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E-raamat: Sampling Spatial Units for Agricultural Surveys

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  • Formaat: PDF+DRM
  • Sari: Advances in Spatial Science
  • Ilmumisaeg: 20-Mar-2015
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Keel: eng
  • ISBN-13: 9783662460085
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Sampling Spatial Units for Agricultural SurveysSuurem pilt
  • Formaat: PDF+DRM
  • Sari: Advances in Spatial Science
  • Ilmumisaeg: 20-Mar-2015
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Keel: eng
  • ISBN-13: 9783662460085
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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.

Arvustused

This monograph presents a contemporary study of sample surveys by geographically distributed data in the agricultural sector. Each chapter contains many dozen references up to the most recent sources. The monograph can be very helpful for lecturers, graduate students, and researchers using survey methods in general, and particularly in spatial agricultural studies. (Stan Lipovetsky, Technometrics, Vol. 59 (1), February, 2017)

This book is a meticulously organized treatise of applying spatial data methods to sample surveys (primarily in agriculture), with the computational engine powered by the R software. It is mainly an intermediate-level reference book for graduate students and (agricultural) researchers to get introduced to the nuances of spatial statistics in survey sampling, and quickly move to hands-on computing. If you are enamoured withthe versatility of R, I highly recommend buying it. (Dipankar Bandyopadhyay, Journal of Statistical Software, Vol. 6, February, 2016)

