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This textbook is a comprehensive introduction to applied spatial
data analysis using R. Each chapter walks the reader through a
different method, explaining how to interpret the results and what
conclusions can be drawn. The author team showcases key topics,
including unsupervised learning, causal inference, spatial weight
matrices, spatial econometrics, heterogeneity and bootstrapping. It
is accompanied by a suite of data and R code on Github to help
readers practise techniques via replication and exercises. This
text will be a valuable resource for advanced students of
econometrics, spatial planning and regional science. It will also
be suitable for researchers and data scientists working with
spatial data.
This textbook is a comprehensive introduction to applied spatial
data analysis using R. Each chapter walks the reader through a
different method, explaining how to interpret the results and what
conclusions can be drawn. The author team showcases key topics,
including unsupervised learning, causal inference, spatial weight
matrices, spatial econometrics, heterogeneity and bootstrapping. It
is accompanied by a suite of data and R code on Github to help
readers practise techniques via replication and exercises. This
text will be a valuable resource for advanced students of
econometrics, spatial planning and regional science. It will also
be suitable for researchers and data scientists working with
spatial data.
This book explores statistical models in regional specialization,
presenting a brand new measure. It begins by reviewing existing
indicators and models of regional specialization before outlining a
newly created, spatially embedded model of specialization based on
the spatial distribution of firms. It addresses the various
applications of the model, and how the model can be used in
regional policy.
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