In detecting the spatial patterns of the points geocoded from the addresses, point pattern analytical methods are used. Quadrat Analysis is one of them. This method evaluates a case distribution by examining how its density changes over space. The density measured by Quadrat Analysis is then compared with the density of a theoretically constructed random pattern to see if the case distribution in question is more clustered or more dispersed than the random pattern. Another one is Nearest Neighbor Statistic, which is derived from the average distance between cases and each of their nearest neighbors and captures information on cases between quadrats. Using ordered neighbor statistic can evaluate the pattern at different spatial scales. A case pattern is said to be more clustered if its observed average distance between nearest neighbors is found to be less than that of a random pattern. Ripley's K statistic is an extension of the ordered neighbor statistics, which can be used to depict the randomness of a case distribution over different spatial scales and capture the characteristic of local variations.