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Título del libro: Environmental Modelling And Software For Supporting A Sustainable Future, Proceedings - 8th International Congress On Environmental Modelling And Software, Iemss 2016
Título del capítulo: Assessing modifiable areal unit problem (MAUP) effects in the analysis of deforestation drivers using local models

Autores UNAM:
JEAN FRANCOIS RAYMOND MARIE MAS CAUSSEL;

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Autores externos:

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Idioma:

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Año de publicación:
2016

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Palabras clave:

Aggregates; Deforestation; Population statistics; Surveys; Bivariate correlations; Environmental variables; Geographical units; Geographically weighted regression models; Population densities; Socio-economic data; Spatial configuration; Substantial variations; Sustainable development

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Resumen:

In order to assess the role of drivers of deforestation, the rate of deforestation is often compared with environmental and socio-economic variables such as slope, soil suitability or population density. Socio-economic data is obtained from census information collected for individual households but commonly presented in aggregate on the basis of geographical units as counties, municipalities, states or countries. A common problem, known as the modifiable areal unit problem (MAUP) is that the results of statistical analysis are not independent of the scale and the spatial configuration of the units used to aggregate the information. In the present study, we evaluate MAUP effects on the assessment of deforestation drivers in Mexico at municipality level using a modelling approach. We used socio-economic variables from the 2010 population census of Mexico along with environmental variables. As population census is given for each human settlement and environmental variables are obtained from high resolution spatial database, it was possible to aggregate the information using a large number of replicates of spatial aggregating units ("pseudo municipalities") to evaluate the effect of aggregation on the values of global and local bivariate correlations (Pearson's r) between pairs of variables and the local parameters of a Geographically Weighted Regression (GWR) model. We found that MAUP can produce substantial variations of global and local statistics such as correlation and parameters of GWR models. © Environmental Modelling and Software for Supporting a Sustainable Future, Proceedings - 8th International Congress on Environmental Modelling and Software, iEMSs 2016. All rights reserved.

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Entidades citadas de la UNAM:

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Fuente:


ISBN:
9788890357459
Editorial:
International Environmental Modelling and Software Society (iEMSs)

Tipo de producto:
Conference Paper