Spatial Interaction Modelling (fully booked)

Date:

21/03/2017 - 22/03/2017

Organised by:

The University of Manchester

Presenter:

Dr Andy Newing & Dr Adam Dennett

Level:

Intermediate (some prior knowledge)

Contact:

Claire Spencer, 0161 275 4579, claire.spencer@manchester.ac.uk

Map:

View in Google Maps (M13 9PL)

Venue:

Cathie Marsh Institute
Basement Lab
Humanities Bridgeford street
University of Manchester

Description:

This two day course is designed to equip participants with the knowledge and skills to build, calibrate and apply powerful spatial interaction models (SIMs). SIMs are used to estimate flows between origins and destinations and have a broad range of applications within geography, planning, transportation, social science and the commercial sector. We assume no prior experience of working with SIMs (or gravity models as they are also known) and focus on both the theoretical and technical components of model building using examples which are intuitively straightforward and familiar to participants (shopping behaviours and migration).

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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.

Objectives:

- 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.

Prerequisites:

- 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 tutorialhttp://tryr.codeschool.com/ - or any of the resources recommended onhttps://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.