Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem
The analysis of census data aggregated by administrative units introduces a statistical bias known as the modifiable areal unit problem (MAUP). Previous researches have mostly assessed the effect of MAUP on upscaling models. The present study contributes to clarify the effects of MAUP on the downsca...
| Main Authors: | , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
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Public Library of Science
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/110662 |
| _version_ | 1855534848027918336 |
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| author | Re, D. da Gilbert, Marius Chaiban, C. Bourguignon, P. Thanapongtharm, Weerapong Robinson, Timothy P. Vanwambeke, S.O. |
| author_browse | Bourguignon, P. Chaiban, C. Gilbert, Marius Re, D. da Robinson, Timothy P. Thanapongtharm, Weerapong Vanwambeke, S.O. |
| author_facet | Re, D. da Gilbert, Marius Chaiban, C. Bourguignon, P. Thanapongtharm, Weerapong Robinson, Timothy P. Vanwambeke, S.O. |
| author_sort | Re, D. da |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The analysis of census data aggregated by administrative units introduces a statistical bias known as the modifiable areal unit problem (MAUP). Previous researches have mostly assessed the effect of MAUP on upscaling models. The present study contributes to clarify the effects of MAUP on the downscaling methodologies, highlighting how a priori choices of scales and shapes could influence the results. We aggregated chicken and duck fine-resolution census in Thailand, using three administrative census levels in regular and irregular shapes. We then disaggregated the data within the Gridded Livestock of the World analytical framework, sampling predictors in two different ways. A sensitivity analysis on Pearson’s r correlation statistics and RMSE was carried out to understand how size and shapes of the response variables affect the goodness-of-fit and downscaling performances. We showed that scale, rather than shapes and sampling methods, affected downscaling precision, suggesting that training the model using the finest administrative level available is preferable. Moreover, datasets showing non-homogeneous distribution but instead spatial clustering seemed less affected by MAUP, yielding higher Pearson’s r values and lower RMSE compared to a more spatially homogenous dataset. Implementing aggregation sensitivity analysis in spatial studies could help to interpret complex results and disseminate robust products. |
| format | Journal Article |
| id | CGSpace110662 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | Public Library of Science |
| publisherStr | Public Library of Science |
| record_format | dspace |
| spelling | CGSpace1106622025-01-24T14:20:17Z Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem Re, D. da Gilbert, Marius Chaiban, C. Bourguignon, P. Thanapongtharm, Weerapong Robinson, Timothy P. Vanwambeke, S.O. livestock data censuses The analysis of census data aggregated by administrative units introduces a statistical bias known as the modifiable areal unit problem (MAUP). Previous researches have mostly assessed the effect of MAUP on upscaling models. The present study contributes to clarify the effects of MAUP on the downscaling methodologies, highlighting how a priori choices of scales and shapes could influence the results. We aggregated chicken and duck fine-resolution census in Thailand, using three administrative census levels in regular and irregular shapes. We then disaggregated the data within the Gridded Livestock of the World analytical framework, sampling predictors in two different ways. A sensitivity analysis on Pearson’s r correlation statistics and RMSE was carried out to understand how size and shapes of the response variables affect the goodness-of-fit and downscaling performances. We showed that scale, rather than shapes and sampling methods, affected downscaling precision, suggesting that training the model using the finest administrative level available is preferable. Moreover, datasets showing non-homogeneous distribution but instead spatial clustering seemed less affected by MAUP, yielding higher Pearson’s r values and lower RMSE compared to a more spatially homogenous dataset. Implementing aggregation sensitivity analysis in spatial studies could help to interpret complex results and disseminate robust products. 2020-01-27 2020-12-29T09:23:08Z 2020-12-29T09:23:08Z Journal Article https://hdl.handle.net/10568/110662 en Open Access Public Library of Science Da Re, D., Gilbert, M., Chaiban, C., Bourguignon, P., Thanapongtharm, W., Robinson, T.P. and Vanwambeke, S.O. 2020. Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem. PLoS ONE 15(1):e0221070. |
| spellingShingle | livestock data censuses Re, D. da Gilbert, Marius Chaiban, C. Bourguignon, P. Thanapongtharm, Weerapong Robinson, Timothy P. Vanwambeke, S.O. Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem |
| title | Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem |
| title_full | Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem |
| title_fullStr | Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem |
| title_full_unstemmed | Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem |
| title_short | Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem |
| title_sort | downscaling livestock census data using multivariate predictive models sensitivity to modifiable areal unit problem |
| topic | livestock data censuses |
| url | https://hdl.handle.net/10568/110662 |
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