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.
Week by week breakdown of the course:
Handling Spatial Data: Simple geometric calculations, distance and bearing, range searching and data sorting.
Divide and Conquer methods: Binary searching, Recursion, Line generalisation.
Grid Data and Arrays. Handling, traversing and searching raster data. Point and focal functions.
Problem solving by task partitioning. Nearest Neighbour analysis and cartogram generation examples.
More advanced raster and vector processing. Developing flow routing algorithms. Processing raw vector data.
Entry Requirements (not applicable to Visiting Students)
||Other requirements|| None
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2016/17, Available to all students (SV1)
||Block 2 (Sem 1)
|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||Mrs Karolina Galera
Tel: (0131 6)50 2572
© Copyright 2016 The University of Edinburgh - 3 February 2017 4:54 am