Applied Spatial Data Analysis with R

By G. Chi. 2015

1,939 citations recorded by the source corpus.

Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of intere... Within both the social and environmental sciences, much of the data collected is within a spatial context and requires statistical analysis for interpretation. The purpose of this book is to describe current methods for the analysis of spatial data. ... Spatial data analysis is a fast growing area and Voronoi diagrams provide a means of naturally partitioning space into subregions to facilitate spatial data manipulation, modelling of spatial structures, pattern recognition and locational optimizatio... With the surge in available spatial data from sources such as GPS and remote sensing, spatial analysis is becoming more important than ever. This volume provides a comprehensive discussion of spatial .... Some Books Bellow will give you all related to applied spatial data analysis with r! Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/3f1c2a64e76ebea6474ea936f88a757e72e42040

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Citations in the corpus

tmap: Thematic Maps in R (2018, methodology): “The programming languages R and Python (Van Rossum et al. 2011) oﬀer more freedom to analyze spatial data and also provide functionalities to visualize spatial data (Bivand et al. 2008; Westra 2015).”
Modelling changes in species’ abundance in response to projected climate change (2012, methodology): “We checked for spatial autocorrelation in the residuals by calculating Moran’s I for a wide range of distances using the ‘spdep’ package (Bivand et al., 2008) for R (R Development Core Team, 2009).”
Covariance Regression Analysis (2017, neutral): “Accordingly, spatial statistics attempts to explain the covariance structure of responses by their geographical locations (Cressie 1991; Bivand, Pebesma, and Gómez-Rubio 2008; Cressie and Wikle 2011).”
Spatial modelling with R-INLA: A review (2018, methodology): “…structure given by (4) to R-INLA: 1) Through an ASCII ﬁle; 2) through a symmetric adjacency matrix of dimension n × n ; 3) by extracting the structure directly from the shapeﬁle using the R-packages maptools (Bivand and Lewin-Koh, 2017) and spdep (Bivand and Piras, 2015; Bivand et al., 2013a).”
On the determinants of Airbnb location and its spatial distribution (2019, background): “Thus, an adjustment is required (Bivand et al., 2008: 260–261).”
ggmap: Spatial Visualization with ggplot2 (2013, methodology): “Fortunately the task is made a good deal easier by the data structures and plot methods of sp, RgoogleMaps, and related packages (Pebesma and Bivand, 2006; Bivand et al., 2008; Loecher and Berlin School of Economics and Law, 2013).”
Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables (2018, methodology): “…and778 implementing the Quantile Regression Forests, and the developers of the spatial analysis packages GDAL,779 rgdal, raster, sp (Pebesma, 2004; Bivand et al., 2008), and SAGA GIS (Conrad et al., 2015), on top of780 which we have built work-flows and examples of…”
On meat eating and human evolution: A taphonomic analysis of BK4b (Upper Bed II, Olduvai Gorge, Tanzania), and its bearing on hominin megafaunal consumption (2014, positive): “The latter is simulated by multiple pair-wise estimations, which create envelopes or confidence intervals for expected complete spatial randomness, which can then be used to assess if the observed function is contained inside (Bivand et al., 2009).”
Spatial Heterogeneity in Hedonic House Price Models: The Case of Austria (2014, methodology): “Instead, a k-nearest neighbour definition with row standardisation is used, accounting for heterogeneously distributed dwellings (Bivand et al., 2008).”
Phylogenetic diversity, trait diversity and niches: species assembly of ferns along a tropical elevational gradient (2011, methodology): “We used simultaneous autoregressive (SAR; Bivand et al., 2008) models to expand traditional linear models (e.”
Neglected biological patterns in the residuals (2011, background): “Heteroscedasticity can also be an issue whenever data has either a temporal or spatial component (Bivand et al. 2008; Cryer and Chan 2008).”
marmap: A Package for Importing, Plotting and Analyzing Bathymetric and Topographic Data in R (2013, background): “Bathy objects can also be converted into the class SpatialGridDataFrame for use in the sp package [16,17] (function as.SpatialGridDataFrame).”
Competition, predation, and migration: Individual choice patterns of Serengeti migrants captured by hierarchical models (2014, neutral): “…the concentration of nitrogen in the grass is inversely correlated with the mean NDVI (see Appendix C), we regression kriged (Hengl et al. 2007, Bivand et al. 2008) the data from the 148 points with a 9-year mean NDVI layer (2000–2009) to generate an accurate estimate of the spatial…”
Application of machine learning methods to spatial interpolation of environmental variables (2011, methodology): “Statistical methods: Generalized least squares trend estimation (GLS) (Bivand et al., 2008) is used because it allows errors to be correlated (Pinheiro and Bates, 2000; Venables and Ripley, 2002).”
Comparing Implementations of Estimation Methods for Spatial Econometrics (2015, methodology): “These three were also using eigenvalues to compute the log-determinant values; Stata spreg ml and R sarml only use eigenvalues, while R lagsarlm oﬀers a selection of methods (Bivand et al. 2013), but here used eigenvalues.”