ABSTRACT
Chapter 12 demonstrates the value of Monte Carlo technique in spatial analysis. Monte Carlo is a numerical analysis method that uses repeated random sampling to obtain the distribution of an unknown probabilistic entity. Many spatial analysis tasks have traditionally relied on data aggregated in various area units, such as census or administrative units, and are subject to criticisms, including MAUP, unfairness of sampling, and also inaccuracy or uncertainty in distance measure by using areal centroids. One case study uses the Monte Carlo method to simulate individual resident locations by using the census and land use inventory data and then aggregates population back to area units of any scale in any shape so that the scale effect and the zonal effect can be examined explicitly. Another case study demonstrates the value of applying the Monte Carlo technique in simulating urban traffic flows. Most of the researchers attribute the discrepancy in measuring the extent of wasteful commuting to scale effect and believe that a sharper resolution (smaller areal unit) helps mitigate the problem. Appendix 11 simulates individual commuters to examine the scale effect on measuring wasteful commuting.
