Overcoming data barriers in spatial agri‐food systems analysis: A flexible imputation framework Article Swipe
Jing Yi
,
S Cohen
,
Sarah Rehkamp
,
Patrick Canning
,
Miguel I. Gómez
,
Houtian Ge
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1111/1477-9552.12523
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1111/1477-9552.12523
Suppressions in public data severely limit the usefulness of spatial data and hinder research applications. In this context, data imputation is necessary to deal with suppressed values. We present and validate a flexible data imputation method that can aid in the completion of under‐determined data systems. The validations use Monte Carlo and optimisation modelling techniques to recover suppressed data tables from the 2017 US Census of Agriculture. We then use econometric models to evaluate the accuracy of imputations from alternative models. Various metrics of forecast accuracy (i.e., MAPE, BIC, etc.) show the flexibility and capacity of this approach to accurately recover suppressed data. To illustrate the value of our method, we compare the livestock water withdrawal estimations with imputed data and suppressed data to show the bias in research applications when suppressions are simply dropped from analysis.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/1477-9552.12523
- OA Status
- hybrid
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- 3
- References
- 35
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https://openalex.org/W4313576652Canonical identifier for this work in OpenAlex
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https://doi.org/10.1111/1477-9552.12523Digital Object Identifier
- Title
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Overcoming data barriers in spatial agri‐food systems analysis: A flexible imputation frameworkWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
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2023Year of publication
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2023-01-04Full publication date if available
- Authors
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Jing Yi, S Cohen, Sarah Rehkamp, Patrick Canning, Miguel I. Gómez, Houtian GeList of authors in order
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https://doi.org/10.1111/1477-9552.12523Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1111/1477-9552.12523Direct OA link when available
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Imputation (statistics), Computer science, Monte Carlo method, Data mining, Econometrics, Spatial analysis, Flexibility (engineering), Statistics, Missing data, Mathematics, Machine learningTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2024: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W2110125735, https://openalex.org/W2954095111, https://openalex.org/W2281522813, https://openalex.org/W2329799209, https://openalex.org/W2768117128, https://openalex.org/W1980620608, https://openalex.org/W3132982068, https://openalex.org/W4241914475, https://openalex.org/W2127695568, https://openalex.org/W1486862277, https://openalex.org/W2065992259, https://openalex.org/W2326976677, https://openalex.org/W2483929027, https://openalex.org/W1965017535, https://openalex.org/W2125291718, https://openalex.org/W2119630556, https://openalex.org/W2921479879, https://openalex.org/W2815308567, https://openalex.org/W2053321284, https://openalex.org/W2977558612, https://openalex.org/W1541350805, https://openalex.org/W2007391476, https://openalex.org/W2160455229, https://openalex.org/W1838764073, https://openalex.org/W2790975750, https://openalex.org/W2048804107, https://openalex.org/W1546184848, https://openalex.org/W2502317260, https://openalex.org/W1814500939, https://openalex.org/W3097542903, https://openalex.org/W2126257586, https://openalex.org/W2040682989, https://openalex.org/W2095304671, https://openalex.org/W2800570821, https://openalex.org/W4242387957 |
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| corresponding_author_ids | https://openalex.org/A5052674563 |
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| sustainable_development_goals[0].display_name | Zero hunger |
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