Inverse Distance Weighting interpolation on the optimum distribution of kernel - Geographically Weighted Regression for land price (original) (raw)
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GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY
Bandwidth plays a crucial role in the Geographically Weighted Regression modelas it affects the model’s ability to describe spatial dependencies. If the bandwidth is too large, the model will be similar to a normal regression model. Conversely, if it is too small, the model will be too rough. Bandwidth can be selected in several ways, e.g. manually determined by experts or using Akaike Information Criteria, Cross-Validation, and Lagrange Multiplier methods. This study offers an alternative approach to choosing bandwidth based on the covariance function representing a linear combination between the Bessel and Gaussian-Type functions. We applied this function to analyze the land price in Manado with four infrastructure accessibility variables, such as accessibility to government offices, education facilities, shopping centers, and healthcare facilities. Therefore, the proposed method is different from the index methods (AIC and CV) which have been used by other researchers. The result...
International Journal of Technology
The study examines the influence of four spatial weighting functions and bandwidths on the performance of geographically weighted regression (GWR), including fixed Gaussian and bisquare adaptive kernel functions, and adaptive Gaussian and bi-square kernel functions relative to the global hedonic ordinary least squares (OLS) models. A demonstration of the techniques using data on 3.232 house sales in Cape Town suggests that the Gaussian-shaped adaptive kernel bandwidth provides a better fit, spatial patterns and predictive accuracy than the other schemes used in GWR. Thus, we conclude that the Gaussian shape with both fixed and adaptive kernel functions provides a suitable framework for house price valuation in Cape Town.
Indonesian Journal of Geography
Land prices, especially in an urban area, are dynamically changing. To be able to do an evaluation, the right models must have the ability to understand land price characteristics that also dynamically changing. Every land price must attach to a location (spatial based). One of the locations (spatial based) models is Geographically Weighted Regression (GWR). This model can provide a local model based on the concept of attachment between observation and regression points. The main component is the determination of Optimum Bandwidth, which will determine the accuracy of the final GWR model. In the bandwidth process, it is necessary to do trial and error to get the Optimum Bandwidth value. Cross-Validation method commonly used to determine optimum bandwidth on observation point, but this study aims to minimize the process of trial and error in determining optimal bandwidth outside the observation point by using kriging interpolation. The Kriging method can substantially provide better...
Examining the effects of land use zoning on land price with geographically weighted regression
Geographical reports of Tokyo Metropolitan University, 2015
The effects of land use zoning on land price is expected to vary across places within an metropolitan area. To assess the local effect of a land use zone in each place, hedonic equation of land prices is estimated using geographically weighted regression (GWR) that allows estimation of locally varying but spatially continuous parameters. An experiment for Sendai Metropolitan Area (Miyagi, Japan) reveals that the effects of land use zones indeed vary, and in some locations even the more restrictive zone designation raise the price of that land than designated otherwise. Based on the equation, the zone that maximizes the price of land for each location can also be identified. While GWR seem to be a usefull tool in evaluating and designing urban land use planning, the experiment in this paper also reveals that obtaining robust result is difficult, and further research is necessary to specify variables and fuctional forms for reliable estimates.
International Journal on Advanced Science, Engineering and Information Technology, 2023
One of the methods used to estimate land prices is the Geographically Weighted Regression (GWR). The GWR method is built based on the dependent and independent variables (land prices) (the spatial proximity between the land object and other facilities). However, this study will develop the independent variable by adding a spatial planning zone to provide the complexity of land price estimation. This study proposes an implementation mechanism by setting each zone type as an independent variable. Based on the spatial planning zones in Eastern Bandung City, there are five spatial planning zones. Thus, 15 variables were used in this GWR model, with ten variables from public facilities and five from spatial planning zones. The variables are categorized into worship, industry, government offices, health, sports/recreation, education, prisons, defense offices, terminals, trade and service zones, industrial zones, and low-residential, medium, and high-residential zones. The results of this study indicate that the implementation of the spatial planning zone variable has a better accuracy rate than the GWR model without involving the spatial planning zone variable. The approach with the proposed mechanism gives better accuracy of 8.6%. Spatial planning zone variable can be a new perspective in making a GWR-based land price estimation model in addition to the physical object variable in the form of public or social facilities, especially to improve the quality of the model formed.
