2014
DOI: 10.1111/rsp3.12028
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Geographically weighted regression analysis of the spatially varying relationship between farming viability and contributing factors in Ohio

Abstract: Economic viability of farming operations determines long term success of US agriculture. This research examines the relationship between farming viability and its contributing factors at county level, using Ohio as an example. An ordinary least square regression (OLS) and a geographically weighted regression (GWR) model are developed to examine the effects of mechanical and biochemical technologies, government payments, product diversity and farm size. The OLS model shows that all factors are globally signific… Show more

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Cited by 7 publications
(3 citation statements)
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“…Second, the GWR model provided a better fit to the data because the Akaike information criterion (AIC) score for the GWR model (1464.46) was lower than the AIC score for the OLS model (1485.71). The 21-point difference between the two AIC scores is well above the generally accepted cutoff of 3 points or greater (Jiang & Xu, 2014). Last, results of the ANOVA test (see Table 2) confirmed that the performance of the GWR model was statistically significantly superior to that of the OLS model.…”
Section: Global Estimates and Model Fitsupporting
confidence: 66%
“…Second, the GWR model provided a better fit to the data because the Akaike information criterion (AIC) score for the GWR model (1464.46) was lower than the AIC score for the OLS model (1485.71). The 21-point difference between the two AIC scores is well above the generally accepted cutoff of 3 points or greater (Jiang & Xu, 2014). Last, results of the ANOVA test (see Table 2) confirmed that the performance of the GWR model was statistically significantly superior to that of the OLS model.…”
Section: Global Estimates and Model Fitsupporting
confidence: 66%
“…An examination of Akaike Information Criterion (AIC) (Akaike, 1974) scores of both models shows that the GWR model provided a better fit to the data because the AIC score of 426.83 was lower than the AIC score for the OLS model (444.93). The 182.1 points difference between the two AIC scores is well above the generally accepted cutoff of 3 points or greater (Fotheringham et al, 2002;Jiang and Xu, 2014). In addition, the Moran's I value on residuals of the OLS model reduces from 0.124 to 0.051 for the GWR model, signifying that the GWR model explains better the relationship between the dependent variable and independents variables.…”
Section: Predictors Of Spatial Pattern Of Tanker Accidentsmentioning
confidence: 66%
“…GWR integrates spatial location factors into regression parameters, permitting the weighting of each data point in the regression based on its proximity, meaning that the nearer points are assigned greater weight [19]. Yet, the conventional GWR model presumes that the optimal bandwidth for all influencing factors is consistent, which hinders the precise depiction of the real spatial processes of soil salinity [20]. In response to this constraint, Fotheringham and others developed the Multi-scale Geographically Weighted Regression (MGWR) model [21].…”
Section: Introductionmentioning
confidence: 99%