Spatial Data Science

with applications in R

von

Pebesma, EdzerBivand, Roger

BuchGebunden

Verkaufsrang25134inBiologie

CHF129.00

BuchCHF129.00

E-BookabCHF87.05

In den Warenkorb

Beschreibung

This book explains the basics of spatial data types, spatial data handling, spatial topology, and statistical analysis of spatial data, with applications in R. In addition to writing one's own script, this book teaches whether running that script or creating that map is a good idea.

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Details

ISBN/GTIN978-1-138-31118-3

ProduktartBuch

EinbandGebunden

VerlagTaylor and FrancisChapman and Hall/CRC

Erscheinungsdatum10.05.2023

ReiheChapman & Hall/CRC The R Series

Seiten300 Seiten

SpracheEnglisch

MasseBreite 156 mm, Höhe 234 mm, Dicke 20 mm

Gewicht900 g

IllustrationenFarb., s/w. Abb.

Artikel-Nr.39465946

KatalogBuchzentrum

Datenquelle-Nr.30463608

WarengruppeBiologie

Weitere Details

Reihe

Dealing with Memory Limits and Working with Large Data Sets in R

Statistical Computing with R

R Graphics

Statistical Computing in C++ and R

Event History Analysis with R

Customer and Business Analytics

Programming Graphical User Interfaces in R

Analyzing Baseball Data with R

Implementing Reproducible Research

Stated Preference Methods Using R

Growth Curve Analysis and Visualization Using R

Computational Actuarial Science with R

Multiple Factor Analysis by Example Using R

Analyzing Sensory Data with R

Displaying Time Series, Spatial, and Space-Time Data with R

Using R for Numerical Analysis in Science and Engineering

Advanced R

Introduction to Scientific Programming and Simulation Using R

Using R for Introductory Statistics

Data Science in R

Parallel Computing for Data Science

Dynamic Documents with R and knitr

Statistics in Toxicology Using R

Graphical Data Analysis with R

Reproducible Research with R and R Studio

Introductory Fisheries Analyses with R

Using the R Commander

bookdown

Spatial Microsimulation with R

The Essentials of Data Science: Knowledge Discovery Using R

R Primer

Dynamic Documents with R and knitr

Reproducible Research with R and R Studio

Extending R

bookdown

Using the R Commander

Customer and Business Analytics

R Primer

blogdown

Flexible Regression and Smoothing

Data Science in R

R Markdown

Reproducible Finance with R

Practical R for Mass Communication and Journalism

Displaying Time Series, Spatial, and Space-Time Data with R

Statistical Computing with R, Second Edition

Hands-On Machine Learning with R

Advanced R, Second Edition

Statistical Inference via Data Science: A ModernDive into R and the Tidyverse

Reproducible Research with R and RStudio

Learn R

Learning Microeconometrics with R

Geocomputation with R

Digital Media

Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

Interactive Documents with R

Engineering Production-Grade Shiny Apps

Javascript for R

Advanced R Solutions

Dose-Response Analysis Using R

Statistics in Toxicology Using R

Distributions for Modeling Location, Scale, and Shape

Event History Analysis with R

Outstanding User Interfaces with Shiny

Rasch Measurement Theory Analysis in R

Spatial Sampling with R

A Criminologist's Guide to R

R for Political Data Science

ANOVA and Mixed Models

Open Science with R

Spatial Data Science

Starting Quantitative Analysis with R

Analyzing US Census Data

Deep Learning and Scientific Computing with R torch

Tidy Finance with R

Model-Based Clustering, Classification, and Density Estimation Using mclust in R

Graphical Data Analysis with R

Statistical Computing in C++ and R

Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

Modern Data Visualization with R

Learn R

Behavior Analysis with Machine Learning Using R

Event History Analysis with R

Spatial Analysis in Geology Using R

Analyzing Baseball Data with R

Introductory Fisheries Analyses with R

Multiple Factor Analysis by Example Using R

Über den/die AutorIn

Pebesma, EdzerBivand, Roger

Edzer Pebesma is professor at the Institute for Geoinformatics of the University of Muenster, Germany, where he leads the spatiotemporal modelling lab. He co-initiated openEO, an open source software ecosystem around a language neutral API for analyzing very large data cubes and image collections.

Roger Bivand is a geographer, emeritus professor of the Department of Economics of the Norwegian School of Economics, Bergen, Norway, has worked with spatial autocorrelation since the 1970's, and is a Fellow of the Spatial Econometrics Association.

Edzer and Roger have actively interacted with the open source geospatial user and developer communities since the last century. They author and maintain a number of key R packages for the handling and analysis of spatial and spatiotemporal data, including sf, stars, s2, sp, and gstat, spdep, spatialreg and rgrass. Both are ordinary members of the R foundation.

Spatial Data Science

Beschreibung

Details

Reihe

Über den/die AutorIn

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