Spatial Interaction Modelling (few places remaining)
|The University of Manchester|
Dr Andy Newing ( University of Leeds) & Dr Adam Dennett (University College London)
28/03/2019 - 29/03/2019
UCL, 25 Gordon Street, London
View in Google Maps (WC1H 0AY)
Claire Spencer, 0161 275 4579, firstname.lastname@example.org
Spatial Interaction Models (SIMs) are statistical models used to predict origin-destination flows. They are widely applied within geography, planning, transportation and the social sciences to predict interactions or flows related to commuting, migration, access to services etc. They are also widely applied across the commercial sector for example to model flows of consumers between home and retail centres with broad applications in commercial decision making and policy evaluation.
This hands on course is designed to equip participants with the skills to build, calibrate and apply spatial interaction models suitable for addressing a broad range of research questions. We dont assume any prior knowledge of spatial interaction modelling and begin by building a SIM for modelling consumer flows between home and retail stores. This intuitively straightforward example is used to understand the model structure, key theoretical assumptions and the model building and calibration process. We work with this model to understand model disaggregation and we also use this example to highlight one of the major commercial applications of the SIM.
The second part of the course will explore how we can use SIMs to explain and predict flows of humans such as daily commuting flows or less frequent migration flows. We will explore how to build and calibrate a production-attraction constrained SIM using the powerful open source software package R. Techniques for fitting a SIM to existing flow data and using the model to estimate missing data or predict future flows will be explored. We will also be able to discuss your own potential applications of the SIM.
- To introduce participants to the production-constrained and production-attraction constrained SIMs and their applications within geography, social sciences, planning and the commercial sector.
- To enable participants to build and calibrate SIMs using Microsoft Excel and R, particularly within the application areas of modelling retail or migration flows.
- To equip participants with the skills to apply their models to predict flows under various what if? scenarios and to estimate missing data.
- To encourage participants to evaluate their modelling framework, to assess model performance and to identify opportunities for model enhancement.
- Participants should have a good working knowledge of Microsoft Excel.
- No prior knowledge of R is required as everything will be taught on the course, however some familiarity will be advantageous if you have no prior knowledge of programming at all. For absolute beginners, resources such as code schools R tutorial http://tryr.codeschool.com/ - or any of the resources recommended on https://www.rstudio.com/online-learning/#R will be good for gaining familiarity before the course.
- It would be helpful if participants had some experience in using GIS (e.g. ArcGIS, QGIS or MapInfo) but this is not essential. We will use GIS to map modelled flows from our Excel model but participants will not be disadvantaged if they are not a GIS user.
Day One: 11am - 5pm
Day Two: 9am - 3pm
- Birkin, M. and Clarke, G. P. 1991. Spatial interaction in geography. Geography Review,4(5), pp.16-21. [A copy will be provided to all participants by email prior to the course].
- Wilson, A. G. 2010. Entropy in urban and regional modelling: retrospect and prospect. Geographical Analysis, 42(4), pp.364-394.
- Dennett, A. 2012. Working Paper Series Paper 181 Estimating flows between geographical locations: get me started in spatial interaction modelling. London: Centre for Advanced Spatial Analysis, University College London.
Additional reading material will be recommended during the course.
Intermediate (some prior knowledge)
Website and registration
Quantitative Data Handling and Data Analysis
Related publications and presentations