CEG8719 : Geospatial Data, Analytics and AI with Project
CEG8719 : Geospatial Data, Analytics and AI with Project
- Offered for Year: 2024/25
- Module Leader(s): Professor Stuart Barr
- Co-Module Leader: Dr Alistair Ford
- Lecturer: Dr Craig Robson
- Owning School: Engineering
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 1 Credit Value: | 10 |
| Semester 2 Credit Value: | 10 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Pre-requisite
Modules you must have done previously to study this module
Pre Requisite Comment
N/A
Co-Requisite
Modules you need to take at the same time
Co Requisite Comment
N/A
Aims
This module aims to develop students’ knowledge of the applied use of geospatial data engineering and analytics to address substantive engineering, environmental and social challenges. The module will explore how modern geospatial data handling, standards, ethics, analysis, simulation modelling and visualisation approaches can be applied help deliver many of the UN sustainable development goals, climate resilience, robust infrastructure systems and sustainable urban planning.
Outline Of Syllabus
The module syllabus will explore the open source approaches employed in applied geospatial data engineering including:
Open source geospatial data
Spatial data and metadata standards
FAIR geospatial data and software
Open source web stack
Spatial databases and data management
Spatial Data Infrastructures (SDIs)
Geospatial data portals and APIs
The data engineering skills and knowledge will be further developed via coverage of the modern analytics and simulation modelling approaches that are transforming the applied utilization of geospatial data within engineering and environmental applications, including:
Fundamental quantitative methods
Modifiable Aerial Unit Problem (MAUP) and Ecological Fallacy
Applied spatial statistics
Spatial and geographical regression analysis
Unsupervised geospatial machine learning
Applied supervised geospatial machine learning
Applied geospatial AI methods
Spatial interaction models
Cellular Automata (CA) and agent-based modelling
Applied geospatial network analysis
Geospatial visualization and decision support
Learning Outcomes
Intended Knowledge Outcomes
Students will acquire knowledge and understanding of the role of geospatial data and analytics in real-world applications, appreciating the contemporary geospatial data solutions that are transforming the understanding of environmental and engineered systems. Students will acquire the required knowledge on the different open geospatial data and metadata standards, how to acquire and manage large scale geospatial data collections and have an excellent understanding of the open source geospatial software stack.
Knowledge and understanding of the analysis and simulation modelling approaches that are transforming the applied use of geospatial data within engineering and environmental applications will be developed. Students will develop a comprehensive knowledge of the applied use of spatial statistics and statistical modelling. This will be augmented with modern unsupervised and supervised machine learning and AI methods that driving modern geospatial data applications. Students will become knowledgeable of the various computational modelling approaches used to simulate urban and infrastructure systems.
Intended Skill Outcomes
Students will acquire a wide range of skills in the use of the geospatial open source software stack for the searching, acquisition, management and dissemination of geospatial data. The practical software manipulation skills will be applied to real-world applications building to the delivery of a ‘complete’ data manegment solution for a chosen application. Students will also develop practical skills in ensuring that data is employed in an open and FAIR manner, the importance of standards and ethical data management and dissemination. Students will enhance skills in the analysis of geospatial data using open source software solutions within a python scripting environment.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 26 | 1:00 | 26:00 | N/A |
| Guided Independent Study | Assessment preparation and completion | 2 | 34:00 | 68:00 | Project assessment work - 34-hrs per-semester |
| Guided Independent Study | Directed research and reading | 26 | 1:00 | 26:00 | Post lecture reading |
| Scheduled Learning And Teaching Activities | Practical | 12 | 3:00 | 36:00 | N/A |
| Scheduled Learning And Teaching Activities | Workshops | 8 | 1:00 | 8:00 | Student seminars on applied geospatial data and analytics |
| Scheduled Learning And Teaching Activities | Workshops | 4 | 1:00 | 4:00 | Project workshops 2 per-semester |
| Guided Independent Study | Independent study | 32 | 1:00 | 32:00 | N/A |
| Total | 200:00 |
Jointly Taught With
| Code | Title |
|---|---|
| CEG3719 | Geospatial Data, Analytics and AI |
Teaching Rationale And Relationship
Lectures are used to present the underlying theory and principles of the use of geospatial data engineering and analysis. Practical sessions will allow students to apply the theory in relation to real world applied environmental applications. Seminars will expose students to cutting edge research and show how environmental applications routinely employ high-level geospatial data/analysis/modelling concepts.
Reading Lists
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 1 | M | 50 | 2000 word report |
| Report | 2 | M | 50 | 2000 word report |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Oral Presentation | 1 | M | Semester 1 applied student seminars |
| Oral Presentation | 2 | M | Semester 2 applied student seminars |
Assessment Rationale And Relationship
The computer practical reports will assess the computational skills and understanding of students to implement geospatial open standards data acquisition, management and delivery, along with their ability to undertake advanced analysis and modelling for real-world applications. Progress in non-assessed practicals will be confirmed by formative assessments to be completed within the practical session. Seminars will help reinforce student understanding of the module content and help develop student presentation skills.
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- CEG8719's Timetable
Past Exam Papers
- Exam Papers Online : www.ncl.ac.uk/exam.papers/
- CEG8719's past Exam Papers
General Notes
N/A
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