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We are in an age of big data where all of our everyday interactions
and transactions generate data. Much of this data is spatial – it
is collected some-where – and identifying analytical
insight from trends and patterns in these increasing rich digital
footprints presents a number of challenges. Whilst other books
describe different flavours of Data Analytics in R and other
programming languages, there are none that consider Spatial
Data (i.e. the location attached to data), or that consider
issues of inference, linking Big Data, Geography, GIS, Mapping and
Spatial Analytics. This is a ‘learning by doing’
textbook, building on the previous book by the same
authors, An Introduction to R for Spatial Analysis and
Mapping. It details the theoretical issues in analyses of Big
Spatial Data and developing practical skills in the reader for
addressing these with confidence.
We are in an age of big data where all of our everyday interactions
and transactions generate data. Much of this data is spatial - it
is collected some-where - and identifying analytical insight from
trends and patterns in these increasing rich digital footprints
presents a number of challenges. Whilst other books describe
different flavours of Data Analytics in R and other programming
languages, there are none that consider Spatial Data (i.e. the
location attached to data), or that consider issues of inference,
linking Big Data, Geography, GIS, Mapping and Spatial Analytics.
This is a 'learning by doing' textbook, building on the previous
book by the same authors, An Introduction to R for Spatial Analysis
and Mapping. It details the theoretical issues in analyses of Big
Spatial Data and developing practical skills in the reader for
addressing these with confidence.
This is a new edition of the accessible and student-friendly 'how
to' for anyone using R for the first time, for use in spatial
statistical analysis, geocomputation and digital mapping. The
authors, once again, take readers from 'zero to hero', updating the
now standard text to further enable practical R applications in
GIS, spatial analyses, spatial statistics, web-scraping and more.
Revised and updated, each chapter includes: example data and
commands to explore hands-on; scripts and coding to exemplify
specific functionality; self-contained exercises for students to
work through; embedded code within the descriptive text. The new
edition includes detailed discussion of new and emerging packages
within R like sf, ggplot, tmap, making it the go to introduction
for all researchers collecting and using data with location
attached. This is the introduction to the use of R for spatial
statistical analysis, geocomputation, and GIS for all researchers -
regardless of discipline - collecting and using data with location
attached.
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