Spatial Regression Analysis of Commercial Land Price Gradients
2001
Commercial land price gradients for an emerging real estate market are estimated using spatial regression techniques. Spatial statistics are used to explore the extent of spatial autocorrelation in the residuals of an OLS land price gradient model. Spatial autocorrelation is present but not to the same degree for all time periods or commercial land uses. Maximum likelihood estimates of land price gradients are as one would expect in mature real estate markets.
Procedia Environmental Sciences, 2011
Geographically Weighted Regression (GWR) is a local modelling technique to estimate regression models with spatially varying relationships. Generally, the Euclidean distance is the default metric for calibrating a GWR model in previous research and applications; however, it may not always be the most reasonable choice due to a partition by some natural or man-made features. Thus, we attempt to use a non-Euclidean distance metric in GWR. In this study, a GWR model is established to explore spatially varying relationships between house price and floor area with sampled house prices in London. To calibrate this GWR model, network distance is adopted. Compared with the other results from calibrations with Euclidean distance or adaptive kernels, the output using network distance with a fixed kernel makes a significant improvement, and the river Thames has a clear cut-off effect on the parameter estimations.
Review on Geographically Weighted Regression (GWR) approach in spatial analysis
Malaysian Journal of Fundamental and Applied Sciences
In spatial analysis, it is important to identify the nature of the relationship that exists between variables. Normally, it is done by estimating parameters with observations which taken from different spatial units that across a study area where parameters are assumed to be constant across space. However, this is not so as the spatial non-stationarity is a condition in which a simple model cannot explain the relationship between some sets of variables. The nature of the model must alter over space to reflect the structure within the data. Non-stationarity means that the relationship between variables under study varies from one location to another depending on physical factors of the environment that are spatially autocorrelated. Geographically Weighted Regression (GWR) is a technique in which it applied to capture the variation by calibrating a multiple regression model, which allows different relationships to exist at different points in space. A robust algorithm has been succes...
Geographically Weighted Regression using a non-euclidean distance metric with simulation data
Agro-Geoinformatics (Agro- …, 2012
Geographically Weighted Regression (GWR) is a local modelling technique to estimate regression models with spatially varying relationships. Generally, the Euclidean distance is the default metric for calibrating a GWR model in previous research and applications; however, it may not always be the most reasonable choice due to a partition by some natural or man-made features. Thus, we attempt to use a non-Euclidean distance metric in GWR. In this study, a GWR model is established to explore spatially varying relationships between house price and floor area with sampled house prices in London. To calibrate this GWR model, network distance is adopted. Compared with the other results from calibrations with Euclidean distance or adaptive kernels, the output using network distance with a fixed kernel makes a significant improvement, and the river Thames has a clear cut-off effect on the parameter estimations.
Mathematical Geosciences, 2010
Increasingly, the geographically weighted regression (GWR) model is being used for spatial prediction rather than for inference. Our study compares GWR as a predictor to (a) its global counterpart of multiple linear regression (MLR); (b) traditional geostatistical models such as ordinary kriging (OK) and universal kriging (UK), with MLR as a mean component; and (c) hybrids, where kriging models are specified with GWR as a mean component. For this purpose, we test the performance of each model on data simulated with differing levels of spatial heterogeneity (with respect to data relationships in the mean process) and spatial autocorrelation (in the residual process). Our results demonstrate that kriging (in a UK form) should be the preferred predictor, reflecting its optimal statistical properties. However the GWRkriging hybrids perform with merit and, as such, a predictor of this form may provide a worthy alternative to UK for particular (non-stationary relationship) situations when UK models cannot be reliably calibrated. GWR predictors tend to perform more poorly than their more complex GWR-kriging counterparts, but both GWR-based models are useful in that they provide extra information on the spatial processes generating the data that are being predicted.