2012
DOI: 10.1111/j.1475-5661.2012.00517.x
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The demographic impacts of the Irish famine: towards a greater geographical understanding

Abstract: The Irish famine of the 1840s had a dramatic effect both on the population within Ireland and the populations of countries such as the US, the UK and Australia, which received the bulk of the Irish diaspora resulting from the famine (Kenny 2003). As such, the effects of the famine have been examined extensively across a range of disciplines. It is therefore a challenge to provide any new perspective on this well-researched area. However, this paper provides novel insights into the spatial effects of the famine… Show more

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Cited by 44 publications
(37 citation statements)
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“…To explain relationships between the turnout and the explanatory census variables we adopted an established spatial statistical data analysis methodology (Fotheringham et al 2012), which consists of the following steps: 1) literature-based variable selection, 2) data-driven model optimisation and 3) a calibration and interpretation of the best possible global and local models. In step 1), we selected fourteen potential explanatory variables based on electoral geography literature and in step 2) reduced their number to eight using correlation analysis and model quality optimisation.…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…To explain relationships between the turnout and the explanatory census variables we adopted an established spatial statistical data analysis methodology (Fotheringham et al 2012), which consists of the following steps: 1) literature-based variable selection, 2) data-driven model optimisation and 3) a calibration and interpretation of the best possible global and local models. In step 1), we selected fourteen potential explanatory variables based on electoral geography literature and in step 2) reduced their number to eight using correlation analysis and model quality optimisation.…”
Section: Methodsmentioning
confidence: 99%
“…In local modelling it is used to compare the quality of the global vs. local models (Fotheringham et al 2002), but it can be used to compare any two models as long as they are calibrated on the same data set. This is done by inspecting the value of this criterion: the lower the value, the better the model quality (Fotheringham et al 2012). …”
Section: Step 2: Model Quality Optimisation and Variable Set Reductionmentioning
confidence: 99%
“…Mansley and Demšar, 2015, Brunsdon et al, 1996, Fotheringham et al, 2013, as well as physical geography and ecology (e.g. Atkinson et al, 2003, Clement et al, 2009, Harris et al, 2010, Jetz et al, 2005, proving the suitability of this tool to provide an explanatory approach in spatially varying relationships (Páez et al, 2011).…”
Section: Global Vs Local Regression Modelmentioning
confidence: 99%
“…Mokyr interpreted the outcome of his own research as casting 'serious doubt on the simple and easy explanation that blames Irish poverty on excess population'. His work elicited widespread reaction and further econometric analyses (Mokyr, 1985;McGregor, 1989;Solar, 1989Solar, , 2017O'Rourke, 1991;Fotheringham et al, 2013;Goodspeed, 2016;Kelly andÓ Gráda, 2015). In this paper we addressed the issue anew, using new data and new variables.…”
Section: That Stark Malthusian Interpretation Of Irish Backwardness Omentioning
confidence: 99%