Elimination of Source A and B Errors in p-Median Location Problems

The p-median problem is a powerful tool in analyzing facility location options when the goal of the location scheme is to minimize the average distance that demand must traverse to reach its nearest facility. It may be used to determine the number of facilities to site, as well as the actual facility locations. Demand data are frequently aggregated in p-median location problems to reduce the computational complexity of the problem. Demand data aggregation, however, results in the loss of locational information. This loss may lead to suboptimal facility location configurations (optimality errors) and inaccurate measures of the resulting travel distances (cost errors). Hillsman and Rhoda (1978) have identified three error components: Source A, B, and C errors, which may result from demand data aggregation. In this article, a method to measure weighted travel distances in p-median problems which eliminates Source A and B errors is proposed. Test problem results indicate that the proposed measurement scheme yields solutions with lower optimality and cost errors than does the traditional distance measurement scheme.     

Disaggregate Migration Behavior and the Volume of Interregional Migration

Nested multinomial logit models are used to investigate migration behavior during the 1971–74 period for a large sample of the population of Ecuador. The nested form of the model makes it possible to test hypotheses about the importance of destination characteristics in conditioning the odds for out-migration. Our empirical results indicate that the odds for migration from each origin are conditioned by the expected utilities of the available set of destinations, as well as characteristics of the origins and the personal characteristics of potential migrants. The association between destination characteristics and the frequency of out-migration allows the total volumes of migration to be adjusted to interregional differences in place-specific utilities.     

What can supraspecies richness tell us?

Biogeographers, ecologists, palaeontologists, and conservation managers often deal with checklists in which not all individuals have been identified to a species level, or the accuracy of species identification is questionable. Is it possible and credible to investigate species richness based on such checklists? Studies on macrofauna in the Far Eastern seas, eastern Arctic seas, and adjacent waters of the Pacific and Arctic Oceans suggest that in different habitats and for diverse taxa, species, and higher taxa richness strongly correlate with each other and increase with an expansion in the study area and sample size according to the species–area law. Such an increase is higher in the bottom zone than in the pelagic. Species and higher taxa richness also show a decrease from lower to higher latitudes, which is in line with the Humboldt–Wallace’s law. According to Willis’ law and self-similarity in the organisation of taxonomic levels, species richness can be assessed based on the genus, family, and order richness. In other words, supraspecies richness itself can tell us the same as species richness and therefore certain global patterns revealed at the species level may also be revealed at the supraspecies level. Such a concordance in general trends among richness parameters at different taxonomic levels in practice implies that species richness can be studied based on lists that lack species identifications or lists with doubtful species identification. We suggest bolder use of supraspecies richness in science and practice, discussing the disadvantages and advantages of this approach.