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Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem
22/02/21 08:27AM
Da Re, D., Gilbert, M., Chaiban, C., Bourguignon, P., Thanapongtharm, W., Robinson, T.P. and Vanwambeke, S.O./. 2020./. PLoS ONE 15(1):e0221070.
Abstract

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 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 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.

Permanent link to cite or share this item: https://hdl.handle.net/10568/110662
DOI: http://dx.doi.org/10.1371/journal.pone.0221070


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