2017/18 Taught Postgraduate Module Catalogue

GEOG5861M Geocomputation (WUN)

15 creditsClass Size: 30

Module manager: Alison Heppenstall
Email: a.j.heppenstall@leeds.ac.uk

Taught: 1 Apr to 31 Jul, 1 Apr to 31 Jul (Adv pre 2223) View Timetable

Year running 2017/18

Pre-requisite qualifications

Good working knowledge of a programming language.

Pre-requisites

GEOG5561M

Intro to Java Programming, WUN

Module replaces

GEOG5061M GIS and Geocomputation

This module is not approved as an Elective

Module summary

This module will introduce you to a range of different methods from the informatics field of artificial intelligence. You will be given hands-on exposure to several different techniques to see how these approaches can be used to solve geographical problems.

Objectives

Appreciation of established and new geocomputational methods
Have an overview of a range of applications and solutions to a range of spatial problems
Be able to apply these tools in different geographical applications

Learning outcomes
On completion of this module students should be able to demonstrate:
a critical understanding of the theory and application of a range of geocomputational techniques
skills in handling and analysing spatial data
skills in data visualisation and mapping in R
expertise in developing and evaluating spatial models

Skills outcomes
Pursuit of knowledge in an in-depth, ordered and motivated way
Learning and independent study

Information processing (including IT skills): literature searches

Data manipulation (including IT skills): analysis of data (especially spatial data); statistical methods; clustering techniques
Communication: report writing; e-mail/discursive skills
Management: safe and effective project planning and execution; time management.
Apply numerical and computational skills to geographical information

Syllabus

Indicative topics could include:
Intro to Geocomputation
Handling Spatial Data in R
Setting up a model
Neural Nets and Monte Carlo Simulation
Visualisation and fuzzy sketch mapping
Agent Based Modelling
Genetic Algorithm in location planning
Big Data and API’s
Web mapping in R

Teaching methods

Delivery type	Number	Length hours	Student hours
Discussion forum	10	1.00	10.00
Independent online learning hours			50.00
Private study hours			90.00
Total Contact hours			10.00
Total hours (100hr per 10 credits)			150.00

Private study

To undertake reading, research and preparation of assessed work

Opportunities for Formative Feedback

Through email communication and online discussion room and also attendance monitoring via activity on VLE

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Essay or Dissertation	2,000 word equivalent project	50.00
Project	2,000 word equivalent project	50.00
Total percentage (Assessment Coursework)		100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 10/08/2016

Disclaimer

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