Last summer I worked with my colleague Dr Andy Newing and a Master’s dissertation student, Charlotte Sturley, who has just won the Royal Geographical Society GIS group prize for best dissertation. Her work focused on classifying consumer data into several groups of behaviour and then building a prototype ABM using NetLogo.
This work posed several challenges: how do we translated observed behaviour into rules that an agent can operate satisfactorily? How should we represent time to mimic temporal as well as spatial patterns in different types of consumer behaviour? Which of the many processes involved within this system should we include? Charlotte’s dissertation (and upcoming paper) addresses these issues in-depth, but in brief the data was analysed in depth (using classification methods and spatial analysis tools) to identify different groups of individuals and their behaviour. We built a highly abstract representation of Leeds which allowed us to match behaviour to the corresponding geodemographic classifications and add in real store distributions. These can be seen below with the red blobs representing different types of stores and the coloured squares representing different areas of Leeds and the different consumer types that reside there.
This is, of course, a highly abstract representation of what is a very complex system and clearly a significant amount of development to the model would be required to fully replicate the real system. However, one of the research questions that we were interested in addressing was whether ABM could replicate the pull of consumers to a store based on distance and attractiveness i.e. could we embed this aspect of a spatial interaction model into an ABM? The answer was yes, and this represents a potentially important shift in the methods by which retailers simulate the likely consequences of different policies on consumer behaviour.
More details on this work can be found in Charlotte’s upcoming paper. A copy of the model code can be downloaded here.