Dataset Information

Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem.

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 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.

SUBMITTER: Da Re D

PROVIDER: S-EPMC6984718 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem.

Da Re Daniele D Gilbert Marius M Chaiban Celia C Bourguignon Pierre P Thanapongtharm Weerapong W Robinson Timothy P TP Vanwambeke Sophie O SO

PloS one 20200127 1

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 ce ...[more]

PMID: 31986146

Similar Datasets

Project description:BackgroundAll analyses of spatially aggregated data are vulnerable to the modifiable areal unit problem (MAUP), which describes the sensitivity of analytical results to the arbitrary choice of spatial aggregation unit at which data are measured. The MAUP is a serious problem endemic to analyses of spatially aggregated data in all scientific disciplines. However, the impact of the MAUP is rarely considered, perhaps partly because it is still widely considered to be unsolvable.ResultsIt was originally understood that a solution to the MAUP should constitute a comprehensive statistical framework describing the regularities in estimates of association observed at different combinations of spatial scale and zonation. Additionally, it has been debated how such a solution should incorporate the geographical characteristics of areal units (e.g. shape, size, and configuration), and in particular whether this can be achieved in a purely mathematical framework (i.e. independent of areal units). We argue that the consideration of areal units must form part of a solution to the MAUP, since the MAUP only manifests in their presence. Thus, we present a theoretical and statistical framework that incorporates the characteristics of areal units by combining estimates obtained from different scales and zonations. We show that associations estimated at scales larger than a minimal geographical unit of analysis are systematically biased from a true minimal-level effect, with different zonations generating uniquely biased estimates. Therefore, it is fundamentally erroneous to infer conclusions based on data that are spatially aggregated beyond the minimal level. Instead, researchers should measure and display information, estimate effects, and infer conclusions at the smallest possible meaningful geographical scale. The framework we develop facilitates this.ConclusionsThe proposed framework represents a new minimum standard in the estimation of associations using spatially aggregated data, and a reference point against which previous findings and misconceptions related to the MAUP can be understood.

Project description:Large scale, high-resolution global data on farm animal distributions are essential for spatially explicit assessments of the epidemiological, environmental and socio-economic impacts of the livestock sector. This has been the major motivation behind the development of the Gridded Livestock of the World (GLW) database, which has been extensively used since its first publication in 2007. The database relies on a downscaling methodology whereby census counts of animals in sub-national administrative units are redistributed at the level of grid cells as a function of a series of spatial covariates. The recent upgrade of GLW1 to GLW2 involved automating the processing, improvement of input data, and downscaling at a spatial resolution of 1 km per cell (5 km per cell in the earlier version). The underlying statistical methodology, however, remained unchanged. In this paper, we evaluate new methods to downscale census data with a higher accuracy and increased processing efficiency. Two main factors were evaluated, based on sample census datasets of cattle in Africa and chickens in Asia. First, we implemented and evaluated Random Forest models (RF) instead of stratified regressions. Second, we investigated whether models that predicted the number of animals per rural person (per capita) could provide better downscaled estimates than the previous approach that predicted absolute densities (animals per km2). RF models consistently provided better predictions than the stratified regressions for both continents and species. The benefit of per capita over absolute density models varied according to the species and continent. In addition, different technical options were evaluated to reduce the processing time while maintaining their predictive power. Future GLW runs (GLW 3.0) will apply the new RF methodology with optimized modelling options. The potential benefit of per capita models will need to be further investigated with a better distinction between rural and agricultural populations.

Project description:Species distribution models (SDMs) are statistical tools to identify potentially suitable habitats for species. For SDMs in river ecosystems, species occurrences and predictor data are often aggregated across subcatchments that serve as modeling units. The level of aggregation (i.e., model resolution) influences the statistical relationships between species occurrences and environmental predictors-a phenomenon known as the modifiable area unit problem (MAUP), making model outputs directly contingent on the model resolution. Here, we test how model performance, predictor importance, and the spatial congruence of species predictions depend on the model resolution (i.e., average subcatchment size) of SDMs. We modeled the potential habitat suitability of 50 native fish species in the upper Danube catchment at 10 different model resolutions. Model resolutions were derived using a 90-m digital-elevation model by using the GRASS-GIS module r.watershed. Here, we decreased the average subcatchment size gradually from 632 to 2 km2. We then ran ensemble SDMs based on five algorithms using topographical, climatic, hydrological, and land-use predictors for each species and resolution. Model evaluation scores were consistently high, as sensitivity and True Skill Statistic values ranged from 86.1-93.2 and 0.61-0.73, respectively. The most contributing predictor changed from topography at coarse, to hydrology at fine resolutions. Climate predictors played an intermediate role for all resolutions, while land use was of little importance. Regarding the predicted habitat suitability, we identified a spatial filtering from coarse to intermediate resolutions. The predicted habitat suitability within a coarse resolution was not ported to all smaller, nested subcatchments, but only to a fraction that held the suitable environmental conditions. Across finer resolutions, the mapped predictions were spatially congruent without such filter effect. We show that freshwater SDM predictions can have consistently high evaluation scores while mapped predictions differ significantly and are highly contingent on the underlying subcatchment size. We encourage building freshwater SDMs across multiple catchment sizes, to assess model variability and uncertainties in model outcomes emerging from the MAUP.

Project description:BackgroundA growing number of meta-analyses reviewed the existing associations between modifiable factors and stroke. However, the methodological quality of them and quality of evidence remain to be assessed by validated tools. Thus, this umbrella review was conducted to consolidate evidence from systematic reviews and meta-analyses of cohort studies investigating the association between modifiable factors and incidence of stroke.MethodsPubMed, Web of Science, Embase, Wanfang and China National Knowledge Infrastructure databases for systematic reviews and meta-analyses of cohort studies from inception until March 2021. Assess the methodological quality of systematic reviews 2 was used to evaluate the methodological quality of each included published meta-analysis. Excess significance test was used to investigate whether the observed number of studies (O) with nominally significant results ('positive' studies, p<0.05) was larger than the expected number of significant results (E). Statistically significant (p<0.05) associations were rated into five levels (strong, highly suggestive, suggestive, weak and no) using specific criteria. Sensitivity analyses were performed.Results2478 records were identified through database searching. At last, 49 meta-analyses including 70 modifiable factors and approximately 856 801 stroke cases were included in the present review. The methodological quality of three meta-analyses was low, while others were critically low. Evidence of walking pace was strong. High suggestive evidence mainly included total meat, processes meat, chocolate, sodium, obesity, pulse pressure, systolic blood pressure, diastolic blood pressure, sleep duration and smoking. Suggestive evidence mainly included dietary approaches to stop hypertension (DASH) diet, vitamin C, magnesium, depression and particulate matter 2.5. After sensitivity analyses, evidence of DASH diet, magnesium and depression turned to weak. No publication bias existed, except only one study which could be explained by reporting bias.DiscussionDiet with rich macronutrients and micronutrients, healthy dietary patterns and favourable physical, emotional health and environmental management should be promoted to decrease the burden of stroke.Prospero registration numberCRD42021249921.

Dataset Information

Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem.

Publications

Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets