Using Cellular Automata for Integrated Modelling of Socio-environmental Systems

1) Guy Engelen , 2) Roger White , 1) Inge Uljee and 1) Paul Drazan
1) RIKS Research Institute for Knowledge Systems; P.O. Box 463; 6200 AL Maastricht; The Netherlands
2) Memorial University of Newfoundland; St. John’s; NF; Canada A1B 3X9

Abstract
Cellular automata provide the key to a dynamic modelling and simulation framework that integrates socio-
economic with environmental models, and that operates at both micro and macro geographical scales. An
application to the problem of forecasting the effect of climate change on a small island state suggests that
such modelling techniques could help planners and policy makers design more effective policies --policies
better tuned both to specific local needs and to overall socio-economic and environmental constraints.

Introduction
Town and country planners face the difficult task of dealing with a world that is complex, interconnected,
and ever-changing. Coastal zone management, urban land-use planning, and the design of policies for
sustainable economic development all pose the problem of dealing with systems in which natural and
human factors are thoroughly intertwined. There is growing scientific evidence that a purely macroscopic
approach to these problems does not suffice, because spatial and organizational details are important in
understanding the dynamics of such systems (Allen and Lesser, 1991; Kauffman, 1993; Langton, 1992;
Nicolis et al. 1989).
At the descriptive level, the need for spatial detail is attained in Geographical Information Systems. But,
in order to put forward effective measures for changing --or maintaining-- the organization of socio-
what is where why it is
economic and environmental systems, it is necessary not only to know but also
there
. These systems must be understood and managed as coherent dynamic entities, so that system
integrity is maintained. We present here a dynamic modelling framework and encompassing decision
support shell that is capable of integrating socio-economic and environmental factors at a variety of scales,
while representing spatial dynamics with a high level of geographical detail. This modelling framework is
quite general in terms of the situations to which it can be usefully applied. But we will present it here in
the form of an example --an application concerning the impact of climate change on a small island state.

An example: Exploring the Impact of Climate Change on a Small Island.
macro-scale
Shifting climate conditions, expressed at the in terms of changes in temperature,
precipitation, and storm frequency, are likely to affect productivity levels, demand patterns, and exports
and imports, and will probably cause migration of people and their activities as well (see e.g. Alm et al.,
micro-scale
1993). But all of these effects are actually expressed, on the ground, as phenomena. For
example, an increase in the total export demand for a particular agricultural product will normally mean
that more land will be required. But the consequences will be very different depending on whether the
land is found by converting existing agricultural land or by clearing forested land, especially if the latter is
easily eroded or is itself ecologically significant. Furthermore, changes in productivity that may occur as
other activities are displaced onto more marginal land, or as erosion causes loss of fertility, will in turn
have repercussions on the macro-level economics. In other words, the spatial details of land use are
important in understanding the impact of macro-level changes.
No one model is capable of capturing the whole range of these phenomena, from those operating on a
world scale down to those that threaten strips of beach or affect individual fields. For example, spatial
interaction based models, consisting of sets of linked dynamic equations, are useful for representing
spatial and temporal dynamics at regional scales (White, 1977; Engelen and Allen, 1986; Pumain et al.,
1989), but become computationally impractical when much spatial detail is required (White and Engelen,
1993). On the other hand, models capable of dealing with extreme geographical detail, such as those
available in Geographical Information Systems, lack the dynamics required to represent the processes
operating in the system (Brimicombe, 1993). One solution to the problem is to make use of a modelling
framework consisting of two linked components --one for macro-level processes and another for those
operating at the micro-level. Both components exchange results continuously during the simulation and
get the data relevant for their level of detail from the same geographical database, ideally a GIS (Figure
1).
macro-level
At the , the modelling framework integrates several component sub-models, representing the
natural, social, and economic sub-systems. These are all linked to each other in a network of mutual,
reciprocal, influence (Figure 1, top). The macro level allows for the use of regionalised representations
and for different types of mathematical formulations, thus permitting a more or less detailed modelling of
various aspects of the sub-systems as required for specific applications. For the case of the small
prototypical Caribbean Island, the macro-level is modelled as a single point in interaction with the world
outside.