Use Of Geographically Weighted Regression Research Articles

Geographical Exploration of the Underrepresentation of Ethnic Minority Cyclists in England

Cycling is encouraged as a means of sustainable urban transport, yet its uptake rate is uneven between different ethnic groups. The ethnic minority population in England is underrepresented as cyclists, but the reasons for this are unclear. Through linear regression and Geographically Weighted Regression (GWR), this research investigates the spatial distribution of the propensity to cycling among the ethnic minority population and the white population across England with the aim to identify the contributing factors toward the discrepancy of cycling rates between both groups and how these factors vary geographically. Results from OLS regression suggest that cycle rates are generally affected most by hilliness, the presence of school-age children, and income, with the presence of school-age children affecting the ethnic minority group and hilliness affecting the white group the most. The use of GWR revealed that income generally reduces cycle rates but has a positive impact in London for both groups. The length of cycleways and the length of 20 mph speed limit roads per unit area were statistically insignificant, but their local coefficients in GWR showed strong regional variations for both groups. The study also found that, with the exception of the level of income, ethnic minority cyclists are less sensitive to contributing factors than the white cyclists.

Localized Spatiotemporal Effects in the Determinants of Property Prices: A Case Study of Seoul

This research investigates both spatial and temporal elements of the apartment pricing process in Seoul, South Korea by modeling the determinants of apartment prices over a ten-year period from 2006 to 2015 with a hedonic price model containing a spatio-temporal lag model calibrated by geographically weighted regression (GWR). The results yield information on both spatial and temporal variations in the processes affecting apartment prices and demonstrate the use of GWR for generating local spatial dependency measures which are conditioned on various covariates rather than being simple descriptions of pattern. The study utilizes a combined approach to account for both spatial dependency in housing prices and spatial heterogeneity in the processes generating those prices. The results suggest that there are spatial variations in the determinants of apartment prices and that these spatial variations are fairly consistent over time. The effect of the spatial lag on house prices exhibits strong spatial variation which again is reasonably consistent over time.

Use of Geographically Weighted Regression (GWR) Method to Estimate the Effects of Location Attributes on the Residential Property Values

This study estimates the effect of locational attributes on residential property values in Kuala Lumpur, Malaysia. Geographically weighted regression (GWR) enables the use of the local parameter rather than the global parameter to be estimated, with the results presented in map form. The results of this study reveal that residential property values are mainly determined by the property’s physical (structural) attributes, but proximity to locational attributes also contributes marginally. The use of GWR in this study is considered a better approach than other methods to examine the effect of locational attributes on residential property values. GWR has the capability to produce meaningful results in which different locational attributes have differential spatial effects across a geographical area on residential property values. This method has the ability to determine the factors on which premiums depend, and in turn it can assist the government in taxation matters.

Extending geographically weighted regression from points to flows: a rail-based case study

At present in the UK an elasticity-based approach is used to forecast changes in rail passenger demand resulting from changes in both the rail service offer and external conditions, with uplift factors calculated based on the proportional change in the level of explanatory variables over time. Changes in these explanatory variables may have differing effects on rail demand in different areas. This is currently controlled for via a limited segmentation of the market with different elasticities estimated for each segment, which inevitably limits the complexity of the variations which can be captured. This paper describes the use of geographically weighted regression (GWR) to enhance the modelling of such spatial variation. First, conventional cross-sectional demand models were calibrated covering major rail flows across the Great Britain. These models were then recalibrated using GWR to allow assessment of spatial variations in rail demand elasticities. Previous applications of GWR have almost exclusively focused on spatial data which have a single point location. This is not the case for rail flows, and this paper compares the results given by several different methods for defining point locations for flows. It also assesses different methods for approximating GWR results to simplify their application in real-life forecasting situations. The results show that the use of GWR can give a significant improvement in the fit of flow-based rail demand models, and that it is possible to spatially segment the UK passenger rail market based on the results from these models. In order to integrate such segmentations with the standard UK rail demand forecasting methodology it would, however, be necessary to extend the GWR methodology further to allow the calibration of GWR models on panel data.

Addressing issues of spatial spillover effects and non-stationarity in analysis of residential burglary crime

The aim of this paper is to address two critical but largely neglected issues in the spatial analysis of urban crime which are spatial spillover effects of crime penetrating neighborhood boundaries and non-stationarity regarding the relationships between contextual factors and neighborhood crime. We use a GIS-based spatial approach to normalize the estimate of burglary crime at block group level and use the geographically weighted regression (GWR) to investigate the correlates of neighborhood crime. Results suggest that the use of normalized measure of neighborhood crime helps better reveal the spatial patterns of burglary crime and the use of GWR accounts for the spatial variations of relationships between contextual factors and crime. In particular, the normalized measure of crime has implications for improving the measurement accuracy of the risk of crime across urban neighborhoods and can be applied to the spatial analysis of other socioeconomic issues such as housing foreclosures and environmental hazards which are also plagued by the spatial spillover issue when geographically contiguous data are analyzed.

