Mean airstreams of Australia

Streamline analyses of mean monthly resultant surface winds over Australia are presented. Mean confluences represent either cyclone trajectories, or modal fronts. These analyses suggest that Australia's annual airstream climate is of two regimes: that of September to March inclusive, and April to August inclusive. The winter half-year is exemplified by a continental airstream source in southern Australia which diverges over virtually the entire continent. It continues into September and October, although dominating only the southern third to half of Australia, and is absent from November to March. During the summer half-year a new pattern emerges, with three maritime air stream sources represented over the continent: first, air from the Southern Ocean penetrates the south coast reaching its furthest northward extension from December to March inclusive; a second source originating over the Pacific Ocean dominates eastern and northern Australia; and a monsoon source originating over the Indian Ocean which flows overland affecting northern Western Australia. Two modal confluences are noted, one over northwestern Australia from November-February separating monsoon flow from that emanating from the east, and another extending from the west central coast east-southeastward to the southeast coast from November-March separating Southern Ocean air from that originating over the Pacific Ocean.     

The determinants of county growth

The determinants of population and employment growth were explored from a broader interregional (as opposed to intraregional) perspective. Data for the 1970s, at the county level of disaggregation, were used to analyze the effects of economic, demographic, and climatic variables on population and employment growth in a simultaneous equation framework. The use of data from the more than 3000 US counties provides a considerably larger testing ground than those used in previous research. The point of departure was a conventional, general equilibrium model in which both households and producers are geographically mobile. The study's dependent variables refer to population, total, and manufacturing employment densities. Family income had a powerful effect in stimulating both population and employment density. A 10% increase in family income led to a 7.9% increase in total and a 9.2% increase in manufacturing employment densities. High family income must stand for high demand, and thus, firms are drawn to an area. High family income also drew households to an area. A 10% increase in family income led to a 5.5% increase in population density. High family income must represent "good" neighborhoods for households. High family income was positively correlated with population and employment density, but in other recent studies either a negative and significant relationship or an insignificant relationship were reported. Local taxes consist of the receipts of county government and those of municipalities, townships, school districts, and special districts within the county. The elasticities reported in Table 4 suggest that a 10% increase in such taxes resulted in about a 0.072% reduction in county population density during the decade. The Industrial Revenue Bonds (IRBs) and the percent of the labor force that is unionized are 2 potential policy instruments at the state level. The study results suggest that IRBs have not stimulated either manufacturing or total employment, and the coefficients were statistically insignificant in the structural equations. The elasticities imply that a 10% increase in percent union reduces total employment by 0.42% and manufacturing employment by 0.18%. The effect on population was tiny. Further, while not intended, the interstate highway program may have been a significant redistributor of population and employment but has not caused immigration of people and jobs from central cities.     

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.