Geographically weighted regression and geostatistical techniques to construct the geogenic radon potential map of the Lazio region: A methodological proposal for the European Atlas of Natural Radiation

Ciotoli, Giancarlo; Voltaggio, Mario; Tuccimei, Paola; Soligo, Michele; Pasculli, Antonio; Beaubien, S.E.; Bigi, Sabina

doi:10.1016/j.jenvrad.2016.05.010

Cited by 75 publications

(58 citation statements)

References 75 publications

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“…R 2 = 0.12) variation for utility expenditures (Table 3) in the explanatory variables. The adjusted R 2 is a recalibration of the R 2 to prevent the artificial increase of R 2 when more variables are added in a model [90]. The Joint F-statistic and Joint Wald statistic are used to assess the model significance [90].…”

Section: Ols Modelmentioning

confidence: 99%

“…The adjusted R 2 is a recalibration of the R 2 to prevent the artificial increase of R 2 when more variables are added in a model [90]. The Joint F-statistic and Joint Wald statistic are used to assess the model significance [90]. The Joint F-statistic is only used when the Koenker (BP) test is not significant [90].…”

Section: Ols Modelmentioning

confidence: 99%

“…The Joint F-statistic and Joint Wald statistic are used to assess the model significance [90]. The Joint F-statistic is only used when the Koenker (BP) test is not significant [90]. Here, the Koenker (BP) statistic is significant, therefore the Joint Wald statistic shows an overall model significance.…”

Section: Ols Modelmentioning

confidence: 99%

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Household Energy Expenditures in North Carolina: A Geographically Weighted Regression Approach

2018

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The U.S. household (HH) energy consumption is responsible for approximately 20% of annual global GHG emissions. Identifying the key factors influencing HH energy consumption is a major goal of policy makers to achieve energy sustainability. Although various explanatory factors have been examined, empirical evidence is inconclusive. Most studies are either aspatial in nature or neglect the spatial non-stationarity in data. Our study examines spatial variation of the key factors associated with HH energy expenditures at census tract level by utilizing geographically weighted regression (GWR) for the 14 metropolitan statistical areas (MSAs) in North Carolina (NC). A range of explanatory variables including socioeconomic and demographic characteristics of households, local urban form, housing characteristics, and temperature are analyzed. While GWR model for HH transportation expenditures has a better performance compared to the utility model, the results indicate that the GWR model for both utility and transportation has a slightly better prediction power compared to the traditional ordinary least square (OLS) model. HH median income, median age of householders, urban compactness, and distance from the primary city center explain spatial variability of HH transportation expenditures in the study area. HH median income, median age of householders, and percent of one-unit detached housing are identified as the main influencing factors on HH utility expenditures in the GWR model. This analysis also provides the spatial variability of the relationship between HH energy expenditures and the associated factors suggesting the need for location-specific evaluation and suitable guidelines to reduce the energy consumption.

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Section: Ols Modelmentioning

confidence: 99%

Section: Ols Modelmentioning

confidence: 99%

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Household Energy Expenditures in North Carolina: A Geographically Weighted Regression Approach

2018

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“…3) building characteristics that include the type of building material, cracks in foundation and walls, ventilation rate (Ciotoli et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is not a surprise that, numerous scientific studies appeared in the literature trying to solve such a difficult issue using different analytical techniques (among others, Zunic et al, 2017a(among others, Zunic et al, , 2017bCiotoli et al, 2017;Ivanova et al, 2017;Yarmoshenko et al, 2016;Kropat et al, 2015;Bossew et al, 2014;Bochicchio et al, 2014;Carpentieri et al, 2011). Generally, two approaches are encountered.…”

Section: Introductionmentioning

confidence: 99%

Sjenica, a Newly Identified Radon Priority Area in Serbia, and Radon Data Correlated With Geological Parameters Using the Multiple Linear Regression Model

Žunić¹,

Stojanovska²,

Boev³

et al. 2018

Carpath. J. Earth Environ. Sci.

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The paper deals with the analysis of the annual indoor radon concentrations variations due to different geological parameters of Sjenica community, Western Serbia. The measured 222 Rn concentrations were ranging from 10 to 1130 Bq/m 3 . In 14% of the buildings, the radon action level of 300 Bq/m 3 is exceeded, indicating that Sjenica community could be characterized as a radon priority area. Each of 35 measuring location was georeferenced and corresponding lithostratigraphic units and geological period was assigned. Data were analyzed using the multiple linear regression (MLR) method and two predictive models were developed. The MLR model generated by the geological periods explained 17% of the radon variability while, the better one, was the lithostratigraphic MLR model, which explained 52% of the radon variability. Analysis has shown that lithostratigraphic units are important parameters in the prediction of radon levels.

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Influence of rock type and geophysical properties on radon flux density

Ryzhakova,

Stavitskaya,

Plastun

2024

Radiat Environ Biophys

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Geographically weighted regression and geostatistical techniques to construct the geogenic radon potential map of the Lazio region: A methodological proposal for the European Atlas of Natural Radiation

Cited by 75 publications

References 75 publications

Household Energy Expenditures in North Carolina: A Geographically Weighted Regression Approach

Household Energy Expenditures in North Carolina: A Geographically Weighted Regression Approach

Sjenica, a Newly Identified Radon Priority Area in Serbia, and Radon Data Correlated With Geological Parameters Using the Multiple Linear Regression Model

Influence of rock type and geophysical properties on radon flux density

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