Measuring Spatial Variations in Relationships with Geographically Weighted Regression

Fotheringham, A. Stewart; Charlton, Martin; Brunsdon, Chris

doi:10.1007/978-3-662-03499-6_4

Cited by 368 publications

(561 citation statements)

References 13 publications

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“…According to the evaluation criteria proposed by Fotheringham [18], for the same sample data, if the difference in the AIC C value between two models is greater than 3, then the model with a lower value has a better fit for the observed data. A higher adjusted R 2 and lower residual squares also indicate a better model.…”

Section: Model Evaluationmentioning

confidence: 99%

Detecting the Spatially Non-Stationary Relationships between Housing Price and Its Determinants in China: Guide for Housing Market Sustainability

Mou

Zhou

2017

Sustainability

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Abstract:Given the rapidly developing processes in the housing market of China, the significant regional difference in housing prices has become a serious issue that requires a further understanding of the underlying mechanisms. Most of the extant regression models are standard global modeling techniques that do not take spatial non-stationarity into consideration, thereby making them unable to reflect the spatial nature of the data and introducing significant bias into the prediction results. In this study, the geographically weighted regression model (GWR) was applied to examine the local association between housing price and its potential determinants, which were selected in view of the housing supply and demand in 338 cities across mainland China. Non-stationary relationships were obtained, and such observation could be summarized as follows: (1) the associations between land price and housing price are all significant and positive yet having different magnitudes; (2) the relationship between supplied amount of residential land and housing price is not statistically significant for 272 of the 338 cities, thereby indicating that the adjustment of supplied land has a slight effect on housing price for most cities; and (3) the significance, direction, and magnitude of the relationships between the other three factors (i.e., urbanization rate, average wage of urban employees, proportion of renters) and housing price vary across the 338 cities. Based on these findings, this paper discusses some key issues relating to the spatial variations, combined with local economic conditions and suggests housing regulation policies that could facilitate the sustainable development of the Chinese housing market.

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Section: Model Evaluationmentioning

confidence: 99%

Detecting the Spatially Non-Stationary Relationships between Housing Price and Its Determinants in China: Guide for Housing Market Sustainability

Mou

Zhou

2017

Sustainability

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“…The local linear regression, introduced to the economic context by McMillen [34], is a relatively recent modelling technique for spatial data analysis. From 1996 the technique was extended by Fotheringham, Brunsdon and Charlton [35][36][37][38] and was also renamed geographically weighted regression. Unlike global regression models, where a single coefficient is estimated for each explanatory variable, GWR enables local variations (over space) in the estimation of coefficients.…”

Section: Methodsmentioning

confidence: 99%

Geographically Weighted Regression in the Analysis of Unemployment in Poland

Lewandowska-Gwarda

2018

IJGI

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Abstract:The main aim of this paper is an application of Geographically Weighted Regression (which enables the identification of the variability of regression coefficients in the geographical space) in the analysis of unemployment in Poland 2015. The study is conducted using 2015 statistical data for 380 districts (LAU 1) in Poland. The research results show that the determinants of unemployment are diverse in the geographic space and do not have a significant impact on unemployment rates in all spatial units (LAU 1). The existence of clusters of districts, characterised by the influence of the variables and a similar strength of interactions, is confirmed. Geographically Weighted Regression (GWR) proved to be an extremely effective instrument of spatial data analysis. The model had a considerably better fit with empirical data than the global model, and it enabled the drawing of detailed conclusions concerning the local determinants of unemployment in Poland.

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“…The GWR is a technique that extends the traditional regression framework. The concepts behind this approach are discussed by Fotheringham and Brunsdon (1996) and further illustrated by Fotheringham (1997) and Fotheringham and Brunsdon (1999).…”

Section: Gwr More Formallymentioning

confidence: 99%

“…Too small a bandwidth will result in estimation problems with some of the local regressions. Brunsdon et al(1996) and Fotheringham et al(1997Fotheringham et al( ,1998 describe how the bandwidth can be calibrated in the model.…”

Section: …………………………………………………………………(7)mentioning

confidence: 99%

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The Price Impact of House Refurbishment Estimated by Geographically Weighted Regression and Hedonic Pricing Model

Lai

Fischer

2016

IJPS

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Geographically Weighted Regression (GWR) is a technique that extends the traditional regression framework by allowing spatial parameters to be explicitly estimated. This paper provides a brief description of the Geographically Weighted Regression used here to value the effect of residential housing refurbishment in the City of Kaohsiung (Taiwan). The GWR results are then compared to a standard hedonic pricing estimation model applied to the same data set. What is intended here is to illustrate the use of a better tool for the identification of the spatial price impact of housing improvement investments in the metropolitan area. More generally, the paper confirms that spatial-adaptable models are required to measure the impact of investments in mixed and fuzzy goods.

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Measuring Spatial Variations in Relationships with Geographically Weighted Regression

Cited by 368 publications

References 13 publications

Detecting the Spatially Non-Stationary Relationships between Housing Price and Its Determinants in China: Guide for Housing Market Sustainability

Detecting the Spatially Non-Stationary Relationships between Housing Price and Its Determinants in China: Guide for Housing Market Sustainability

Geographically Weighted Regression in the Analysis of Unemployment in Poland

The Price Impact of House Refurbishment Estimated by Geographically Weighted Regression and Hedonic Pricing Model

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