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.
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