GEOGG125 Principles of Spatial Analysis
CORE: GEOGG125 - PRINCIPLES OF SPATIAL ANALYSIS
Term 1 (2013)
Paul Longley, Mike de Smith and postgraduate demonstrators
This course will provide an introduction to spatial analysis including: problem design, measurement, representation and statistics; spatial processes; point data - distribution, randomness & clustering, densities, ESDA and prediction; lines and linear data; areas and spatial autocorrelation; interpolation, prediction and geostatistics.
The module aims to:
- equip students with an understanding of the principles underlying the conception, representation/measurement and analysis of spatial phenomena.
- present an overview of the core organising concepts and techniques of Geographic Information Systems, and the software and analysis systems that are integral to their effective deployment in advanced spatial analysis.
- provide an introduction to the principles underlying the analysis of spatial data in general and spatial statistics in particular
- enable students to use GIS for generating and visualising summary statistics
- examine, analyse and simulate a range of spatial patterns and processes
- facilitate a good understanding of the nature of spatial fields and their structure
- review the many different sources of uncertainty in spatial data and spatial processing and suggest to address such issues in analysis and research.
The sessions in this module will equip students with an understanding of the principles underlying the analysis of spatial data. By the end of the unit, the student should:
- understand how Geographic Information Systems have developed and how GIS is core to Geographic Information Science
- grasp how GIS has been successfully deployed in a range of real world applications
- understand the fundamental representational models of GIS and how these are deployed using different data structures
- understand in conceptual terms what is special about spatial data
- grasp the rudiments of good cartographic design and geocisualisation
- understand the sources and operation of uncertainties in GIS
- have an good understanding of the principles underlying the analysis of spatial data in general and spatial statistics in particular
- be able to use GIS for generating and visualising summary statistics
- be able to examine, analyse and simulate a range of spatial patterns and processes
- have a good understanding of the nature of spatial fields and their structure
- appreciate the many different sources of uncertainty in spatial data and spatial processing and how to address such issues in analysis and research.
Format and Assessment:
The first six sessions will be assessed using online quizzes based on the ‘Turning Data into Information using ArcGIS 10.0’ modules.
Although primarily a lecture-based course, there will be a number of different learning approaches used, including in-Lecture practicals, modules from the ESRI Virtual Campus (co-authored by lecturing staff) and Microprojects (brief presentations by students). Students will be expected to access library and web resources and to download and run free software from the departmental cluster and the Internet. Lecture notes and a range of electronic documentation, data and software will be made available from the course homepage/Moodle.
Student support will be available in timetabled practical sessions and individual support by the lecturing staff in term weekly office hours.