A study on cellular automata based on relational databases and spatio-temporal simulations of culture diffusion

LUO Ping L2, DU Qing-yun 1, HE Su-fang 2, LI Sen 2, MICHAEL Gallaghel 3, NIU Hui-en 3
(1. School of Resources and Environmental Sciences, Wuhan University, Wuhan 430079, P. R. China; 2. Department of Tourism & Geography, Foshan University, Foshan 528000, P. R. China; 3. Shenzhen Graduate School of Town Planning, Shenzhen 518031, P. IL China)

ABSTRACT: This paper presents a development of the extended Cellular Automata (CA), based on relational databases
(RDB), to model dynamic interactions among spatial objects. The integration of Geographical Information System (GIS) and CA has the great advantage of simulating geographical processes. But standard CA has some restrictions in cellular
shape and neighbourhood and neighbour rules, which restrict the CA's ability to simulate complex, real world environments. This paper discusses a cell's spatial relation based on the spatial objeet's geometrical and non-geometrical characteris-
tics, and extends the cell' s neighbour definition, and considers that the cell' s neighbour lies in the forms of not only spatial adjacency but also attribute correlation. This paper then puts forward that spatial relations between two different cells can be divided into three types, ineluding spatial adjacency, neighbourhood and complicated separation. Based on traditional ideas, it is impossible to settle CA's restrictions completely. RDB-based CA is an academic experiment, in which
some fields are designed to describe the essential information needed to define and select a cell's neighbour.

The culture innovation diffusion system has multiple forms of space diffusion and inherited characteristics that the RDB-based CA is
capable of simulating more effectively. Finally this paper details a successful case study on the diffusion of fashion wear
trends. Compared to the original CA, the RDB-based CA is a more natural and efficient representation of human knowl-
edge over space, and is an effective tool in simulating complex systems that have multiple forms of spatial diffusion.
KEY WORDS: spatial relationship; cellular automata; relational database; culture diffusion; spatio-temporal simula-
tion

1 INTRODUCTION
The functionality of spatio-temporal analysis and modelling is a drive for GIS to further applications in various applied fields and digital earth plans. However, current commercial GIS lacks those capabilities for
spatio-temporal distribution, prediction, and simulation of spatio-temporal processes, and especially do not have the ability to simulate complex dynamic interactions among economic, human, cultural and ecological pro-
cesses(ZHANG and CUI, 2000). Thus, there is an urgent requirement for traditional GIS to provide not only spatio-temporal data management services but also tools for scenario generation. The basic thought to study

complex spatial system is to apply the theory of complex systems, combining the essential rules of geography, selecting proper study methods, and designing proper models. As a result, more effective and powerful methods for studying complex systems have begun to be
applied in order to understand geographic systems; for example, system dynamics, neural networks, artificial intelligence, etc. (ZHOU et al., 1999). The integration of GIS and dynamic models has become an important area of research on GIS because it greatly improves the ability
of GIS to support spatial decision-making and simulate geographical processes. The undamental problem, however, is that the conceptual representation of space and
time in dynamic modelling and in GIS are not com