Chapter On the use of auxiliary information in spatial sampling

Technology development has led to a growing availability of low-cost data ready-to-use, frequently derived from large scale observations (i.e. data from pervasive systems like GPS sensors, or remote sensing data from earth observation technologies). Oftentimes, these data can’t directly answer speci...

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Main Authors: Bocci, Chiara, ROCCO, EMILIA
פורמט: Online
שפה:אנגלית
יצא לאור: Firenze University Press, Genova University Press 2023
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גישה מקוונת:ONIX_20230803_9791221501063_95
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author Bocci, Chiara
ROCCO, EMILIA
author_browse Bocci, Chiara
ROCCO, EMILIA
author_facet Bocci, Chiara
ROCCO, EMILIA
author_sort Bocci, Chiara
collection Directory of Open Access Books
description Technology development has led to a growing availability of low-cost data ready-to-use, frequently derived from large scale observations (i.e. data from pervasive systems like GPS sensors, or remote sensing data from earth observation technologies). Oftentimes, these data can’t directly answer specific questions posed by researchers and data users, or even if they can they are subject to measurement errors or self-selection bias. In both cases it is still necessary to rely, at least partially, on ad-hoc probabilistic surveys. On the other hand, the precision and quality of surveys estimates can be improved by using the data derived from these new sources as auxiliary information in the design phase and/or in the estimation phase. We present a sequential sampling strategy, suitable to investigate a spatially-related phenomenon, which exploits the auxiliary information at design level in order to obtain efficient estimates when the relation between the auxiliary and study variables it is not completely known and/or is not univocally defined for the whole population under study. Using this strategy the final sample is obtained after two (or more) steps: (i) in the first step we collect an initial sample of observations on the target variable, which is used also to investigate the relation between the auxiliary and study variables; (ii) then, this relation is exploited to target and tailor the subsequent sampling step; (iii) additional steps can be included by applying the procedure iteratively. The performance of the suggested strategy is investigated through Monte Carlo experiments by considering several scenarios, which differ in the distributions of the auxiliary and study variables and in their relation.
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spelling doab-20.500.12854ir-1121532025-07-17T10:01:37Z Chapter On the use of auxiliary information in spatial sampling Bocci, Chiara ROCCO, EMILIA Probabilistic survey data Sampling allocation strategies Spatial data thema EDItEUR::J Society and Social Sciences thema EDItEUR::J Society and Social Sciences Technology development has led to a growing availability of low-cost data ready-to-use, frequently derived from large scale observations (i.e. data from pervasive systems like GPS sensors, or remote sensing data from earth observation technologies). Oftentimes, these data can’t directly answer specific questions posed by researchers and data users, or even if they can they are subject to measurement errors or self-selection bias. In both cases it is still necessary to rely, at least partially, on ad-hoc probabilistic surveys. On the other hand, the precision and quality of surveys estimates can be improved by using the data derived from these new sources as auxiliary information in the design phase and/or in the estimation phase. We present a sequential sampling strategy, suitable to investigate a spatially-related phenomenon, which exploits the auxiliary information at design level in order to obtain efficient estimates when the relation between the auxiliary and study variables it is not completely known and/or is not univocally defined for the whole population under study. Using this strategy the final sample is obtained after two (or more) steps: (i) in the first step we collect an initial sample of observations on the target variable, which is used also to investigate the relation between the auxiliary and study variables; (ii) then, this relation is exploited to target and tailor the subsequent sampling step; (iii) additional steps can be included by applying the procedure iteratively. The performance of the suggested strategy is investigated through Monte Carlo experiments by considering several scenarios, which differ in the distributions of the auxiliary and study variables and in their relation. 2023-08-08T05:38:44Z 2023-08-08T05:38:44Z 2023-08-03T15:06:07Z 2023 chapter ONIX_20230803_9791221501063_95 2704-5846 https://library.oapen.org/handle/20.500.12657/74899 9791221501063 https://directory.doabooks.org/handle/20.500.12854/112153 eng Proceedings e report open access image/png image/jpeg Attribution 4.0 International Attribution 4.0 International https://library.oapen.org/bitstream/20.500.12657/74899/1/9791221501063-27.pdf https://library.oapen.org/bitstream/20.500.12657/74899/1/9791221501063-27.pdf Firenze University Press, Genova University Press 10.36253/979-12-215-0106-3.27 10.36253/979-12-215-0106-3.27 74113d79-2268-4658-88bb-6e8757c543b0 ASA 2022 Data-Driven Decision Making 9791221501063 6 Florence open access
spellingShingle Probabilistic survey data
Sampling allocation strategies
Spatial data
thema EDItEUR::J Society and Social Sciences
thema EDItEUR::J Society and Social Sciences
Bocci, Chiara
ROCCO, EMILIA
Chapter On the use of auxiliary information in spatial sampling
title Chapter On the use of auxiliary information in spatial sampling
title_full Chapter On the use of auxiliary information in spatial sampling
title_fullStr Chapter On the use of auxiliary information in spatial sampling
title_full_unstemmed Chapter On the use of auxiliary information in spatial sampling
title_short Chapter On the use of auxiliary information in spatial sampling
title_sort chapter on the use of auxiliary information in spatial sampling
topic Probabilistic survey data
Sampling allocation strategies
Spatial data
thema EDItEUR::J Society and Social Sciences
thema EDItEUR::J Society and Social Sciences
topic_facet Probabilistic survey data
Sampling allocation strategies
Spatial data
thema EDItEUR::J Society and Social Sciences
thema EDItEUR::J Society and Social Sciences
url ONIX_20230803_9791221501063_95
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