MSc Geographical Information Science

Fees for entry in 2027 have not yet been set. For reference, the fees for the academic year beginning September 2026 were as follows:

• MSc (full-time)
  UK students (per annum): £15,200
  International, including EU, students (per annum): £33,600

The fees quoted above are fully inclusive of tuition, administration and computational costs.

Fees for entry are subject to yearly review. The University reserves the right to increase your tuition fee by up to 7% each year for courses lasting more than one year, including to reflect rising costs associated with delivering our educational and wider student experience. Postgraduate fees information .

Always contact the admissions team if you are unsure which fee applies to your qualification award and method of attendance.

International student CAS deposit

Self-funded international applicants are required to pay a deposit of £2500 towards their tuition fees before a confirmation of acceptance for studies (CAS) is issued. Some applicants will be required to pay a higher deposit. More information on tuition fee deposits .

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

We know that student finance can be complicated. The links below provide further information to help guide you.

• Learn more about - student finance options for UK students .
• Learn more about - fees and finance for international students .
• Graduates of The University of Manchester and Manchester Metropolitan University can receive a 10% discount on their master's degree tuition fees as part of our Manchester Alumni Loyalty Discount scheme.

As Geographical Information Systems become increasingly available to the general public, the users of those technologies are becoming increasingly detached from the Geographical Information Science behind the software. Understanding GIS seeks to remedy this by using the Python programming language to explore how common GIS operations work and teach students to be able to develop their own solutions to a range of spatial problems.

The course will teach students the main paradigms and algorithms that underpin GIS, geocomputation and the Python programming language. In doing so, it removes reliance upon ‘black box’ software (e.g., ArcGIS Pro). Rather than simply teaching students to use Python as a GIS, this course uses Python to allow students to see what is happening inside the ‘black box’ – focusing on ‘how it works’, rather than simply ‘how to use it’.

The unit aims to:

• Explore GIS software works (as opposed to simply how to use it).
• Explore the fundamental principles of GIS and computing.
• Develop valuable skills in the Python programming language, including version control.
• Develop problem-solving skills by writing software
• Meet the requirements of employers looking for highly skilled GIS operators and developers

By taking this unit, students will develop advanced skills in GIS through gaining a detailed understanding of key foundational concepts and relevant computational principles (KU1); and core algorithms (KU3); as well as the key theoretical paradigms and arguments that underpin them (IS1). In doing so, students’ knowledge, understanding and intellectual skills relevant to the field will be elevated beyond those of a typical GIS ‘user’, and towards that of a highly skilled specialist (or ‘expert’).

This depth of understanding is combined with critical practical skills, including competencies in handling a range of data types and structures (PS1), use of the Python programming language to solve spatial problems and implement algorithms (PS2), and demonstrate competency in producing quality map outputs according to cartographic principles (PS3). These skills will develop alongside highly employable transferrable skills, including the direct and continuous application to spatial problem solving (TS1), which is the underpinning approach to learning used throughout the course. Other key transferrable skills include all aspects of the software development process, such as planning, writing and debugging software and version control (TS3). This provides a strong foundation for learning new programming languages, software or areas of application as desired. Software development good-practice is (PS1-3, TS1-3) encouraged through the direct assessment of elegance, efficiency, robustness and understanding of the code that the students have written.

Students will also gain experience utilising a programming forum via the VLE. Even for experienced programmers, identifying and addressing coding errors is a time-consuming task, so being able to utilise forums effectively (such as Stack Overflow) is a key skill for those working with code and data, particularly when novel or unfamiliar problems are encountered. Students will utilise the forum to search for existing posts or create their own when a difficult problem is encountered, and as the course and their expertise develops, will be able to respond to other students’ queries, further enhancing their own learning and depth of understanding.

In order to facilitate this learning and empower the students to keep up with the progress of the course, Understanding GIS is classed as AI Prohibited, and has a detailed course-specific AI Policy. 

