Open AccessBook
Applied Spatial Data Analysis with R
TLDR
Hello, world: handling spatial data in R.Abstract:
Hello, world: handling spatial data in R.- Classes for spatial data in R.- Visualizing spatial data.- Spatial data import and export.- Further methods for handling spatial data.- Customising spatial data classes and methods.- Spatial point pattern analysis.- Interpolation and geostatistics.- Areal data and spatial autocorrelation.- Modelling areal data.- Disease mapping.- Afterword.- References.read more
Citations
More filters
Journal ArticleDOI
Simple Features for R: Standardized Support for Spatial Vector Data
TL;DR: The sf package implements simple features in R, and has roughly the same capacity for spatial vector data as packages sp, rgeos and rgdal, and its place in the R package ecosystem, and the potential to connect R to other computer systems are described.
Journal ArticleDOI
ggmap: Spatial Visualization with ggplot2
David Kahle,Hadley Wickham +1 more
TL;DR: This article details some new methods for the visualization of spatial data in R using the layered grammar of graphics implementation of ggplot2 in conjunction with the contextual information of static maps from Google Maps, OpenStreetMap, Stamen Maps or CloudMade Maps and presents an overview of a few utility functions.
Journal ArticleDOI
The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives
Dominique Lord,Fred L. Mannering +1 more
TL;DR: In the absence of detailed driving data that would help improve the identification of cause and effect relationships with individual vehicle crashes, most researchers have addressed this problem by framing it in terms of understanding the factors that affect the frequency of crashes -the number of crashes occurring in some geographical space (usually a roadway segment or intersection) over some specified time period as mentioned in this paper.
Journal ArticleDOI
SoilGrids1km--global soil information based on automated mapping.
Tomislav Hengl,Jorge Mendes de Jesus,R. A. MacMillan,Niels H. Batjes,Gerard B. M. Heuvelink,Eloi Ribeiro,Alessandro Samuel-Rosa,Bas Kempen,Johan G. B. Leenaars,Markus G. Walsh,Maria Ruiperez Gonzalez +10 more
TL;DR: SoilGrids1km provides an initial set of examples of soil spatial data for input into global models at a resolution and consistency not previously available and results of regression modeling indicate that the most useful covariates for modeling soils at the global scale are climatic and biomass indices, lithology, and taxonomic mapping units derived from conventional soil survey.
Journal ArticleDOI
Comparing Implementations of Estimation Methods for Spatial Econometrics
Roger Bivand,Gianfranco Piras +1 more
TL;DR: This review constitutes an up-to-date comparison of generalized method of moments and maximum likelihood implementations now available, using the cross-sectional US county data set provided by Drukker, Prucha, and Raciborski (2013d).