Spatial econometric methods in agricultural economics using R
Author(s)
Bibliographic Information
Spatial econometric methods in agricultural economics using R
(A Science Publishers book)
CRC Press, 2022
- : pbk
Available at 1 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical references and index
Description and Table of Contents
Description
- Analyses real data sets from start to conclusion.
- Includes an extensive set of examples of the use of R to construct graphs and maps and to model and analyze spatial data.
- Provides background information on exploratory and graphical data analysis and on spatial econometrics methods.
- Lists the possible types of spatial data used to analyze and model agriculture economics phenomena (and offers several codes for each example in the R software environment).
- Presents the methods of spatial data analysis and of spatial econometric modeling appropriate for each agricultural data type.
- Examines how each spatial data type can be used to explore spatial structures and how the spatial effects can be properly added to agricultural economics models.
- Outlines methods for model estimation when data is not available for the whole population but for a sample survey.
- Illustrates the simplest and more sophisticated methods both to convert data from one type to another and to integrate different spatial data sources.
Table of Contents
1. Basic Concepts 2. Spatial Sampling Designs 3. Including Spatial Information in Estimation from Complex Survey Data 4. Yield Prediction in Agriculture: A Comparison Between Regression Kriging and Random Forest 5. Land Cover/Use Analysis and Modelling 6. Statistical Systems in Agriculture 7. Exploring Spatial Point Patterns in Agriculture 8. Spatial Analysis of Farm Data 9. Spatial Econometric Modelling of Farm Data 10. Areal Interpolation Methods: The Bayesian Interpolation Method 11. Small Area Estimation of Agricultural Data 12. Cross-sectional Spatial Regression Models for Measuring Agricultural -convergence 13. Spatial Panel Regression Models in Agriculture
by "Nielsen BookData"