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

Contributor(s): Material type: TextTextSeries: Routledge advanced texts in economics and financePublisher: Milton Park, Abingdon, Oxon ; New York, NY : Routledge, 2021Description: xxv, 593 pages : illustrations ; 30 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9780367470760
Subject(s): Genre/Form: Additional physical formats: Online version:: Applied spatial statistics and econometricsDDC classification:
  • 519.535 22 APP
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 Collection Call number Vol info Status Date due Barcode Item holds
Book - Borrowing Book - Borrowing Central Library First floor Alahram 519.535 APP (Browse shelf(Opens below)) 282 Available 000056200
Total holds: 0

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.

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