Regression analysis of spatial data.

By Colin M. Beale, J. Lennon, J. Yearsley, M. Brewer, David A. Elston. Ecology Letters, 2010

644 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/75ce85bb362f8c808f9f8e9d547d7e11353984ae

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In Environmental Engineering

In Global and Planetary Change

In Nature and Landscape Conservation

Citations in the corpus

Measuring the relative effect of factors affecting species distribution model predictions (2014, background): “Although these are two different types of SAC, they both usually result in spatially correlated residuals from the fit of a SDM and are difficult to distinguish based on data (Guisan & Thuiller 2005; Dormann et al. 2007; Beale et al. 2010).”
Eight (and a half) deadly sins of spatial analysis (2012, background): “…this fundamental property of geographical data was largely ignored until recently (Foody, 2004; Bickford & Laffan, 2006; Cassemiro et al., 2007; Beale et al., 2010; Landeiro & Magnusson, 2011), and it remains very uncommon for geographical ecology papers to report that they have determined…”
Spatial statistics, spatial regression, and graph theory in ecology (2012, methodology): “A wide variety of spatial regression methods can be used to account for the presence of significant spatial autocorrelation in the data (either in the response or explanatory variables; Haining, 2003; Fortin and Dale, 2005; Dormann et al., 2007; Beale et al., 2010; Bini et al., 2009).”
Habitat availability for amphibians and extinction threat: a global analysis (2015, methodology): “analysis of spatial data (Dormann et al., 2007; Beale et al., 2010).”
Community ecology in the age of multivariate multiscale spatial analysis (2012, background): “2007, Beale et al. 2010]) and can be considered for testing summary statistics of community data as response variables, such as species richness or species scores along a single ordination axis (Diniz-Filho and Bini 2005). Rangel et al. (2007) chose another strategy by modeling species richness as s”
Data Integration for Large-Scale Models of Species Distributions. (2020, background): “For grids, the development and fitting of distribution models is a mature process; each grid cell can be considered on its own, and themain complication is usually whether spatial autocorrelation should be accounted for [61].”
Macroecology of pollination networks (2013, methodology): “Measurements from geographically close locations are expected to be more similar than measurements taken from distant locations violating the usual assumption of independency (Beale et al., 2010).”
Scale‐dependent relationships between tree species richness and ecosystem function in forests (2013, methodology): “generalized least-squares is a reliable method for doing so (Beale et al. 2010).”
The spatial structure of Antarctic biodiversity (2014, background): “The significance of autocorrelation for understanding variation in biodiversity remains the subject of intense interest (e.g., Beale et al. 2010, Hawkins 2012).”
Generating spatially constrained null models for irregularly spaced data using Moran spectral randomization methods (2015, methodology): “…replicates with a fixed correlationwith the original data (e.g. as needed for simulation studies to test performance of estimation methods in regression analysis of spatial data; Beale et al. 2010) as they control the parameters of the generating process, not the properties of the observed pattern.”