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• W1569653358 abstract "Amara M, Ayadi M. The local geographies of welfare in Tunisia: Does neighbourhood matter? The aim of this article is to show that spatial analysis techniques outperform non-spatial statistical counterparts for understanding the geographic determinants of welfare and poverty in Tunisia. First, an Exploratory Spatial Data Analysis, based on a Geographical Information System, was conducted to visualise the local spatial structure of welfare. Second, a spatial autoregressive (SAR) model and a geographically weighted regression (GWR) model, respectively, were used to deal with both spatial autocorrelations and unobserved spatial heterogeneity of households' behaviours. Spatial and non-spatial models were compared according to their predictive performances. Results of this case study confirm that SAR and GWR spatial models are preferable to the traditional non-spatial regression model and that they give a better approximation of the Tunisian poverty map." @default.
• W1569653358 created "2016-06-24" @default.
• W1569653358 creator A5028479634 @default.
• W1569653358 creator A5039960883 @default.
• W1569653358 date "2012-01-20" @default.
• W1569653358 modified "2023-10-03" @default.
• W1569653358 title "The local geographies of welfare in Tunisia: Does neighbourhood matter?*" @default.
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• W1569653358 doi "https://doi.org/10.1111/j.1468-2397.2011.00863.x" @default.
• W1569653358 hasPublicationYear "2012" @default.
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