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DRPS : Course Catalogue : School of Geosciences : Postgraduate Courses (School of GeoSciences)

Postgraduate Course: Object Orientated Software Engineering: Spatial Algorithms (PGGE11106)

Course Outline
SchoolSchool of Geosciences CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThe course assumes a prior working knowledge of the Python 3 programming language. It uses these to develop an understanding of computational algorithms used to manipulate and analyse spatial data and the concepts behind object oriented programming. A range of practical examples are used to identify and utilise generic algorithmic principles across a variety of different spatial data types and problems. Concepts of algorithm efficiency and resource usage and design of clear, maintainable software, are addressed. There is a strong practical emphasis to learning on the course and it is delivered through a sequence of five, four-hour workshops that allow you to iteratively learn about aspects of algorithm design and then to implement these in practice for yourself. PGGE 11234 Technological Infrastructures for GIS or other equivalent experience is a pre-requisite for this course.
After this course a student should be able to interpret any piece of python code they may need to use, through a mixture of direct experience and knowing how to find and interpret online resources.
Course description Week 1: Introduction to python programming

- Github version control and code repository
- Computer basics
- Revision of loops and file I/O
- Introduction to algorithm design: Finding minima and sorting

ASSESSMENT: Weekly journal

Week 2: Objects and flexible programs

- Using the command line to make programmable programs
- Objects and classes
- Binary search: Loop and recursion

ASSESSMENT: Weekly journal

Week 3: Geospatial python

- Geospatial packages: pyproj and gdal
- Importing your own packages
- Function fitting
- Douglas-Peucker line generalization

ASSESSMENT: Weekly journal.

Week 4: Geospatial data formats

- HDF5 and geotiff packages
- Raster-vector intersections
- Batch processing: Large datasets

ASSESSMENT: Weekly journal. Individual project introduced.

Week 5: Big data and machine learning

- More geospatial packages: Geopandas
- RAM management
- A brief introduction to machine learning
- Machine learning; getting data into the machine

ASSESSMENT: None. Work on project
Entry Requirements (not applicable to Visiting Students)
Pre-requisites It is RECOMMENDED that students have passed Technological Infrastructures for GIS (PGGE11234)
Prohibited Combinations Other requirements A working knowledge of the basics of Python is essential, particularly data types (lists, dictionaries etc.) and flow control (if's, loops and functions).
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2023/24, Available to all students (SV1) Quota:  40
Course Start Block 3 (Sem 2)
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 98 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 100% coursework:

Learning journal and weekly code respository to be submitted every week (30%, marked over all 4 weeks) - submitted by 12pm every Friday.

Final Project (70%) - Friday 1st of March at 12pm.
Feedback Formative feedback will be given for each week¿s journal entry. These will all be marked at the end of the course.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understand object oriented programming.
  2. Identify how different spatial data models can be implemented in object-oriented designs.
  3. Have an understanding the principles of algorithm development and of generic concepts employed in algorithm design and be familiar with a range of algorithms used to manipulate and analyse spatial data.
  4. Be able to develop Python classes suited to the representation and analysis of spatial data.
  5. Be able to undertake spatial data Input / Output in standard formats.
Reading List
Note that all python documentation is online, and students are expected to become familiar with navigating this. Online documentation:

There are a number of very helpful forums online with answers for common questions. Writing the right term in to a search engine should take you directly to the answer on one of these:

For additional practice, online tutorials have kindly been made freely available from the link below using your university e-mail.

Downey A, 2015. Think Python: How to Think Like a Computer Scientist. 2nd Edition, Version 2.2.23, Green Tea Press.

Martelli A, (2009), Python in a Nutshell, O¿Reiley¿Lutz M, Learning Python(2013),

O¿Reiley¿Sedgewick R, and Wayne K (2011):,Algorithms 4th edition¿Westra, E 2015

Beazley, D.M., 2009. Python essential reference. Addison-Wesley Professional.
Additional Information
Course URL
Graduate Attributes and Skills Not entered
KeywordsPGGE11106 Algorithms,Java,object oriented design
Course organiserDr Steven Hancock
Tel: (01316)51 7112
Course secretaryMrs Katherine Ingram
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