Postgraduate Course: Object Orientated Software Engineering: Spatial Algorithms (PGGE11106)
|School||School of Geosciences
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Available to all students
|Summary||The course assumes a prior working knowledge of the Python programming language and of Object-Oriented design principles. It uses these to develop understanding of computational algorithms used to manipulate and analyse spatial data. A range of examples is 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. PGGE 11042 Object-Oriented Software Engineering Principles or other equivalent experience is a pre-requisite for this course.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2015/16, Available to all students (SV1)
||Block 3 (Sem 2)
|Learning and Teaching activities (Further Info)
Lecture Hours 18,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||100% coursework: practical assessment 20%; project (80%)
|No Exam Information
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.
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
|Course organiser||Dr Nick Hulton
Tel: (0131 6)50 2531
|Course secretary||Mr Edwin Cruden
Tel: (0131 6)50 2543
© Copyright 2015 The University of Edinburgh - 18 January 2016 4:34 am