960 resultados para sampling spatial location


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In this paper we address the issue of locating hierarchical facilities in the presence of congestion. Two hierarchical models are presented, where lower level servers attend requests first, and then, some of the served customers are referred to higher level servers. In the first model, the objective is to find the minimum number of servers and theirlocations that will cover a given region with a distance or time standard. The second model is cast as a Maximal Covering Location formulation. A heuristic procedure is then presented together with computational experience. Finally, some extensions of these models that address other types of spatial configurations are offered.

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Most facility location decision models ignore the fact that for a facility to survive it needs a minimum demand level to cover costs. In this paper we present a decision model for a firm thatwishes to enter a spatial market where there are several competitors already located. This market is such that for each outlet there is a demand threshold level that has to be achievedin order to survive. The firm wishes to know where to locate itsoutlets so as to maximize its market share taking into account the threshold level. It may happen that due to this new entrance, some competitors will not be able to meet the threshold and therefore will disappear. A formulation is presented together with a heuristic solution method and computational experience.

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In this paper we present a model that studies firm mergers in a spatial setting. A new model is formulated that addresses the issue of finding the number of branches that have to be eliminated by a firm after merging with another one, in order to maximize profits. The model is then applied to an example of bank mergers in the city of Barcelona. Finally, a variant of the formulation that introduces competition is presented together with some conclusions.

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Spatial evaluation of Culicidae (Diptera) larvae from different breeding sites: application of a geospatial method and implications for vector control. This study investigates the spatial distribution of urban Culicidae and informs entomological monitoring of species that use artificial containers as larval habitats. Collections of mosquito larvae were conducted in the São Paulo State municipality of Santa Bárbara d' Oeste between 2004 and 2006 during house-to-house visits. A total of 1,891 samples and nine different species were sampled. Species distribution was assessed using the kriging statistical method by extrapolating municipal administrative divisions. The sampling method followed the norms of the municipal health services of the Ministry of Health and can thus be adopted by public health authorities in disease control and delimitation of risk areas. Moreover, this type of survey and analysis can be employed for entomological surveillance of urban vectors that use artificial containers as larval habitat.

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ABSTRACT The Brazilian Atlantic Forest is one of the world's biodiversity hotspots, and is currently highly fragmented and disturbed due to human activities. Variation in environmental conditions in the Atlantic Forest can influence the distribution of species, which may show associations with some environmental features. Dung beetles (Coleoptera: Scarabaeinae) are insects that act in nutrient cycling via organic matter decomposition and have been used for monitoring environmental changes. The aim of this study is to identify associations between the spatial distribution of dung beetle species and Atlantic Forest structure. The spatial distribution of some dung beetle species was associated with structural forest features. The number of species among the sampling sites ranged widely, and few species were found in all remnant areas. Principal coordinates analysis indicated that species composition, abundance and biomass showed a spatially structured distribution, and these results were corroborated by permutational multivariate analysis of variance. The indicator value index and redundancy analysis showed an association of several dung beetle species with some explanatory environmental variables related to Atlantic Forest structure. This work demonstrated the existence of a spatially structured distribution of dung beetles, with significant associations between several species and forest structure in Atlantic Forest remnants from Southern Brazil.