The current version of the course will introduce students to the following key concepts:

• Coordinate Reference Systems
• Geodesy
• Vector Data Structures
• Topology
• Spatial indexes
• Generalisation
• Cartographic Distortion
• Raster Data Structures
• Network Data Structures
• Parallel Computing
• Simulation

These concepts are examined in the context of a range of applied examples relating to:

• Choropleth mapping
• Geodesic measurement
• Accessibility analysis
• Geometry simplification (Visvalingam-Whyatt algorithm)
• Finite-scale distortion analysis (Canter’s method)
• Flooding analysis (flood-fill algorithm)
• Visibility analysis (8WS Viewshed algorithm)
• Least-cost path analysis (A* algorithm)
• Greenspace Exposure (Green Visibility Index)
• Residential Segregation (Schelling model)

Many of these core concepts are foundational to Geographical Information Science and so are essential for students to develop GIS expertise, though will be updated as appropriate to reflect ongoing technological developments in the field. The listed applications might also be updated to reflect new research and technologies that need to be emphasised. 
 

The course unit will be delivered through ten hybrid lecture and practical sessions. Each of the teaching weeks will involve approximately one-hour of lecture material (L) and two-hours of practical material (P), all delivered in a computer lab in a single 3-hour session (normally in the format 30L-30P-30L-90P minutes).

Each session will introduce a theoretical grounding for the related practical, as well as the related principles of computing and software development. This will comprise integrated lecture and practical elements ensuring that knowledge can be applied and consolidated. The course will progressively build skills in GIS and Python programming by visiting a new and increasingly complex topic area each week.

Sessions will draw upon a range of resources, including PowerPoint slides for lectures, web-based walkthrough guides for practical sessions, links to relevant web resources, an online forum on the VLE. Each session is followed up with a code ‘solution’ to support learning.

The course necessarily has a steep learning curve, and students will be expected to practice this programming outside of the classroom in order to fully understand the material. In recognition of this, the required reading for the course is extremely limited (as students should be spending this time practicing their coding).

• Understand the foundational concepts of Geographical Information Science
• Understand key GIS algorithms, including how they work, how to implement them, and their strengths and weaknesses

• Critically evaluate key GIS paradigms

   

• Demonstrate competency in handling and integrating multiple types of spatial data.
• Demonstrate competency in using the Python programming language to solve spatial problems and implement algorithms
• Demonstrate competency in producing high-quality map outputs programmatically, according to key cartographic principles

• Develop problem solving skills, enabling students to break down complex problems into solutions that can be implemented in code
• Demonstrate skills in the software development process, including planning, writing and debugging software and version control

Assessment task 1
Assessment 1 comprises a simple programming task in which the students are set a simple spatial problem. The solution consolidates the practical and theoretical knowledge that students have learned in the first weeks of the course.

Students must submit fully commented code with complete version control history for evaluation, as well as justifying their approach and explaining the results in their report to demonstrate understanding

Algorithm + 1,000-word report.
40% weighting.

Assessment task 2
Assessment 2 comprises converting an existing algorithm published in the GIS literature into a script in order to undertake data analysis for a client.

Students must submit fully commented code with complete version control history for evaluation, as well as justifying their approach and explaining the results in their report to demonstrate understanding. 
. 
Algorithm + 1,000-word report.
60% weighting.

Feedback provided via the Canvas, 15 working days after submission.
Additional formative feedback available in class and via the programming forum.

