Cities play a critical role in our lives, providing habitats for more than half the world’s population in 2008 and this proportion is predicted to increase to over 75 percent by the year 2100. With this growth comes pressure on housing both in terms of supply and location. However, understanding such systems is extremely complex as they are composed of many parts, with many dynamically changing parameters and large numbers of discrete actors interacting within space. The heterogeneous nature of cities makes it difficult to generalise localised problems from that of city-wide problems. To understand urban problems such as sprawl, congestion and segregation, has recently lead researchers to focus on a bottom-up approach to urban systems, specifically researching the reasoning on which individual decisions are made. One such approach is agent-based modelling (ABM) which allows one to simulate the individual actions of diverse agents, measuring the resulting system behaviour and outcomes over time. Furthermore, such models allow us to test different ideas and theories of urban change in the safe environment of the computer, thus allowing scientists to understand urban phenomena through analysis and experimentation, a traditional goal of science.
With the rise of ABM, there has been a growing interest in developing integrated geographical information systems (GIS) and ABM applications. The linkage between the two allows agent-based modellers to have agents related to actual geographic locations and for GIS users, it provides the ability to model the emergence of phenomena through the individual interaction of features in a GIS over space and time.
This paper will explore how agent-based models coupled loosely with GIS can be created to explore housing issues such as segregation, and how residential and employee groups interact to form urban spatial structures characteristic of large cities through a series of illustrated examples dimensioned upon
. Furthermore the paper will discuss the potential of combing ABM with fine resolution data (e.g. Ordnance Survey MasterMap TOIDS to represent individual buildings; MasterMap’s Address layer to populate these building with a number of units, assigning individual agents to each of these units and sales data from the Land Registry to give the units a price) to create models exploring the small scale dynamics of the London housing market from the individual perspective. ABM is inherently suited to such a study as it allows the representation of an heterogeneous population with individual agents having different behaviours and characteristics. For example, the agent’s decisions/behaviour of where to locate is affected by numerous factors. Location is a trade-off between such things as price of dwelling, type of residence and its location, both in terms of neighbourhood and in relation to place of work, all of which vary depending on age, sex, marital status and income. To conclude the paper will present a series of problems and challenges with this approach and ABM in general. London