1 Essential Statistical Concepts, Definitions, and Terminology
1(36)
1.1 Introduction
1(2)
1.2 Sampling from Finite Populations
3(11)
1.3 The Predictive Approach: The Concept of Superpopulations
14(1)
1.4 Statistics for Spatial Data
15(22)
1.4.1 Types of Spatial Data
16(1)
1.4.2 Spatial Dependence
17(2)
1.4.3 Statistical Model for Spatial Data
19(15)
Conclusions
34(1)
References
35(2)
2 Overview and Brief History
37(12)
2.1 Introduction
37(1)
2.2 The Use of Spatial Units When Sampling Natural and Environmental Resources
38(2)
2.3 Examples of Agricultural Surveys Based on Spatial Reference Frames
40(9)
2.3.1 JAS
40(2)
2.3.2 LUCAS
42(2)
2.3.3 AGRIT
44(1)
2.3.4 TER-UTI
45(2)
Conclusions
47(1)
References
47(2)
3 GIS: The Essentials
49(14)
3.1 Introduction
49(2)
3.2 Introduction to GIS Concepts and Data Models
51(3)
3.3 Spatial Analysis of GIS Data
54(5)
3.4 GRASS: An Open Source GIS
59(4)
Conclusions
61(1)
References
62(1)
4 An Introduction to Remotely Sensed Data Analysis
63(28)
4.1 Introduction
63(1)
4.2 Basic Concepts
64(6)
4.3 Geometric and Radiometric Corrections
70(5)
4.4 Image Enhancement
75(2)
4.5 Multispectral Transformations
77(2)
4.6 The Thematic Extraction of Information
79(7)
4.6.1 Unsupervised Classification
80(3)
4.6.2 Supervised Classification
83(2)
4.6.3 The Contextual Approach to the Thematic Extraction of Information
85(1)
4.7 GRASS for Analyzing Remotely Sensed Images
86(5)
Conclusion
88(1)
References
89(2)
5 Setting Up the Frame
91(12)
5.1 Introduction
91(1)
5.2 Choice of the Statistical Unit
92(2)
5.3 Main Advantages and Disadvantages of Different Frames Typologies
94(2)
5.4 Frame Construction
96(7)
Conclusions
98(2)
References
100(3)
6 Sampling Designs
103(46)
6.1 Introduction
103(1)
6.2 Simple Random Sampling
104(6)
6.3 Systematic Sampling
110(3)
6.4 Unequal Selection Probabilities
113(6)
6.5 Stratified Sampling
119(7)
6.6 Multi-stage Sampling
126(5)
6.7 Multi-phase Sampling
131(4)
6.8 Sample Coordination and Longitudinal Surveys
135(4)
6.9 Ranked Set Sampling
139(1)
6.10 Adaptive Sampling
140(2)
6.11 Cut-Off Sampling
142(7)
Conclusions
144(1)
References
145(4)
7 Spatial Sampling Designs
149(48)
7.1 Introduction
149(3)
7.2 Some Motivations for Spreading the Sample
152(2)
7.3 Sampling Plans that Exclude Adjacent Units
154(2)
7.4 Generalized Random Tessellation Sampling
156(7)
7.5 The Balanced Sampling and Cube Method
163(11)
7.6 Selection Methods Based on the Distance Between Statistical Units
174(9)
7.7 Numerical Evaluation of the Inclusion Probabilities
183(2)
7.8 Empirical Exercises
185(12)
7.8.1 Simulated Populations
185(5)
7.8.2 A Case Study: Assessing the Ecological Condition of Lakes in Northeastern USA
190(3)
Conclusions
193(1)
References
194(3)
8 Sample Size and Sample Allocation
197(22)
8.1 Introduction
197(2)
8.2 Sample Size Estimation for Simple Random Sampling
199(4)
8.3 Sample Size Estimation for Stratified Sampling
203(5)
8.3.1 Proportional Allocation
204(1)
8.3.2 Optimal Allocation
205(3)
8.4 The Multipurpose Allocation Problem
208(5)
8.4.1 Computational Aspects
210(3)
8.5 Modeling Auxiliary and Survey Variables: The Anticipated Moment Approach
213(6)
Conclusions
216(1)
References
217(2)
9 Survey Data Collection and Processing
219(20)
9.1 Introduction
219(1)
9.2 Questionnaire Design
220(3)
9.3 Data Collection, Instruction Manual, Training of Enumerators, and Field Work Management
223(4)
9.4 Data Editing
227(5)
9.5 Quality Assurance
232(7)
Conclusions
235(1)
References
236(3)
10 Advances in Sampling Estimation
239(32)
10.1 Introduction
239(2)
10.2 Using Auxiliary Information to Improve the Estimation
241(5)
10.3 Calibration Estimator
246(7)
10.4 Adjusting for Nonresponses
253(4)
10.5 Variance Estimation
257(7)
10.6 Multiple Frames
264(7)
Conclusions
268(1)
References
269(2)
11 Small Area Estimation
271(34)
11.1 Introduction
271(3)
11.2 Direct and Indirect Estimation Methods
274(10)
11.3 Small Area Models
284(4)
11.3.1 Area Level Models
284(2)
11.3.2 Unit Level Models
286(1)
11.3.3 Generalized Linear Mixed Models
287(1)
11.4 Estimation for Small Area Models
288(8)
11.5 The Spatially Augmented Approach to Small Area Estimation
296(4)
11.6 The Benchmarking Problem
300(5)
Conclusions
301(1)
References
302(3)
12 Spatial Survey Data Modeling
305
12.1 Introduction
305(1)
12.2 Model-Based Inference for Finite Populations
306(5)
12.3 Spatial Interpolation as a Predictive Approach for Finite Populations
311(2)
12.4 Analysis of Spatial Survey Data
313
Conclusions
324(1)
References
325
Roberto Benedetti is Professor of Economic Statistics at University of Chieti-Pescara (Italy). He was visiting researcher at the National Centre for Geographic Information Analysis of the University of California at Santa Barbara, at Regional Economics Applications Laboratory of University of Illinois at Urbana-Champaign, at Centre for Statistical and Survey Methodology of University of Wollongong, and received a Ph.D. in Methodological Statistics from the University of Rome in 1994. From 1994 to 2001, he held positions at the Italian National Statistical Institute as the head of the Agricultural Statistics Service. His current research interests focus on agricultural statistics, sample design, small area estimation, and spatial data analysis.

Federica Piersimoni is Senior Researcher at Agricultural Statistics Service within the Department of Economic Statistics of Italian National Statistical Institute from 1996. She was visiting researcher at Regional EconomicsApplications Laboratory of University of Illinois at Urbana-Champaign, and at Centre for Statistical and Survey Methodology of University of Wollongong. In 2014 she received a Ph.D. in Economics and Statistics from the University of Chieti - Pescara. Her main research interests concern disclosure control, sample design, and agricultural statistics.

Paolo Postiglione is Associate Professor of Economic Statistics at University of Chieti-Pescara (Italy). He was visiting researcher at Regional Economics Applications Laboratory of University of Illinois at Urbana-Champaign, at Regional Research Institute of West Virginia University, and received a Ph.D. in Statistics from the University of Chieti - Pescara in 1998. From 1996 to 2001 he was Statistical Executive at Ministry of Transports and Navigation in staff position of the Head of Human Resources. His current research interests focus on spatial statistics and econometrics, spatial sampling, regional economic convergence, models for spatial non-stationary data, and agricultural statistics.
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