A Comparison of Bandwidth and Kernel Function Selection in Geographically Weighted Regression for House Valuation

Yacim, Joseph Awoamim; Boshoff, Douw Gert Brand

doi:10.14716/ijtech.v10i1.975

Cited by 12 publications

(9 citation statements)

References 12 publications

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“…The geographically weighted regression (GWR) model is a statistical regression model proposed by British scholars (36) to account for the spatial heterogeneity of variables, and it is a kind of spatial econometric model. It is generally believed that GWR models improve upon the traditional spatial regression method, and various studies have shown that GWR models are the best method for exploring the spatial heterogeneity of housing prices and their influencing factors (37)(38)(39)(40). Therefore, we use a GWR model to explore the relationship between housing prices and air quality.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Spatial Heterogeneity of Residents' Marginal Willingness to Pay for Clean Air in Shanghai

Lai

Liu

et al. 2021

Front. Public Health

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Previous studies have paid little attention to the spatial heterogeneity of residents' marginal willingness to pay (MWTP) for clean air at a city level. To fill this gap, this study adopts a geographically weighted regression (GWR) model to quantify the spatial heterogeneity of residents' MWTP for clean air in Shanghai. First, Shanghai was divided into 218 census tracts and each tract was the smallest research unit. Then, the impacts of air pollutants and other built environment variables on housing prices were chosen to reflect residents' MWTP and a GWR model was used to analyze the spatial heterogeneity of the MWTP. Finally, the total losses caused by air pollutants in Shanghai were estimated from the perspective of housing market value. Empirical results show that air pollutants have a negative impact on housing prices. Using the marginal rate of transformation between housing prices and air pollutants, the results show Shanghai residents, on average, are willing to pay 50 and 99 Yuan/m2 to reduce the mean concentration of PM2.5 and NO2 by 1 μg/m3, respectively. Moreover, residents' MWTP for clean air is higher in the suburbs and lower in the city center. This study can help city policymakers formulate regional air management policies and provide support for the green and sustainable development of the real estate market in China.

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Section: Introductionmentioning

confidence: 99%

Exploring the Spatial Heterogeneity of Residents' Marginal Willingness to Pay for Clean Air in Shanghai

Lai

Liu

et al. 2021

Front. Public Health

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“…As the explanatory term, heterogeneity addresses regional similarity and global difference in a deposit (Yacim and Boshoff 2019). Identifying and modeling spatial heterogeneity in a lignite site require specifying critical field parameters like influential distances (bandwidth) and the main determinants like weights.…”

Section: Methodology 21 Geographical Consideration Of Lignite Depositmentioning

confidence: 99%

Learning distance effect on lignite quality variables at global and local scales

Yaylagul

Tutmez

2020

Int J Coal Sci Technol

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Determining scale and variable effects have critical importance in developing an energy resource policy. This study aims to explore the relationships in heterogeneous lignite sites using different scale models, spatial weighting as well as error-based pair-wise identification. From a statistical learning framework, the relationships among the quality variables such as geochemical variables and the contributions of the coordinates to quality measures have been exhibited by generalized additive models. In this way, the critical roles of spatial weights provided by the coordinates have been specified at a global scale. The experimental studies reveal that incorporating the geological weighting in the models as the additional information improves both accuracy and transparency. Because relationships among lignite quality variables and sampling locations are spatially non-stationary, the local structure and interdependencies among the variables were analyzed by geographically weighting regression. The local analyses including spatial patterns of bandwidths, search domains as well as residual-based areal dependencies provided not only the critical zones but also availability of pair-wise model alternatives by calibrating a model at each point for location-specific parameter learning. The results completely show that the weighting models applied at different scales can take spatial heterogeneity into consideration and these abilities provide some meta-data and specific information using in sustainable energy planning.

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“…Kernel, which is a kernel weighting function assumes that the bandwidth adapts itself in size relatives to variations in the density of the data [7]. Meanwhile, the weighting based on area (contiguity) uses the queen contiguity weight, which can represent the intersection of the sides and the intersection of the corners [8].…”

Section: Deby Ardianti 213mentioning

confidence: 99%

Distance and Areas Weighting of GWR Kriging for Stunting Cases In East Java

Ardianti

Pramoedyo

Nurjannah

2021

CAUCHY

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Spatial heterogeneity shows the characteristic location from one location to others location and it is the main assumption in Geographically Weighted Regression. The location becomes a weight on GWR model, There are two groups of location weight namely based on distance and area. The weight considers the closeness between the location. The accuracy weighted is needed because the weighting represents the data location. The aim of this research was to get a suitable weighting method for stunting data. This research used secondary data about stunting and the influence factors of stunting such as coverage visiting of pregnant women (K1), consumption of FE tablet, exclusive of breastfeeding, immunization coverage, and clean health behaviour. Those data obtained from the Healthy Ministry of East Jawa.Based on the results of this research show that the goodness weighting for GWR modell is Adaptive Bisquare Kernel (distance weighting). The predicted mapping stunting is showed by interpolation Kriging with a range of 27% to 49,5%.

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A Comparison of Bandwidth and Kernel Function Selection in Geographically Weighted Regression for House Valuation

Cited by 12 publications

References 12 publications

Exploring the Spatial Heterogeneity of Residents' Marginal Willingness to Pay for Clean Air in Shanghai

Exploring the Spatial Heterogeneity of Residents' Marginal Willingness to Pay for Clean Air in Shanghai

Learning distance effect on lignite quality variables at global and local scales

Distance and Areas Weighting of GWR Kriging for Stunting Cases In East Java

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