Well-spread samples with dynamic sample sizes
© The Author(s) 2024. Published by Oxford University Press on behalf of The International Biometric Society..
A spatial sampling design determines where sample locations are placed in a study area so that population parameters can be estimated with relatively high precision. If the response variable has spatial trends, spatially balanced or well-spread designs give precise results for commonly used estimators. This article proposes a new method that draws well-spread samples over arbitrary auxiliary spaces and can be used for master sampling applications. All we require is a measure of the distance between population units. Numerical results show that the method generates well-spread samples and compares favorably with existing designs. We provide an example application using several auxiliary variables to estimate total aboveground biomass over a large study area in Eastern Amazonia, Brazil. Multipurpose surveys are also considered, where the totals of aboveground biomass, primary production, and clay content (3 responses) are estimated from a single well-spread sample over the auxiliary space.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:80 |
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| Enthalten in: |
Biometrics - 80(2024), 2 vom: 27. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Robertson, Blair [VerfasserIn] |
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| Links: |
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| Themen: |
Environmental sampling |
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| Anmerkungen: |
Date Completed 10.04.2024 Date Revised 10.04.2024 published: Print Citation Status MEDLINE |
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| doi: |
10.1093/biomtc/ujae026 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a A spatial sampling design determines where sample locations are placed in a study area so that population parameters can be estimated with relatively high precision. If the response variable has spatial trends, spatially balanced or well-spread designs give precise results for commonly used estimators. This article proposes a new method that draws well-spread samples over arbitrary auxiliary spaces and can be used for master sampling applications. All we require is a measure of the distance between population units. Numerical results show that the method generates well-spread samples and compares favorably with existing designs. We provide an example application using several auxiliary variables to estimate total aboveground biomass over a large study area in Eastern Amazonia, Brazil. Multipurpose surveys are also considered, where the totals of aboveground biomass, primary production, and clay content (3 responses) are estimated from a single well-spread sample over the auxiliary space | ||
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