Applied spatial statistics and econometrics: data analysis in R

By:

Kopczewska, Katarzyna

Material type: Text

TextPublication details: Routledge London 2021Description: xxv, 593 pISBN:

9780367470760

Subject(s):

DDC classification:

519.535 KOP

Summary: Book Description 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.

List(s) this item appears in: Operation & quantitative Techniques | HR & OB

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	519.535 KOP (Browse shelf(Opens below))	1	Available		004202

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Previous	519.535 DUN Principal components analysis	519.535 HAR Applied multivariate statistical analysis	519.535 KLI Principles and practice of structural equation modeling	519.535 KOP Applied spatial statistics and econometrics:	519.535 TAB Using multivariate statistics	519.5354 PAG Multiple factor analysis by example using R	519.536 ALL Fixed effects regression models	Next

Book Description
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.

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