Search results for "spatial dependence"

showing 6 items of 36 documents

Spatial Mark-Recapture Method in the Estimation of Crayfish Population Size

1995

The mark-recapture method is considered for estimation of population size of slowly moving animals like crayfish. The Petersen type estimator for closed population is generalized for situations where recaptures are spatially dependent between the capture sites, and its variance approximation is derived using point processes as models for the population. The method of quadratic forms is suggested to be used as variance estimator. Finally, a trapping design is proposed where onc trap at recapture is replaced by four adjacent traps. A simulation experiment is performed to explain the robusticity of the new trapping design against movements of animals.

Statistics and Probabilityeducation.field_of_studyPopulation sizePopulationEstimatorGeneral MedicineTrappingCrayfishPoint processMark and recaptureStatisticsStatistics Probability and UncertaintySpatial dependenceeducationMathematicsBiometrical Journal
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Modeling the Spatial Distribution of Xylella fastidiosa: A Nonstationary Approach with Dispersal Barriers

2022

Spatial species distribution models often assume isotropy and stationarity, implying that spatial dependence is direction-invariant and uniform throughout the study area. However, these assumptions are violated when dispersal barriers are present. Despite this, the issue of nonstationarity has been little explored in the context of plant health. The objective of this study was to evaluate the influence of barriers in the distribution of Xylella fastidiosa in the demarcated area in Alicante, Spain. Occurrence data from 2018 were analyzed through spatial Bayesian hierarchical models. The stationary model, illustrating a scenario without control interventions or geographical features, was com…

Xylella fastidiosaAlmond leaf scorchNon-stationary modelsIsotropySpecies distributionStochastic partial differential equationPlant ScienceContainmentBiologySpatial distributionbiology.organism_classificationDisease controlINLABiological dispersalU10 Mathematical and statistical methodsStatistical physicsXylella fastidiosaSpatial dependenceInvariant (mathematics)H20 Plant diseasesAgronomy and Crop ScienceBarriersEradication
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Clubs de convergence et effets de débordements géographiques : une analyse spatiale sur données régionales européennes, 1980-1995

2007

Our article offers an econometric model of spatial interactions for the empirical analysis of growth in European regions over the period 1980-1995. The model detects spatial spillover effects and makes it possible to take account of the European economy’s strong polarization. More specifically, by factoring in both spatial autocorrelation and spatial heterogeneity, we characterize the economic polarization pattern in European regions, identify convergence clubs, and model them as spatial regimes. We estimate a two-regime model with spatially autocorrelated errors and show that the convergence process differs between the two regimes. We find a strongly significant spatial spillover effect : …

convergenceclubs de convergence ; économétrie spatiale ; dépendance spatiale ; effets de débordements géographiques ; Classification JEL C21 - C51 - R11 - R15 ; â-convergence05 social sciences0211 other engineering and technologies021107 urban & regional planning02 engineering and technology[SHS.ECO]Humanities and Social Sciences/Economics and Financerégions européennesconvergence clubs ; ß-convergence ; spatial econometrics ; spatial dependence ; spatial spillover effects JEL Classification C21 - C51 - R11 - R150502 economics and business8. Economic growth[ SHS.ECO ] Humanities and Social Sciences/Economies and finances050207 economicsBusiness and International Management[SHS.ECO] Humanities and Social Sciences/Economics and FinanceGeneral Economics Econometrics and FinanceComputingMilieux_MISCELLANEOUSanalyse spatialeAERES B Economie Gestion - CoNRS37-R3 - EconLit - Code JEL : C21 R12
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Geostatistics for Mapping Leaf Area Index over a Cropland Landscape: Efficiency Sampling Assessment

2010

This paper evaluates the performance of spatial methods to estimate leaf area index (LAI) fields from ground-based measurements at high-spatial resolution over a cropland landscape. Three geostatistical model variants of the kriging technique, the ordinary kriging (OK), the collocated cokriging (CKC) and kriging with an external drift (KED) are used. The study focused on the influence of the spatial sampling protocol, auxiliary information, and spatial resolution in the estimates. The main advantage of these models lies in the possibility of considering the spatial dependence of the data and, in the case of the KED and CKC, the auxiliary information for each location used for prediction pur…

cropland landscapeleaf area indexScienceQgeostatistics methodsSampling (statistics)GeostatisticsField (geography)KrigingGeneral Earth and Planetary SciencesCommon spatial patternSpatial dependenceLeaf area indexVariogramspatial samplingMathematicsRemote sensingRemote Sensing
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Regional Disparities and Spatial Dependence of Bankruptcy in Spain

2021

Firm survival, bankruptcy, and turnaround are of great interest nowadays. Bankruptcy is the ultimate resource for a company to survive when it is affected by a severe decline. Thus, determinants of firm turnaround and survival in the context of bankruptcy are of interest to researchers, managers, and policy-makers. Prior turnaround literature has broadly studied firm-specific factors for turnaround success. However, location-specific factors remain relatively unstudied despite their increasing relevance. Thus, this paper aims to evaluate the existence of spatial dependence on the outcome of the bankruptcy procedure. Economic geography and business literature suggest that location matters an…

provincial clustersIndex (economics)General Mathematicsspatial dependenceContext (language use)Sample (statistics)Logistic regressionDisease cluster:CIENCIAS ECONÓMICAS [UNESCO]survivalResource (project management)0502 economics and businessComputer Science (miscellaneous)Per capitaspainQA1-939geostatisticsMoran’s Index050207 economicsEngineering (miscellaneous)05 social sciencesUNESCO::CIENCIAS ECONÓMICASbankruptcyBankruptcySpainDemographic economicsBusiness050203 business & managementmoran’s indexMathematicsMathematics
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A spatio-temporal measure of spatial dependence: An example using read estate data

2013

International audience

spatial dependence[ SHS.ECO ] Humanities and Social Sciences/Economies and finances[SHS.ECO] Humanities and Social Sciences/Economics and Finance[SHS.ECO]Humanities and Social Sciences/Economics and FinanceMeasureComputingMilieux_MISCELLANEOUS
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