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The precise sampling of soil, biological or micro climatic attributes in tropical forests, which are characterized by a high diversity of species and complex spatial variability, is a difficult task. We found few basic studies to guide sampling procedures. The objective of this study was to define a sampling strategy and data analysis for some parameters frequently used in nutrient cycling studies, i. e., litter amount, total nutrient amounts in litter and its composition (Ca, Mg, Κ, Ν and P), and soil attributes at three depths (organic matter, Ρ content, cation exchange capacity and base saturation). A natural remnant forest in the West of São Paulo State (Brazil) was selected as study area and samples were collected in July, 1989. The total amount of litter and its total nutrient amounts had a high spatial independent variance. Conversely, the variance of litter composition was lower and the spatial dependency was peculiar to each nutrient. The sampling strategy for the estimation of litter amounts and the amount of nutrient in litter should be different than the sampling strategy for nutrient composition. For the estimation of litter amounts and the amount of nutrients in litter (related to quantity) a large number of randomly distributed determinations are needed. Otherwise, for the estimation of litter nutrient composition (related to quality) a smaller amount of spatially located samples should be analyzed. The determination of sampling for soil attributes differed according to the depth. Overall, surface samples (0-5 cm) showed high short distance spatial dependent variance, whereas, subsurface samples exhibited spatial dependency in longer distances. Short transects with sampling interval of 5-10 m are recommended for surface sampling. Subsurface samples must also be spatially located, but with transects or grids with longer distances between sampling points over the entire area. Composite soil samples would not provide a complete understanding of the relation between soil properties and surface dynamic processes or landscape aspects. Precise distribution of Ρ was difficult to estimate.

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The spatial variability of strongly weathered soils under sugarcane and soybean/wheat rotation was quantitatively assessed on 33 fields in two regions in São Paulo State, Brazil: Araras (15 fields with sugarcane) and Assis (11 fields with sugarcane and seven fields with soybean/wheat rotation). Statistical methods used were: nested analysis of variance (for 11 fields), semivariance analysis and analysis of variance within and between fields. Spatial levels from 50 m to several km were analyzed. Results are discussed with reference to a previously published study carried out in the surroundings of Passo Fundo (RS). Similar variability patterns were found for clay content, organic C content and cation exchange capacity. The fields studied are quite homogeneous with respect to these relatively stable soil characteristics. Spatial variability of other characteristics (resin extractable P, pH, base- and Al-saturation and also soil colour), varies with region and, or land use management. Soil management for sugarcane seems to have induced modifications to greater depths than for soybean/wheat rotation. Surface layers of soils under soybean/wheat present relatively little variation, apparently as a result of very intensive soil management. The major part of within-field variation occurs at short distances (< 50 m) in all study areas. Hence, little extra information would be gained by increasing sampling density from, say, 1/km² to 1/50 m². For many purposes, the soils in the study regions can be mapped with the same observation density, but residual variance will not be the same in all areas. Bulk sampling may help to reveal spatial patterns between 50 and 1.000 m.

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Aim  The imperfect detection of species may lead to erroneous conclusions about species-environment relationships. Accuracy in species detection usually requires temporal replication at sampling sites, a time-consuming and costly monitoring scheme. Here, we applied a lower-cost alternative based on a double-sampling approach to incorporate the reliability of species detection into regression-based species distribution modelling.Location  Doñana National Park (south-western Spain).Methods  Using species-specific monthly detection probabilities, we estimated the detection reliability as the probability of having detected the species given the species-specific survey time. Such reliability estimates were used to account explicitly for data uncertainty by weighting each absence. We illustrated how this novel framework can be used to evaluate four competing hypotheses as to what constitutes primary environmental control of amphibian distribution: breeding habitat, aestivating habitat, spatial distribution of surrounding habitats and/or major ecosystems zonation. The study was conducted on six pond-breeding amphibian species during a 4-year period.Results  Non-detections should not be considered equivalent to real absences, as their reliability varied considerably. The occurrence of Hyla meridionalis and Triturus pygmaeus was related to a particular major ecosystem of the study area, where suitable habitat for these species seemed to be widely available. Characteristics of the breeding habitat (area and hydroperiod) were of high importance for the occurrence of Pelobates cultripes and Pleurodeles waltl. Terrestrial characteristics were the most important predictors of the occurrence of Discoglossus galganoi and Lissotriton boscai, along with spatial distribution of breeding habitats for the last species.Main conclusions  We did not find a single best supported hypothesis valid for all species, which stresses the importance of multiscale and multifactor approaches. More importantly, this study shows that estimating the reliability of non-detection records, an exercise that had been previously seen as a naïve goal in species distribution modelling, is feasible and could be promoted in future studies, at least in comparable systems.

