Geographic Weighted Regression (GWR)

Amenities and infrastructure provision in urban areas are essential for the sustainable future of cities in developing countries like India. Indian cities have large development deficits and find it challenging to bridge the gap using traditional methods. Provision of these facilities costs money, which is often not available. However, access to amenities and infrastructure adds to land premium, which, if captured, can be used to finance the provision of these facilities. In India, very little information is available on the value of accessibility and infrastructure provision, and thus, these indirect benefits are primarily ignored by urban planners. This study fills the gap by identifying these benefits using Rajkot city in India as a case study. A geographic weighted regression model is used to model the relationship. It is found that land price variation is explained to a good extent using the model. Estimates show that infrastructure and amenities have a substantial impact on land value, much higher than the cost required to provide these.

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Geographic weighted regression: applicability to epidemiological studies of leprosy

Revista da Sociedade Brasileira de Medicina Tropical ◽

10.1590/0037-8682-0307-2015 ◽

2016 ◽

Vol 49 (1) ◽

pp. 74-82 ◽

Cited By ~ 7

Author(s):

Mônica Duarte-Cunha ◽

Andréa Sobral de Almeida ◽

Geraldo Marcelo da Cunha ◽

Reinaldo Souza-Santos

Keyword(s):

Epidemiological Studies ◽

Weighted Regression ◽

Geographic Weighted Regression

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Analysis of aquifer vulnerability and water quality using SINTACS and geographic weighted regression

Environmental Earth Sciences ◽

10.1007/s12665-011-1447-4 ◽

2011 ◽

Vol 66 (8) ◽

pp. 2257-2271 ◽

Cited By ~ 3

Author(s):

Jose A. Ramos Leal ◽

Felipe O. Tapia Silva ◽

Ismael Sandoval Montes

Keyword(s):

Water Quality ◽

Aquifer Vulnerability ◽

Weighted Regression ◽

Geographic Weighted Regression

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Spatial Differentiation and Influencing Factors of Second-Hand Housing Prices: A Case Study of Binhu New District, Hefei City, Anhui Province, China

Journal of Mathematics ◽

10.1155/2021/8792550 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Song Xu ◽

Zhen Zhang

Keyword(s):

Spatial Heterogeneity ◽

House Prices ◽

Housing Prices ◽

Spatial Differentiation ◽

Global Scale ◽

Weighted Regression ◽

Subway Station ◽

Building Structure ◽

Hedonic Price ◽

Geographic Weighted Regression

The multiscale geographic weighted regression (MGWR) model obtains different influence scales of various variables better than the classical geographic weighted regression (GWR) model. This paper studies the price characteristics of second-hand residential transactions in Binhu New District taking advantage of the hedonic price model and MGWR model and draws the following conclusions. (1) There are obvious spatial positive correlation and spatial heterogeneity in the price of second-hand housing in Binhu New District. (2) The number of bedrooms, area, age of the house, and the distance to the nearest school have small effect on the scale, so they have strong spatial heterogeneity. The decoration status and floor are global scale variables, and their spatial heterogeneity is weak. (3) The number of bedrooms, orientation, decoration status, floor, and building structure all positively affect house prices, while area, house age, the distance to the nearest subway station, and the distance to the nearest school negatively affect house prices. Among all factors, the distance to the nearest school is the most important factor affecting house prices, followed by the number of bedrooms and then followed by the distance to the nearest subway station and area, while the orientation, floor, building structure, and decoration conditions have less impact, and the house age has the weakest impact.

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