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.
Author(s): Roberto Benedetti, Federica Piersimoni, Paolo Postiglione
Series: Advances in Spatial Science
Publisher: Springer
Year: 2015
Language: English
Pages: C, XVIII, 325
Cover
Advances in Spatial Science
Sampling Spatial Units for Agricultural Surveys
Copyright
© Springer-Verlag Berlin Heidelberg 2015
ISSN 1430-9602
ISSN 2197-9375 (electronic)
ISBN 978-3-662-46007-8
ISBN 978-3-662-46008-5 (eBook)
DOI 10.1007/978-3-662-46008-5
Library of Congress Control Number: 2015933847
Dedication
Preface
Outline of the Book
Acknowledgments
Proem
Contents
Chapter 1: Essential Statistical Concepts, Definitions, and Terminology
1.1 Introduction
1.2 Sampling from Finite Populations
1.3 The Predictive Approach: The Concept of Superpopulations
1.4 Statistics for Spatial Data
1.4.1 Types of Spatial Data
1.4.2 Spatial Dependence
1.4.3 Statistical Model for Spatial Data
1.4.3.1 Geostatistics
1.4.3.2 Lattice Data Analysis
1.4.3.3 Spatial Point Pattern Analysis
Conclusions
References
Chapter 2: Overview and Brief History
2.1 Introduction
2.2 The Use of Spatial Units When Sampling Natural and Environmental Resources
2.3 Examples of Agricultural Surveys Based on Spatial Reference Frames
2.3.1 JAS
2.3.2 LUCAS
2.3.3 AGRIT
2.3.4 TER-UTI
Conclusions
References
Chapter 3: GIS: The Essentials
3.1 Introduction
3.2 Introduction to GIS Concepts and Data Models
3.3 Spatial Analysis of GIS Data
3.4 GRASS: An Open Source GIS
Conclusions
References
Chapter 4: An Introduction to Remotely Sensed Data Analysis
4.1 Introduction
4.2 Basic Concepts
4.3 Geometric and Radiometric Corrections
4.4 Image Enhancement
4.5 Multispectral Transformations
4.6 The Thematic Extraction of Information
4.6.1 Unsupervised Classification
4.6.2 Supervised Classification
4.6.3 The Contextual Approach to the Thematic Extraction of Information
4.7 GRASS for Analyzing Remotely Sensed Images
Conclusion
References
Chapter 5: Setting Up the Frame
5.1 Introduction
5.2 Choice of the Statistical Unit
5.3 Main Advantages and Disadvantages of Different Frames Typologies
5.4 Frame Construction
Conclusions
References
Chapter 6: Sampling Designs
6.1 Introduction
6.2 Simple Random Sampling
6.3 Systematic Sampling
6.4 Unequal Selection Probabilities
6.5 Stratified Sampling
6.6 Multi-stage Sampling
6.7 Multi-phase Sampling
6.8 Sample Coordination and Longitudinal Surveys
6.9 Ranked Set Sampling
6.10 Adaptive Sampling
6.11 Cut-Off Sampling
Conclusions
References
Chapter 7: Spatial Sampling Designs
7.1 Introduction
7.2 Some Motivations for Spreading the Sample
7.3 Sampling Plans that Exclude Adjacent Units
7.4 Generalized Random Tessellation Sampling
7.5 The Balanced Sampling and Cube Method
7.6 Selection Methods Based on the Distance Between Statistical Units
7.7 Numerical Evaluation of the Inclusion Probabilities
7.8 Empirical Exercises
7.8.1 Simulated Populations
7.8.2 A Case Study: Assessing the Ecological Condition of Lakes in Northeastern USA
Conclusions
References
Chapter 8: Sample Size and Sample Allocation
8.1 Introduction
8.2 Sample Size Estimation for Simple Random Sampling
8.3 Sample Size Estimation for Stratified Sampling
8.3.1 Proportional Allocation
8.3.2 Optimal Allocation
8.4 The Multipurpose Allocation Problem
8.4.1 Computational Aspects
8.5 Modeling Auxiliary and Survey Variables: The Anticipated Moment Approach
Conclusions
References
Chapter 9: Survey Data Collection and Processing
9.1 Introduction
9.2 Questionnaire Design
9.3 Data Collection, Instruction Manual, Training of Enumerators, and Field Work Management
9.4 Data Editing
9.5 Quality Assurance
Conclusions
References
Chapter 10: Advances in Sampling Estimation
10.1 Introduction
10.2 Using Auxiliary Information to Improve the Estimation
10.3 Calibration Estimator
10.4 Adjusting for Nonresponses
10.5 Variance Estimation
10.6 Multiple Frames
Conclusions
References
Chapter 11: Small Area Estimation
11.1 Introduction
11.2 Direct and Indirect Estimation Methods
11.3 Small Area Models
11.3.1 Area Level Models
11.3.2 Unit Level Models
11.3.3 Generalized Linear Mixed Models
11.4 Estimation for Small Area Models
11.5 The Spatially Augmented Approach to Small Area Estimation
11.6 The Benchmarking Problem
Conclusions
References
Chapter 12: Spatial Survey Data Modeling
12.1 Introduction
12.2 Model-Based Inference for Finite Populations
12.3 Spatial Interpolation as a Predictive Approach for Finite Populations
12.4 Analysis of Spatial Survey Data
Conclusions
References
