			Home About us Contact

Spatial Regression Models (spatial + regression_models)

Distribution by Scientific Domains

Life Sciences	57%

Selected Abstracts

A General Misspecification Test for Spatial Regression Models: Dependence, Heterogeneity, and Nonlinearity

JOURNAL OF REGIONAL SCIENCE, Issue 2 2001
Thomas De Graaff
There is an increasing awareness of the potentials of nonlinear modeling in regional science. This can be explained partly by the recognition of the limitations of conventional equilibrium models in complex situations, and also by the easy availability and accessibility of sophisticated computational techniques. Among the class of nonlinear models, dynamic variants based on, for example, chaos theory stand out as an interesting approach. However, the operational significance of such approaches is still rather limited and a rigorous statistical-econometric treatment of nonlinear dynamic modeling experiments is lacking. Against this background this paper is concerned with a methodological and empirical analysis of a general misspecification test for spatial regression models that is expected to have power against nonlinearity, spatial dependence, and heteroskedasticity. The paper seeks to break new research ground by linking the classical diagnostic tools developed in spatial econometrics to a misspecification test derived directly from chaos theory,the BDS test, developed by Brock, Dechert, and Scheinkman (1987). A spatial variant of the BDS test is introduced and applied in the context of two examples of spatial process models, one of which is concerned with the spatial distribution of regional investments in The Netherlands, the other with spatial crime patterns in Columbus, Ohio. [source]

Ecological risk assessment of persistent toxic substances for the clam Tapes philipinarum in the lagoon of venice, italy

ENVIRONMENTAL TOXICOLOGY & CHEMISTRY, Issue 6 2004
Christian Micheletti
Abstract Because of contamination of sediments of the Lagoon of Venice, Italy, by inorganic pollutants (e.g., arsenic, cadmium, chromium, copper, lead, mercury, nickel, and zinc) and organic pollutants (e.g., polychlorobiphenyls), as well as the ecological and economical relevance of the edible clam Tapes philipinarum, an ecological risk assessment was undertaken to ascertain the extent of bioaccumulation that would pose a significant risk. Risk was estimated by means of toxic units and hazard quotient approaches, by comparing the exposure concentration with the effect concentration. Clam exposure was estimated by applying previous results based on bioaccumulation spatial regression models. In addition, a comparison was made between sum of dioxin-like polychlorinated biphenyl (PCB) congeners and total PCB bioaccumulation provided by spatial regression models and by a partitioning model. The effect concentrations were calculated as tissue screening concentrations, as the product of pollutant sediment quality criteria and the bioaccumulation factor. Finally, the cumulative risk posed by selected inorganic pollutants and total PCBs was estimated and a map of risk was drawn. The resulting chemicals of potential ecological concern were mercury, cadmium, arsenic, and nickel, as well as, to a lesser extent, total PCBs. [source]

Space varying coefficient models for small area data

ENVIRONMETRICS, Issue 5 2003
Renato M. Assunção
Abstract Many spatial regression problems using area data require more flexible forms than the usual linear predictor for modelling the dependence of responses on covariates. One direction for doing this is to allow the coefficients to vary as smooth functions of the area's geographical location. After presenting examples from the scientific literature where these spatially varying coefficients are justified, we briefly review some of the available alternatives for this kind of modelling. We concentrate on a Bayesian approach for generalized linear models proposed by the author which uses a Markov random field to model the coefficients' spatial dependency. We show that, for normally distributed data, Gibbs sampling can be used to sample from the posterior and we prove a result showing the equivalence between our model and other usual spatial regression models. We illustrate our approach with a number of rather complex applied problems, showing that the method is computationally feasible and provides useful insights in substantive problems. Copyright © 2003 John Wiley & Sons, Ltd. [source]

Diversity and species composition of West African ungulate assemblages: effects of fire, climate and soil

GLOBAL ECOLOGY, Issue 6 2008
Erik Klop
ABSTRACT Aim, Anthropogenic fires are a major component of the ecology of rangelands throughout the world. To assess the effects of these fires on the diversity patterns of herbivores, we related gradients in fire occurrence, climate and soil fertility to patterns in alpha and beta diversity of African ungulates. Location, West Africa. Methods, We used a survey-based approach for ungulates in 37 protected areas in desert, savanna and rain forest habitats throughout West Africa, combined with satellite images of fire occurrence and digital maps of actual evapotranspiration and soil fertility. Alpha diversity was related to the environmental variables using conventional and spatial regression models. We investigated beta diversity using partial Mantel tests and ordination techniques, and by partitioning the variance in assemblage composition into environmental and spatial components. Results, The species richness of grazers showed a quadratic relationship with actual evapotranspiration, whereas that of browsers and frugivores showed a linear relationship. However, in the multiple regression models fire occurrence was the only variable that significantly correlated with the species richness of grazers. Soil fertility was weakly related to overall beta diversity and the species richness of browsers, but was non-significant in the multiple regression models. Fire occurrence was the most important variable explaining species composition of the overall species set and of grazers, whereas the assemblage composition of browsers and frugivores was explained mostly by actual evapotranspiration. Main conclusions, In contrast to previous studies, our analyses show that moisture and nutrients alone fail to adequately predict the diversity patterns of grazing ungulates. Rather, the species richness and assemblage composition of grazers are largely governed by anthropogenic fires that modify the quality and structure of the grass sward. Diversity patterns of browsers and frugivores are markedly different from grazers and depend mainly on the availability of moisture, which is positively correlated with the availability of foliage and fruits. Our study highlights the importance of incorporating major human-induced disturbances or habitat alterations into analyses of diversity patterns. [source]

Type and spatial structure of distribution data and the perceived determinants of geographical gradients in ecology: the species richness of African birds

GLOBAL ECOLOGY, Issue 5 2007
Jana M. McPherson
ABSTRACT Aim, Studies exploring the determinants of geographical gradients in the occurrence of species or their traits obtain data by: (1) overlaying species range maps; (2) mapping survey-based species counts; or (3) superimposing models of individual species' distributions. These data types have different spatial characteristics. We investigated whether these differences influence conclusions regarding postulated determinants of species richness patterns. Location, Our study examined terrestrial bird diversity patterns in 13 nations of southern and eastern Africa, spanning temperate to tropical climates. Methods, Four species richness maps were compiled based on range maps, field-derived bird atlas data, logistic and autologistic distribution models. Ordinary and spatial regression models served to examine how well each of five hypotheses predicted patterns in each map. These hypotheses propose productivity, temperature, the heat,water balance, habitat heterogeneity and climatic stability as the predominant determinants of species richness. Results, The four richness maps portrayed broadly similar geographical patterns but, due to the nature of underlying data types, exhibited marked differences in spatial autocorrelation structure. These differences in spatial structure emerged as important in determining which hypothesis appeared most capable of explaining each map's patterns. This was true even when regressions accounted for spurious effects of spatial autocorrelation. Each richness map, therefore, identified a different hypothesis as the most likely cause of broad-scale gradients in species diversity. Main conclusions, Because the ,true' spatial structure of species richness patterns remains elusive, firm conclusions regarding their underlying environmental drivers remain difficult. More broadly, our findings suggest that care should be taken to interpret putative determinants of large-scale ecological gradients in light of the type and spatial characteristics of the underlying data. Indeed, closer scrutiny of these underlying data , here the distributions of individual species , and their environmental associations may offer important insights into the ultimate causes of observed broad-scale patterns. [source]