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Temporal variability was studied in the common sea urchin Paracentrotus lividus through the analysis of the genetic composition of three yearly cohorts sampled over two consecutive springs in a locality in northwestern Mediterranean. Individuals were aged using growth ring patterns observed in tests and samples were genotyped for five microsatellite loci. No reduction of genetic diversity was observed relative to a sample of the adult population from the same location or within cohorts across years. FST and amova results indicated that the differentiation between cohorts is rather shallow and not significant, as most variability is found within cohorts and within individuals. This mild differentiation translated into estimates of effective population size of 90100 individuals. When the observed excess of homozygotes was taken into account, the estimate of the average number of breeders increased to c. 300 individuals. Given our restricted sampling area and the known small-scale heterogeneity in recruitment in this species, our results suggest that at stretches of a few kilometres of shoreline, large numbers of progenitors are likely to contribute to the larval pool at each reproduction event. Intercohort variation in our samples is six times smaller than spatial variation between adults of four localities in the western Mediterranean. Our results indicate that, notwithstanding the stochastic events that take place during the long planktonic phase and during the settlement and recruitment processes, reproductive success in this species is high enough to produce cohorts genetically diverse and with little differentiation between them. Further research is needed before the link between genetic structure and underlying physical and biological processes can be well established.

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The New Economic Geography literature allows detailed analysis of the factors that determine the location decisions of firms in integrated markets. However, the competitive process is modelled in a rather rudimentary way, and the empirical evidence has usually been obtained from reduced-form econometric specifications. This study describes a structural model that takes into account strategic interactions between firms. We investigate the relationship between the degree of perceived competition ¿ not only from local firms but from firms in other regions ¿ and geographic concentration. The preliminary results indicate that, in aggregate terms, local firms present stronger competition than firms in other regions. Moreover, it is confirmed that greater geographical concentration of production reduces market power, due to the intensification of local competition; however, its impact on production costs is unclear.

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The New Economic Geography literature allows detailed analysis of the factors that determine the location decisions of firms in integrated markets. However, the competitive process is modelled in a rather rudimentary way, and the empirical evidence has usually been obtained from reduced-form econometric specifications. This study describes a structural model that takes into account strategic interactions between firms. We investigate the relationship between the degree of perceived competition ¿ not only from local firms but from firms in other regions ¿ and geographic concentration. The preliminary results indicate that, in aggregate terms, local firms present stronger competition than firms in other regions. Moreover, it is confirmed that greater geographical concentration of production reduces market power, due to the intensification of local competition; however, its impact on production costs is unclear.

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Assessing the spatial variability of soil chemical properties has become an important aspect of soil management strategies with a view to higher crop yields with minimal environmental degradation. This study was carried out at the Centro Experimental of the Instituto Agronomico, in Campinas, São Paulo, Brazil. The aim was to characterize the spatial variability of chemical properties of a Rhodic Hapludox on a recently bulldozer-cleaned area after over 30 years of coffee cultivation. Soil samples were collected in a 20 x 20 m grid with 36 sampling points across a 1 ha area in the layers 0.0-0.2 and 0.2-0.4 m to measure the following chemical properties: pH, organic matter, K+, P, Ca2+, Mg2+, potential acidity, NH4-N, and NO3-N. Descriptive statistics were applied to assess the central tendency and dispersion moments. Geostatistical methods were applied to evaluate and to model the spatial variability of variables by calculating semivariograms and kriging interpolation. Spatial dependence patterns defined by spherical model adjusted semivariograms were made for all cited soil properties. Moderate to strong degrees of spatial dependence were found between 31 and 60 m. It was still possible to map soil spatial variability properties in the layers 0-20 cm and 20-40 cm after plant removal with bulldozers.

