Applied spatial statistics and econometrics : data analysis in R / Katarzyna Kopczewska, [editor].

Contributor(s):

Kopczewska, Katarzyna [editor.]

Material type: Text

TextSeries: Routledge advanced texts in economics and financePublisher: Milton Park, Abingdon, Oxon ; New York, NY : Routledge Taylor & Francis Group, 2021Description: xxv,592 p. ill. ; 28 cmContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9781003033219

Subject(s):

Additional physical formats: Print version:: Applied spatial statistics and econometricsDDC classification:

519.535 22 APP

LOC classification:

HA30.6

Summary: "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 showcase 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"-- Provided by publisher.

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Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Books	Central Library, SUST General Stacks	519.535 APP (Browse shelf(Opens below))	1	Available		0077565
Books	Central Library, SUST General Stacks	519.535 APP (Browse shelf(Opens below))	2	Available		0078838

Includes bibliographical references and index.

"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 showcase 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"-- Provided by publisher.

Description based on print version record.

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