Postgraduate Course: Further Spatial Analysis (PGGE11085)
|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||This course explores further methods for the analysis of geographical data. Building on ideas from the introductory module, this course begins by examining fuzzy and probabilistic models for representing uncertainty in geographical data. Various methods for interpolating point data to create surfaces are then considered, including kriging. Methods for the detection of clustering within point data sets are studied, with particular reference to the problem of finding ¿hotpots¿. Other methods explored include geographically weighted regression and spatial interaction modelling. Methods are illustrated through real-world use cases. In most weeks the lecture material is supported by a practical. Students submit coursework from two assessed practicals.
Lecture: Fuzzy and probabilistic models of uncertainty
Practical: Map Comparison Toolkit
Lecture: Spatial Interpolation
Practical: Interpolation using Geostatistical Analyst
Lecture: Geographically Weighted Regression
Practical: GWR in ArcGIS
Lecture: Clustering in geographical data
Practical: Hotspot mapping using Crimestat
Spatial Interaction Modelling
Entry Requirements (not applicable to Visiting Students)
|| It is RECOMMENDED that students have passed
Introduction To Spatial Analysis (PGGE11091)
||Other requirements|| None
Information for Visiting Students
|Pre-requisites||It is RECOMMENDED that students have passed Introduction to Spatial Analysis (PGGE11091)
|High Demand Course?
Course Delivery Information
|Academic year 2016/17, Available to all students (SV1)
||Block 4 (Sem 2)
|Learning and Teaching activities (Further Info)
Lecture Hours 12,
Seminar/Tutorial Hours 12,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Interpolation Practical (50%) deadline
Clustering Practical (50%) deadline
|No Exam Information
On completion of this course, the student will be able to:
- understand the concepts and principles underlying the methods of spatial analysis introduced
- be aware of the typical applications for these methods in the real-world and to critically reflect on their appropriateness and limitations
- have practical experience of applying this knowledge by carrying out some of these methods using GIS or related software
- plan and write up practical assignments within the specified parameters and to a professional standard
- have demonstrated autonomy and time-management by completing the analysis and writing up of the practical assignments, and reading beyond the lectures to consolidate their knowledge and understanding
|Recommended basic or preparatory reading:|
Bailey, T.C. and Gatrell, A.C. (1995). Interactive spatial data analysis.
Birkin, M., Clarke, G., Clarke, M. and Wilson, A. (1995) Intelligent GIS: Location Decisions and Strategic Planning. Geoinformation International, Cambridge.
Bonham-Carter, G. (1994) Geographic information systems for geoscientists: modelling with GIS. Pergamon, Oxford, 398pp.
Clarke, G and Stillwell, (2004) Applied GIS and Spatial Analysis. Wiley.
Burrough, P.A and McDonnell, R.A (1998) Principles of geographical information systems. Clarendon Press, Oxford. For geostatistics, errors and fuzzy sets, read chapters 5, 6 and 8-11.
Chainey, S and Radcliffe, J (2000) GIS and Crime Mapping. Wiley.
Crimestat III User Manual http://www.icpsr.umich.edu/CrimeStat/
Fischer, M. Scholten, H.J and Unwin, D. (1996) Spatial analytical perspectives on GIS. Taylor and Francis, London.
Fotheringham, S. Brunsdon, C and Charlton, M (2000) Quantitative Geography: perspectives on spatial data analysis. Sage.
Fotheringham, S. Brunsdon, C and Charlton, M (2002) Geographically weighted regression: the analysis of spatially varying relationships. Wiley.
Haining, R. (2003) Spatial Data Analysis: Theory and Practice. CUP.
Lam, N.S. (1983) Spatial interpolation methods: a review. American Cartographer 10: 129-49.
Map Comparison Toolkit 3.0 (2006) http://www.riks.nl/mck/
Longley, P.A. and Batty, M. (eds.) (2003) Advanced Spatial Analysis - The CASA book of GIS. ESRI Press, Redlands. California.
Longley, P.A., Goodchild, M. F., Maguire, D.J. and Rhind, D. W. (eds.) (1999) Geographical Information Systems (Vol 1: Principles and Techniques, Vol 2 Management and Applications). Wiley.
Longley, P.A., Goodchild, M. F., Maguire, D.J. and Rhind, D. W. (eds.) (2005) Geographical Information Systems: Principles, Techniques, Management and Application (abridged edition). Wiley.
O'Sullivan, D. and D. J. Unwin (2003 or 2010) Geographic Information Analysis. Wiley, New York.
Openshaw, S. (1991) Developing appropriate spatial analysis methods for GIS. In Maguire, D. J., Goodchild, M. F. and Rhind, D. W. (Eds.) GIS: Principles and Applications, Vol. 1, Chapter 25, pp. 389-402. Longman.
Visser H and T. de Nijs, 2006. The Map Comparison Kit. Environmental Modeling & Software 21, 346-358.
Webster, R and Oliver, M.A (1990) Statistical methods in soil and land resources survey. Oxford, OUP. 316p.
|Course organiser||Dr Neil Stuart
Tel: (0131 6)50 2549
|Course secretary||Mrs Karolina Galera
Tel: (0131 6)50 2572
© Copyright 2016 The University of Edinburgh - 3 February 2017 4:54 am