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The research and its outcomes presented here focus on spatial
sampling of agricultural resources. The authors introduce sampling
designs and methods for producing accurate estimates of crop
production for harvests across different regions and countries.
With the help of real and simulated examples performed with the
open-source software R, readers will learn about the different
phases of spatial data collection. The agricultural data analyzed
in this book help policymakers and market stakeholders to monitor
the production of agricultural goods and its effects on environment
and food safety.
- 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.
The research and its outcomes presented here focus on spatial
sampling of agricultural resources. The authors introduce sampling
designs and methods for producing accurate estimates of crop
production for harvests across different regions and countries.
With the help of real and simulated examples performed with
the open-source software R, readers will learn about the
different phases of spatial data collection. The agricultural data
analyzed in this book help policymakers and market stakeholders to
monitor the production of agricultural goods and its effects on
environment and food safety.
- 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.
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Kristen Stewart, Nora von Waldstätten, …
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