•    Battersby, S. E., Finn, M. P., Usery, E. L., & Yamamoto, K. H. (2014). Implications of web Mercator and its use in online mapping. Cartographica: The International Journal for Geographic Information and Geovisualization, 49(2), 85-101.
•    Brent, R. P. (1973). An Algorithm with Guaranteed Convergence for Finding a Zero of a Function. In Algorithms for Minimization without Derivatives, Englewood Cliffs, NJ: Prentice-Hall,
•    Bresenham, J. E. (1965). Algorithm for computer control of a digital plotter. IBM Systems journal, 4(1), 25-30.
•    Brinkmann, S. T., Kremer, D., & Walker, B. B. (2022). Modelling eye-level visibility of urban green space: Optimising city-wide point-based viewshed computations through prototyping. AGILE: GIScience Series, 3, 27.
•    Canters, F., Deknopper, R., & De Genst, W. (2005). A new approach for designing orthophanic world maps. In Proceedings of the 22nd International Cartographic Conference (pp. 9-16).
•    Crameri, F., Shephard, G. E., & Heron, P. J. (2020). The misuse of colour in science communication. Nature communications, 11(1), 1-10.
•    Douglas, D.H. and Peucker, T.K., 1973. Algorithms for the reduction of the number of points required to represent a digitised line or its caricature. The Canadian Cartographer 10 (2) 112-122.
•    Gosling P., C. & Symeonakis E. (2020). Automated map projection selection for GIS, Cartography and Geographic Information Science, 47:3, 261-276.
•    Guttman, A. (1984). R-trees: A dynamic index structure for spatial searching. In Proceedings of the 1984 ACM SIGMOD international conference on Management of data (pp. 47-57).
•    Hart, P. E.; Nilsson, N. J.; Raphael, B. (1968) A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Transactions on Systems Science and Cybernetics SSC4 4 (2): 100-107.
•    Huck, J., Whyatt, D., & Coulton, P. (2015). Visualizing patterns in spatially ambiguous point data. Journal of Spatial Information Science, 2015(10), 47-66.
•    Huck, J. J., Whyatt, J. D., Davies, G., Dixon, J., Sturgeon, B., Hocking, B., Tredoux C., Jarman, N. & Bryan, D. (2023). Fuzzy Bayesian inference for mapping vague and place-based regions: a case study of sectarian territory. International Journal of Geographical Information Science, 37(8), 1765-1786.
•    Huck, J. (2024). Evaluating Map Projections for Globemaking. The Cartographic Journal, 61(3), 207-217.
•    Huck, J. (2025). The QGIS Polygon Divider: Polygon partition into an irregular equal area grid. Environment and Planning B: Urban Analytics and City Science, 23998083251378340.
•    Huck, J. J., Dennis, M., & Labib, S. M. (2025). Addressing bias in the use of buffers for focal and geographically weighted analyses. International Journal of Geographical Information Science, 39(6), 1183-1202.
•    Karney, C. F. (2013). Algorithms for geodesics. Journal of geodesy, 87(1), 43-55.
•    Kaucic, B., & Zalik, B. (2002, April). Comparison of viewshed algorithms on regular spaced points. In Proceedings of the 18th spring conference on Computer graphics (pp. 177-183).
•    Labib, S. M., Huck, J. J., & Lindley, S. (2021). Modelling and mapping eye-level greenness visibility exposure using multi-source data at high spatial resolutions. Science of the Total Environment, 755, 143050.

Teaching and learning will be designed to be inclusive through providing materials online in advance of sessions in accessible formats (visual media, lecture recordings). Students can engage with discussion in class and online via Canvas.

Assessment instructions and criteria are clearly communicated in advance of the deadline via Canvas, the course webpage, and via lecture recordings. Although the assessments appear similar in scope, each is assessed on a range of criteria (report, algorithm, code, and map quality), and have key differences (A1 is achieving a goal, A2 is implementing an algorithm from the literature).

Individualised feedback is provided for all students for both assessments, and formative feedback is provided in class and via the programming forum. Assessments are spaced to give students time to act on feedback.

Essential software used as part of this unit meets accessibility requirements. All are open source and can be installed on any machine / Operating System, the integrated development environment provides accessible features such as word auto-completion, colour coding of variables names-functions-strings and inline spell-check, as well as the ability to modify further for specific needs (e.g., colour blindness).