ABSTRACT
This chapter looks at two simple stochastic models, the Monte Carlo and Markov chain models that serve a wide range of uses in urban analysis. Markov chain models are the simplest of the dynamic and quasi-dynamic models currently in use in urban analysis. Stochastic models incorporate some probabilistic notions in their formulation. The important characteristic of the Monte Carlo method is the decision process which fundamentally is stochastic, based on the throw of dice or a set of random numbers from random number tables or generated by the computer. The steps involved in the use of the Monte Carlo technique may be carried out manually or by the computer. The chapter aims to study the processes of urban development through the spatial distribution and growth of houses in the town. Consequently the Monte Carlo technique could be suitable as a way of discerning the variables of the urban development process.
