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Geographical Units (geographical + unit)
Selected AbstractsA new index of access to primary care services in rural areasAUSTRALIAN AND NEW ZEALAND JOURNAL OF PUBLIC HEALTH, Issue 5 2009Matthew R. McGrail Abstract Objective: To outline a new index of access to primary care services in rural areas that has been specifically designed to overcome weaknesses of using existing geographical classifications. Methods: Access was measured by four key dimensions of availability, proximity, health needs and mobility. Population data were obtained through the national census and primary care service data were obtained through the Medical Directory of Australia. All data were calculated at the smallest feasible geographical unit (collection districts). The index of access was measured using a modified two-step floating catchment area (2SFCA) method, which incorporates two necessary additional spatial functions (distance-decay and capping) and two additional non-spatial dimensions (health needs and mobility). Results: An improved index of access, specifically designed to better capture access to primary care in rural areas, is achieved. These improvements come from: 1) incorporation of actual health service data in the index; 2) methodological improvements to existing access measures, which enable both proximity to be differentiated within catchments and the use of varying catchment sizes; and 3) improved sensitivity to small-area variations. Conclusion: Despite their recognised weaknesses, the Australian government uses broad geographical classifications as proxy measures of access to underpin significant rural health funding programs. This new index of access could provide a more equitable means for resource allocation. Implications: Significant government funding, aimed at improving health service access inequities in rural areas, could be better targeted by underpinning programs with our improved access measure. [source] Dependence of broad-scale geographical variation in fleshy-fruited plant species richness on disperser bird species richnessGLOBAL ECOLOGY, Issue 4 2004Ana L. Márquez ABSTRACT Aim, We analysed the interdependence of avian frugivore- and fruited plant-species richness at the scale of major river basins across Europe, taking into account several environmental factors along different spatial gradients. Location, Continental Europe and the British Isles. Methods, We focused on wintering birds and autumn/winter fruiting plants, and used major river basins as geographical units and Structural Equation Modelling as the principal analytical tool. Results, The statistical influence of disperser species richness on fleshy-fruited plant species richness is roughly double that of the reverse. Broad-scale variation in frugivore richness is more dependent on environmental factors than on fruited plant richness. However, the influence of disperser richness on plant richness is four times higher than the influence of environmental factors. Environmental influences on both birds and plants are greater than purely spatial influences. Main conclusions, Our results are interpreted as indicating that biotic dispersal of fruits strongly affects broad-scale geographical trends of fleshy-fruited plant species richness, whereas richness of fruited plants moderately affects frugivore richness. [source] How Big is Your Neighbourhood?JOURNAL OF AGRICULTURAL ECONOMICS, Issue 1 2007Spatial Implications of Market Participation Among Filipino Smallholders O18; R15; C11 Abstract We present a procedure for estimating two quantities defining the spatial externality in discrete-choice commonly referred to as ,the neighbourhood effect'. One quantity, the propensity for neighbours to make the same decision, reflects traditional preoccupations; the other quantity, the magnitude of the neighbourhood itself, is novel. Because both quantities have fundamental bearing on the magnitude of the spatial externality, it is desirable to have a robust algorithm for their estimation. Using recent advances in Bayesian estimation and model comparison, we devise such an algorithm and illustrate its application to a sample of northern-Filipino smallholders. We determine that a significant, positive, neighbourhood effect exists; that, among the 12 geographical units comprising the sample, the neighbourhood spans a three-unit radius; and that policy prescriptions are significantly altered when calculations account for the spatial externality. [source] Global patterns of plant diversity and floristic knowledgeJOURNAL OF BIOGEOGRAPHY, Issue 7 2005Gerold Kier Abstract Aims, We present the first global map of vascular plant species richness by ecoregion and compare these results with the published literature on global priorities for plant conservation. In so doing, we assess the state of floristic knowledge across ecoregions as described in floras, checklists, and other published documents and pinpoint geographical gaps in our understanding of the global vascular plant flora. Finally, we explore the relationships between plant species richness by ecoregion and our knowledge of the flora, and between plant richness and the human footprint , a spatially explicit measure of the loss and degradation of natural habitats and ecosystems as a result of human activities. Location, Global. Methods, Richness estimates for the 867 terrestrial ecoregions of the world were derived from published richness data of c. 1800 geographical units. We applied one of four methods to assess richness, depending on data quality. These included collation and interpretation of published data, use of species,area curves to extrapolate richness, use of taxon-based data, and estimates derived from other ecoregions within the same biome. Results, The highest estimate of plant species richness is in the Borneo lowlands ecoregion (10,000 species) followed by nine ecoregions located in Central and South America with , 8000 species; all are found within the Tropical and Subtropical Moist Broadleaf Forests biome. Among the 51 ecoregions with , 5000 species, only five are located in temperate regions. For 43% of the 867 ecoregions, data quality was considered good or moderate. Among biomes, adequate data are especially lacking for flooded grasslands and flooded savannas. We found a significant correlation between species richness and data quality for only a few biomes, and, in all of these cases, our results indicated that species-rich ecoregions are better studied than those poor in vascular plants. Similarly, only in a few biomes did we find significant correlations between species richness and the human footprint, all of which were positive. Main conclusions, The work presented here sets the stage for comparisons of degree of concordance of plant species richness with plant endemism and vertebrate species richness: important analyses for a comprehensive global biodiversity strategy. We suggest: (1) that current global plant conservation strategies be reviewed to check if they cover the most outstanding examples of regions from each of the world's major biomes, even if these examples are species-poor compared with other biomes; (2) that flooded grasslands and flooded savannas should become a global priority in collecting and compiling richness data for vascular plants; and (3) that future studies which rely upon species,area calculations do not use a uniform parameter value but instead use values derived separately for subregions. [source] What can the age composition of a population tell us about the age composition of its out-migrants?POPULATION, SPACE AND PLACE (PREVIOUSLY:-INT JOURNAL OF POPULATION GEOGRAPHY), Issue 1 2007Jani S. Little Abstract Preliminary findings show that the age structure of a population can provide valuable information about the age composition of its out-migrants, and that this relationship can become a key ingredient in the proposed new method for estimating the age profile of out-migrants when accurate data are not available. The method relies on the Rogers-Castro model schedule to consistently and accurately represent age profiles of out-migration, and the results show that variation among these out-migration schedules can be captured by a typology based on a small set of clusters, or families of schedules. Membership of the clusters is then predicted from simple measures of population composition using discriminant function analysis. The investigation is based on data for US states, CMSAs, MSAs and non-metropolitan counties, and their outflows of migrants between 1995 and 2000. The measures of population age composition come from official 1995 intercensal age-specific population estimates for the same geographical units. Copyright © 2007 John Wiley & Sons, Ltd. [source] | ||||||||||