https://ogma.newcastle.edu.au/vital/access/ /manager/Index en-au 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:25979 Wed 04 Sep 2019 12:23:32 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:22937 Sat 24 Mar 2018 07:16:49 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:23128 ij). Note that each row (column) of D is associated with a demand (facility) location. We say that column k dominates column l if d_ik ≤ d_il for all i ≠ k . We use the term strongly dominates in the case of strict inequalities. Observe that locating a facility at a dominated location l would provide no advantage to locating a facility at k except possibly in serving the demands of customers in location l. Further, strongly dominated columns would only be used for ‘self-serve’. Consequently, dominated column can be dropped to generate a feasible solution and the location can later be considered as a possible ‘self-service’ facility. We extend the concept of dominance somewhat further as follows. We say columns k and l dominate column j if d_ij ≤min{d_ik, d_il} for all i ≠ j . In this case there is no advantage in using location j (except for serving customers in location j) when locations k and l are used. So again we can drop the dominated column j if columns k and l are used. The term strongly is used as before. We further extend this concept of dominance as follows. We say that column k partially dominates column l if d_ik ≤ d_il for at least half or more of the entries for which i ≠ k . Similarly, we say columns k and l partially dominate column j if d_ij ≥ min{d_ik, d_il} for at least half or more of the entries for which i ≠ j. Partially dominated columns correspond to nodes which may be assigned ‘self-serve’ facilities in the original and the reduced matrix. In this paper, we developed a new greedy algorithm based on a concept known as dominance to obtain solutions for the p-median problem. This concept reduces the number of columns of a distance matrix by considering potential facilities that are near and those that are far from the population or demand. We illustrate our ideas and the algorithm with an example. We further applied the new algorithm to effectively locate additional ambulance stations in the Central and South East metropolitan areas of Perth to complement the existing ones. We also compare the performance of our new Greedy Reduction Algorithm (GRA) with the existing greedy algorithm of the p-median problem.]]> Sat 24 Mar 2018 07:16:36 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:22977 Sat 24 Mar 2018 07:11:38 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:22979 aggregate association index (or the AAI), developed by Beh (2008 and 2010) which enumerates the overall extent of association about individuals that may exist at the aggregate level when individual level data is not available. The applicability of the technique is demonstrated by using leukaemia relapse data of Cave et al. (1998). This data is presented in the form of a contingency table that cross-classifies the follow up status of leukaemia relapse by whether cancer traces were found (or not) on the basis of polymerase child reaction (PCR) – a modern method used to detect cancerous cells in the body assumed superior than conventional for that period, microscopic identification. Assuming that the joint cell frequencies of this table are not available, and that the only available information is contained in the aggregate data, we first quantify the extent of association that exists between both variables by calculating the AAI. This index shows that the likelihood of association is high. As the AAI has been developed by exploiting Pearson’s chi-squared statistics, the AAI inherently suffers from the well-known large sample size effect that can overshadow the true nature of the association shown in the aggregate data of a given table. However, in this paper we show that the impact of sample size can be isolated by generating a pseudo population of 2x2 tables under the given sample size. Therefore, the focus of this paper is to present an approach to help answer the question “is this high AAI value statistically significant or not?” by using aggregate data only. The answer to this question lies we believe, in the calculation of the p-value of the nominated index. We shall present a new method of numerically quantifying the p-value of the AAI thereby gaining new insights into the statistical significance of the association between two dichotomous variables when only aggregate level information is available. The pseudo p-value approach suggested in this paper enhances the applicability of the AAI and thus can be considered a valuable addition to the literature of aggregate data analysis.]]> Sat 24 Mar 2018 07:11:37 AEDT ]]>