Postgraduate Course: Object Orientated Software Engineering: Spatial Algorithms (PGGE11106)
Course Outline
| School | School of Geosciences |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | The course assumes a prior working knowledge of the Python programming language and some knowledge of Object-Oriented design principles. It uses these to develop an understanding of computational algorithms used to manipulate and analyse spatial data. A range of examples are used to identify and utilise generic algorithmic principles across a variety of different spatial data types and problems. Concepts of algorithm efficiency are addressed but emphasis is also placed on clarity of design and implementation. 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.
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| Course description |
Course Description (week by week breakdown of the course)
Week 1:
Handling Spatial Data: Simple geometric calculations, range searching and data sorting.
Week 2
Divide and Conquer methods: Binary searching, Recursion, Line generalization.
Week 3
Grid Data and Arrays. Handling, traversing and searching raster data. Point and focal functions.
Week 4
Problem solving by task partitioning. Nearest Neighbour analysis and cartogram generation examples.
Week 5
Review, Coursework Project Help. Or an unassessed session on integrating Python with ArcMap
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Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2018/19, Available to all students (SV1)
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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 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
100% coursework |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- identify how different spatial data models can be implemented in object-oriented designs.
- 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.
- be able to develop Python classes suited to the representation and analysis of spatial data.
- be able to undertake spatial data Input/Output in standard formats and to interface Python with other proprietary software.
- be able to complete programming and software documentation within specified parameters and to a professional standard.
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Reading List
http://www.python.org/
Martelli A, (2009), Python in a Nutshell, O¿Reiley
Lutz M, Learning Python(2009), O¿Reiley
Sedgewick R, and Wayne K (2011):,Algorithms 4th edition
Westra, E 2015 Python GeoSpatial Analysis essentials. Packt publishing
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Contacts
| Course organiser | |
Course secretary | Mrs Karolina Galera
Tel: (0131 6)50 2572
Email: k.galera@ed.ac.uk |
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