Location Models

Non-parametric smoothed location model is another powerful approach which can be used to discriminate the objects that contain both continuous and binary variables. However, the smoothed location model is infeasible in estimating parameters when a large number of binary variables involved in the study. To handle this issue, the combination of two variable extraction techniques namely principal component analysis (PCA) and multiple correspondence analysis (MCA) are carried out before the construction of the smoothed location model. In fact, there are four types of MCA but only Indicator MCA and joint correspondence analysis (JCA) will be discussed in this article. Thus, the performance of the smoothed location model together with combination of PCA and two types of MCA, i.e. Indicator MCA and JCA, will be compared and evaluated. The overall results from simulation study show that the smoothed location model performed better when the binary extraction is done by JCA rather than the Indicator MCA in terms of misclassification rate and computational efficiency.

Reasonable and useful information can be analysed and presented through a Geographical Information System (GIS) for any desirable spatial application. This paper considers the problem of opening new facilities in the presence of competing firms. We build a Spatial Decision Support System (SDSS) that link together selected information from the database of a GIS with a mathematical decision model which is based on the Maximum Capture model (MaxCap) that support the process of selecting the optimal locations for the facilities. The problem of opening petrol stations is considered as a case study to illustrate the design of the DSS and how quantitative and qualitative decisions factors can be taken into consideration to illustrate customer’s behaviour while patronizing a given outlet.

Location modeling, both inductive and deductive, is widely used in archaeology to predict or investigate the spatial distribution of sites. The commonality among these approaches is their consideration of only spatial effects of the first order (i.e., the interaction of the locations with the site characteristics). Second-order effects (i.e., the interaction of locations with each other) are rarely considered. We introduce a deductive approach to investigating such second-order effects using linguistic hypotheses about settling behavior in the Final Palaeolithic. A Poisson process was used to simulate a point distribution using expert knowledge of two distinct hunter-gatherer groups, namely, reindeer hunters and elk hunters. The modeled points and point densities were compared with the actual finds. The G-, F-, and K-function, which allow for the identification of second-order effects of varying intensity for different periods, were applied. The results reveal differences between the two investigated groups, with the reindeer hunters showing location-related interaction patterns, indicating a spatial memory of the preferred locations over an extended period of time. Overall, this paper shows that second-order effects occur in the geographical modeling of archaeological finds and should be taken into account by using approaches such as the one presented in this paper.