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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 the use of computer programming in spatial analysis,including batch processing, handling large datasets and developing novel algorithms and data products. 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.

Technological Infrastructures for GIS (PGGE11234) or other equivalent experience of basic python is a pre-requisite for this course.
Course description Week 1
Introduction to python programming: Computer basics; Version control software and repositories; Revision of python basics; Reading and writing files; Introduction to algorithm design: Finding minima and sorting

Week 2
Objects and flexible programs: Using the command line to make programmable programs; Objects and classes; Function fitting; Binary search, by loop or recursion

Week 3
Geospatial algorithm design: Geospatial packages: introduction to pyproj and gdal; Further algorithm design: Recursion; Handling raster data; Reusing existing code

Week 4
Raster functions: Focal functions; nested loops; Speeding up with geopandas; Practicing algorithm design

Week 5
Handling big, novel data: Example of algorithm design using LVIS lidar data; Batch processing; Handling big data; Revision of all aspects of course so far
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 2019/20, 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 5 weeks)

Final Project (70%) - submission due Friday, week 6
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 and to interface Python with other proprietary software and be able to complete programming and software documentation within specified parameters and to a professional standard
Reading List
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
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 secretaryMs Heather Penman
Tel: (0131 6)50
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