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The loss of biodiversity has become a matter of urgent concern and a better understanding of local drivers is crucial for conservation. Although environmental heterogeneity is recognized as an important determinant of biodiversity, this has rarely been tested using field data at management scale. We propose and provide evidence for the simple hypothesis that local species diversity is related to spatial environmental heterogeneity. Species partition the environment into habitats. Biodiversity is therefore expected to be influenced by two aspects of spatial heterogeneity: 1) the variability of environmental conditions, which will affect the number of types of habitat, and 2) the spatial configuration of habitats, which will affect the rates of ecological processes, such as dispersal or competition. Earlier, simulation experiments predicted that both aspects of heterogeneity will influence plant species richness at a particular site. For the first time, these predictions were tested for plant communities using field data, which we collected in a wooded pasture in the Swiss Jura mountains using a four-level hierarchical sampling design. Richness generally increased with increasing environmental variability and "roughness" (i.e. decreasing spatial aggregation). Effects occurred at all scales, but the nature of the effect changed with scale, suggesting a change in the underlying mechanisms, which will need to be taken into account if scaling up to larger landscapes. Although we found significant effects of environmental heterogeneity, other factors such as history could also be important determinants. If a relationship between environmental heterogeneity and species richness can be shown to be general, recently available high-resolution environmental data can be used to complement the assessment of patterns of local richness and improve the prediction of the effects of land use change based on mean site conditions or land use history.

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The influence of relief forms has been studied by several authors and explains the variability in the soil attributes of a landscape. Soil physical attributes depend on relief forms, and their assessment is important in mechanized agricultural systems, such as of sugarcane. This study aimed to characterize the spatial variability in the physical soil attributes and their relationship to the hillslope curvatures in an Alfisol developed from sandstone and growing sugarcane. Grids of 100 x 100 m were delimited in a convex and a concave area. The grids had a regular spacing of 10 x 10 m, and the crossing points of this spacing determined a total of 121 georeferenced sampling points. Samples were collected to determine the physical attributes related to soil aggregates, porosity, bulk density, resistance to penetration and moisture within the 0-0.2 and 0.2-0.4 m depth. Statistical analyses, geostatistics and Student's t-tests were performed with the means of the areas. All attributes, except aggregates > 2 mm in the 0-0.2 m depth and macroporosity at both depths, showed significant differences between the hillslope curvatures. The convex area showed the highest values of the mean weighted diameter, mean geometric diameter, aggregates > 2 mm, 1-2 mm aggregates, total porosity and moisture and lower values of bulk density and resistance to penetration in both depth compared to the concave area. The number of soil attributes with greater spatial variability was higher in the concave area.

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Soil properties play an important role in spatial variability of crop yield. However, a low spatial correlation has generally been observed between maps of crop yield and of soil properties. The objectives of the present investigation were to assess the spatial pattern variability of soil properties and of corn yield at the same sampling intensity, and evaluate its cause-and-effect relationships. The experimental site was structured in a grid of 100 referenced points, spaced at 10 m intervals along four parallel 250 m long rows spaced 4.5 m apart. Thus, points formed a rectangle containing four columns and 25 rows. Therefore, each sampling cell encompassed an area of 45 m² and consisted of five 10 m long crop rows, in which the referenced points represented the center. Samples were taken from the layers 0-0.1 m and 0.1-0.2 m. Soil physical and chemical properties were evaluated. Statistical analyses consisted of data description and geostatistics. The spatial dependence of corn yield and soil properties was confirmed. The hypothesis of this study was confirmed, i.e., when sampling the soil to determine the values of soil characteristics at similar to sampling intensity as for crop yield assessments, correlations between the spatial distribution of soil characteristics and crop yield were observed. The spatial distribution pattern of soil properties explained 65 % of the spatial distribution pattern of corn yield. The spatial distribution pattern of clay content and percentage of soil base saturation explained most of the spatial distribution pattern of corn yield.