammar, writing support, translation- outside of that approved by UCD Access) will need to be agreed with your supervisor in advance and specified in a mandatory appendix. Even if there was no use an appendix that addresses AI use is required for all dissertations. Any use must adhere to academic integrity and UCD rules. The only exception to this will be in agreement with your supervisor, and this requires an explicit statement in your dissertation.

More deails: https://www.ucd.ie/registrar/ucdstrategicpoliciesandinitatives/ai-at-ucd/ai-at-UCD

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.

The United Nations identified seventeen Sustainable Development Goals (SDGs) as core to the 2030 Agenda for Sustainable Development, and UCD contributes in general to SDG 4 Quality Education. Further SDGs explored within this module if relevant are listed below. A scale of 1 - 5 indicates the extent to which the SDG is covered.


Goal Name Description Coverage

End poverty in all its forms everywhere 3

End hunger, achieve food security and improved nutrition and promote sustainable agriculture 2

Ensure healthy lives and promote well-being for all at all ages 4

Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all 3

Achieve gender equality and empower all women and girls 2

Ensure availability and sustainable management of water and sanitation for all 2

Ensure access to affordable, reliable, sustainable and modern energy for all 4

Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all 3

Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation 2

Reduce inequality within and among countries 5

Make cities and human settlements inclusive, safe, resilient and sustainable 5

Ensure sustainable consumption and production patterns 4

Take urgent action to combat climate change and its impacts 5

Conserve and sustainably use the oceans, seas and marine resources for sustainable development 3

Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss 3

Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels 5

Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development 5
 

Student Effort Hours:
Student Effort Type Hours
Specified Learning Activities

50
Autonomous Student Learning

200
Lectures

12
Laboratories

13
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 Component repeat (in-module) Offered
Exam (In-person): Final Exam End of trimester
Duration:
2 hr(s)
 Graded No
60
 No
Individual Project: Final Assignment Week 10, Week 11, Week 12, Week 14, Week 15 Graded No
40
 No

As part of UCD's student support, under the Additional Consideration policy, extensions may be available for the following assessments in the module: Assignment (including essay/poster), Portfolio, Reflective Assignment, Report(s), and Individual Project.


Carry forward of passed components
Yes
 

Resit In Terminal Exam
Spring Yes - 2 Hour
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.

Name Role
Xinyue Dong Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - 1 Fri 13:00 - 14:50
Autumn Lecture Offering 1 Week(s) - 2, 5, 11 Fri 13:00 - 14:50
Autumn Lecture Offering 1 Week(s) - 3, 4, 6, 7, 9, 10, 12 Fri 13:00 - 14:50
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