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Understanding Local Modelling
Wed 9 November 2016, 13:30 – 16:30 GMT
The Urban Big Data Centre invite you to participate in a free training seminar on the concepts and processes for local modelling of spatial data. 'Understanding Local Modelling' is provided via our SASNet Fellowship Programme and will consist of two presentations from Stewart Fotheringham, Professor of Computational Spatial Science in the School of Geographical Sciences and Urban Planning at Arizona State University.
The afternoon seminar will provide time for discussion and networking around featured talks by Prof. Fotheringham:
'Local Modelling' - A short course that introduces the importance of local modelling and when to choose such models over the more common global modelling framework. Examples will be given of types of local models with reference to the well-known Geographically Weighted Regression (GWR) framework that Prof. Fotheringham co-developed.
'My model's bigger than yours' - A lecture demonstrating two modelling types that can be used in the analysis of Big Data (see abstract at bottom of page).
This free event will be held at the Hilton Glasgow Grosvenor in their spacious Kelvin Suite meeting room and will be relevant to anyone with an interest in big data and spatial data processes. It will be methods-focused and a knowledge of regression and hypothesis testing is recommended.
Suggested background reading:
A. Stewart Fotheringham, Chris Brunsdon and Martin Charlton 2002 Geographically Weighted Regression: The Analysis of Spatially Varying Relationships xii and 269 pages, Wiley: Chichester. ISBN 0-471-49616-2 (hardback).
A. Stewart Fotheringham 2009 “Geographically Weighted Regression” Chapter 13 pp 243-253 in The SAGE Handbook of Spatial Analysis eds. A. Stewart Fotheringham and P.A. Rogerson, Sage: London
***Please note: Registration is requested for catering purposes. Refreshments and snacks will be provided.
If you have any questions about the workshop, please contact: Keith.Maynard@glasgow.ac.uk
Presenter Short Bio
Stewart Fotheringham is Professor of Computational Spatial Science in the School of Geographical Sciences and Urban Planning at ASU. He is also a Senior Scientist in the Ann Wrigley Sustainability Institute. He established both the Centre for GeoInformatics at the University of St Andrews in Scotland and the National Centre for Geocomputation in Ireland. He is a member of the US National Academy of Sciences and the Academica Europeae and a Fellow of the UK’s Academy of Social Sciences and serves on the Executive Committee of the Transportation Research Board and on the Evaluation panel for the European Research Council’s Consolidator Grants scheme. He was awarded the first Science Foundation Ireland Research Professorship in 2004 and has been awarded over $15m in funding, published 12 books and almost 200 research papers and book chapters. His research interests are in the analysis of spatial data sets using statistical, mathematical and computational methods. He is well-known in the fields of spatial interaction modelling and local statistical analysis; the latter as one of the developers of Geographically Weighted Regression. He has substantive interests in health data, crime patterns, retailing and migration.
Abstract: My Model's Bigger Than Yours
We seem to be surrounded by evidence suggesting we are in a new era in terms of data analysis – that of BIG data. Geography is in the midst of this phenomenon as many of the large data sets that have been produced in recent years have a spatial component. However, another current trend, which is equally important and equally in the domain of geography, has received much less attention: this is the development of BIG models. In this seminar, I demonstrate two examples of BIG models: one consists of an extension of the well-known Geographically Weighted Regression (GWR) framework; the other is an extension of the traditional ‘family’ of spatial interaction models.