• Methods
  • Collaborative Approaches
  • e-science & Data Management
  • Epistemology
  • Ethnographic Methods
  • Experiments
  • Mixed Methods
  • Qualitative Interviewing Analysis
  • Quality Assessment
  • Survey Related
  • Visual & Sound

• UoM methods-related events
  • all events at Manchester
  • methods courses

• Next Events:
  • Symposium: The future of the multi-ethnic city (29 May)
  • Designing Early Phase Clinical Trials (10 June)
  • Manchester Methods Summer School (17 June)
  • Researching Work and Family: Methods across disciplines (19 June)
  • Interpretative Phenomenological Analysis (02 July)

• Next Methods Courses:
  • 10 Jun: Designing Early Phase Clinical Trials
  • 17 Jun: Manchester Methods Summer School

Spatial Data

Gail Millin-Chalabi, Mimas - Landmap Service

Spatial data can be referred to as geographic data or geospatial data. Spatial data provides the information that identifies the location of features and boundaries on Earth. Spatial data can be processed and analysed using Geographical Information Systems (GIS) or Image Processing packages. There are different types of spatial data which can be split into two categories: • Feature Data (vector data model) = entity of the real world e.g. a road, a tree or a building these can be represented as a point, line or polygon in space • Coverage Data (raster data model) = mapping of continuous data in space expressed as a range of values e.g. a satellite image, an aerial photograph, a Digital Surface Model (DSM) or Digital Terrain Model (DTM), text file with daily precipitation values. Coverage data can be represented as a grid or triangulated irregular network

Spatial data is analysed to create meaningful information for a particular application or purpose. The communication of information from the spatial analysis is often represented as a map produced with a specific audience in mind.

Accessing Spatial Data

It is now common place for spatial data to be provided through the web. There are standards for delivery of spatial data on the web provided by the Open Geospatial Consortium (OGC). These standards include:

• Web Map Services (WMS) – accessing a portrayal of spatial data on the web e.g. map
• Web Coverage Services (WCS) – downloading coverage data
• Web Feature Services (WFS) – downloading feature data
There is a range of academic services that provide spatial data and OGC services e.g.
• Landmap hosted at Mimas, University of Manchester
• Digimap hosted at EDINA, University of Edinburgh
• NERC Earth Observation Data Acquisition and Analysis Service (NEODAS), Airborne Research and Survey Facility (ARSF), Plymouth Marine Laboratory (PML)
• Global Land Cover Facility (GLCF) hosted at the University of Maryland

Manchester Experts

Dr Sarah Lindley, Geography

Ms Julia McMorrow, Geography

Dr Angela Harris

Dr Kamie Kitmitto, Mimas

Mr Richard Kingston, Planning

Projects using Spatial Data

Millin-Chalabi, G.M., McMorrow, J.M. and Agnew, C.A. (2011), Using ASAR & ERS-2 to Detect a Moorland Fire Scar in the Peak District National Park. In 5th Int Conference on Wildland Fire, 09 May 2011 - 13 May 2011. eScholar ID:130108

This research project is in collaboration with Mimas and the School of Environment and Development (SED). The focus is to develop a spatial and temporal model of wildfire risk and impact assessment using both optical and radar remotely sensed data for the Peak District National Park (PDNP). Key study areas include Bleaklow and Kinder which experienced large fires on 18 April 2003.

McMorrow, J., Lindley, S., Aylen, J., Cavan, G., Albertson, K., Boys, D. "Moorland wildfire risk, visitors and climate change: patterns, prevention and policy." In Drivers of Change in Upland Environments, ed. A. Bonn, T. Allott, K. Hubacek & J. Stewart, 404-431. Abingdon: Routledge, 2009. eScholarID:3b3098

This text resulted by a collaboration of expertise in remote sensing, GIS, planning and economics to produce a spatial data model which identified the key factors affecting wildfire risk in the Peak District National Park.

Key References

Lillesand, T., Kiefer, R.W. and Chipman, J. (2008) Remote Sensing and Image Interpretation, 6th edition. Wiley: London [ISBN 978-0-470-05245-7]

Longley, P. Goodchild, M and Rhind, D. (2001) Geographic Information Systems and Science John Wiley and Sons : Chichester [ISBN: 0471892750]

Heywood I, Cornelius S and Caver S. An Introduction to Geographical Information Systems Second Edition Prentice Hall [ISBN 0130611980]

Online Resources

Geospatial Analysis - A comprehensive guide by Dr Michael de Smith and Prof Paul Longley, University College London, and Prof Mike Goodchild, UC Santa Barbara

Open Geospatial Consortium

Landmap Service

Digimap

NEODAAS

GLCF