How can we address real-world challenges in conservation science without a solid understanding and practical expertise in analysing spatial data, alongside a robust grasp of emerging AI tools? Today’s conservation scientists and practitioners have access to an unprecedented volume of spatial and remote sensing data, and Geographic Information Systems (GIS) are increasingly applied in geography, ecology and conservation to help tackle global, regional and local conservation issues. In parallel, AI is transforming how spatial and non-spatial data are analysed and interpreted across the natural and social sciences. As these fields increasingly focus on large spatial datasets, employers frequently report skills shortages in GIS and associated AI competencies. Consequently, developing these skills, including AI literacy, applying AI to conservation problems in both theory and practice, and using AI for scenario planning and futures thinking, can open valuable career opportunities. This module introduces GIS as a tool for solving spatial problems, equipping you with marketable skills relevant to both research and commercial contexts. You will learn a range of methods for the collection, presentation and analysis of spatial data, alongside hands-on training in the most widely used GIS software. The module begins with core GIS principles, including key concepts, data sources, map creation and transformation, together with relevant AI tools. It then progresses to more advanced GIS operations, such as spatial data manipulation, vector and raster analysis, and an introduction to Remote Sensing. No prior knowledge of GIS, AI, statistics or programming is required, and students from any discipline are welcome to join this module.
Lectures, 12, Seminars/PC workshop 20
The module is optional for the following courses
MSc Conservation Science
Also available as an elective module.
Report: Assessment Details: Report of GIS analysis worth 50%.
Report. Assessment Details: Report of GIS analysis & AI Analysis 1,500 words worth 50%. This assessment is pass/compulsory
Reassessment Method: Like-for-like (different topic choice where specified).
The University is committed to ensuring that core reading materials are in accessible electronic format in line with the Kent Inclusive Practices. The most up to date reading list for each module can be found on the university's reading list pages.
On successfully completing the module, students will be able to:
1. Demonstrate a comprehensive and advanced understanding of the principles and concepts of GIS and Remote Sensing
2. Apply advanced technical competence using commercial GIS software, integrating and managing spatial data from multiple sources to undertake robust spatial analyses
3. Develop and implement advanced GIS-based analytical methodologies to address real-world practical and strategic problems
4. Apply AI-based methodologies and within GIS workflows to perform spatial and non-spatial analyses that support evidence-based decision-making in real-world conservation and environmental contexts
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