ABSTRACT
This chapter provides a brief introduction to the Monte Carlo simulation technique. The Monte Carlo method generates suitable random numbers of parameters or inputs to explore the behaviour of a complex system or process. In a Monte Carlo simulation, each set of random samples is called an iteration and is recorded. The Monte Carlo simulation technique is widely used in spatial analysis. A popular application of the Monte Carlo simulation in spatial analysis is to test statistical hypotheses using randomization tests. P. Clifford et al. used a Monte Carlo simulation technique to assess statistical tests for the correlation coefficient or the covariance between two spatial processes in a spatial autocorrelation. The proposed simulation approaches disaggregate the reported zonal commuting trips into individual trips and hence permits a more accurate estimation of commute distances by mitigating the aggregation errors and zonal effect.