The influence of sampling density on geographically weighted regression: a case study using forest canopy height and optical data

Geographically weighted regression (GWR) extends the conventional ordinary least squares (OLS) regression technique by considering spatial nonstationarity in variable relationships and allowing the use of spatially varying coefficients in linear models. Previous forest studies have demonstrated the better performance of GWR compared to OLS when calibrated and validated at sampled locations where field measurements are collected. However, the use of GWR for remote-sensing applications requires generating estimates and evaluating the model performance for the large image scene, not just for sampled locations. In this study, we introduce GWR to estimate forest canopy height using high spatial resolution Quickbird (QB) imagery and evaluate the influence of sampling density on GWR. We also examine four commonly used spatial analysis techniques – OLS, inverse distance weighting (IDW), ordinary kriging (OK) and cokriging (COK) – and compare their performance with that using GWR. Results show that (i) GWR outperformed OLS at all sampling densities; however, they produced similar results at low sampling densities, suggesting that GWR may not produce significantly better results than OLS in remote-sensing operational applications where only a small number of field data are collected. (ii) The performance of GWR was better than those of IDW, OK and COK at most sampling densities. Among the spatial interpolation techniques we examined, IDW was the best to estimate the canopy height at most densities, while COK outperformed OK only marginally and produced larger canopy height estimation errors than both IDW and GWR. (iii) GWR had the advantage of generating canopy height estimation maps with more accurate estimates than OLS, and it preserved patterns of geographic features better than IDW, OK or COK.

Neighborhood Characteristics and Housing Values within the San Juan, MSA, Puerto Rico

Este estudio utiliza el método tradicional de regresión global de mínimos cuadrados ordinarios (OLS) y una forma de regresión local, Regresión Ponderada Geográficamente (GWR), para examinar la influencia de las características del vecindario en los valores de las casas a través de San Juan, Puerto Rico MSA y discute la importancia y resultados de los efectos del vecindario en los valores de las casas. Además, desagrupa la influencia de la presencia de grupos nacionales, tales como cubanos y dominicanos, en vez de agruparlos en el grupo monolítico de "Hispanos". Ha sido una práctica común en la literatura el agrupar a nacionalidades bastante diversas, tales como mexicanos, cubanos, puertorriqueños y dominicanos en un simple grupo monolítico etiquetado "Hispanos". El uso de GWR ayuda a identificar los efectos del vecindario porque incorpora relaciones geográficas locales (o las excepciones) de las características del vecindario y de los valores de las casas que ocurren a través del área de estudio destacando la importancia del contexto.

Relationship Between Transport Accessibility and Land Value: Local Model Approach with Geographically Weighted Regression

In recent years, land value capture has attracted increasing attention because of its potential for funding transport infrastructure. It is well acknowledged that transport infrastructure can improve accessibility to employment and amenities; thus one might expect that it is the improved accessibility that adds value to land. Therefore, the issues in the relationship between transport accessibility and land value rise in connection with the concept of land value capture. A study looked at the relationship between transport accessibility and land value with the implication of a local model, geographically weighted regression (GWR). Traditional techniques, such as hedonic models, used to understand the attributes of land value, are global models that could be misleading in examining the spatially varying relationships, such as transport accessibility and land value. By using the Tyne and Wear region in the United Kingdom as a case study, the study revealed that nonstationarity existing in the relationship between transport accessibility and land value indicates that transport accessibility may have a positive effect on land value in some areas but a negative or no effect in others; this suggests that a uniform land value capture would be inappropriate. The use of GWR allows such spatially varying relationships to be revealed, leading to a better understanding of the factors determining positive land value uplift and the implications of spatially dependent transport access premiums in housing values in the context of value capture policies.

Relationship between Transport Accessibility and Land Value

In recent years, land value capture has attracted increasing attention because of its potential for funding transport infrastructure. It is well acknowledged that transport infrastructure can improve accessibility to employment and amenities; thus one might expect that it is the improved accessibility that adds value to land. Therefore, the issues in the relationship between transport accessibility and land value rise in connection with the concept of land value capture. A study looked at the relationship between transport accessibility and land value with the implication of a local model, geographically weighted regression (GWR). Traditional techniques, such as hedonic models, used to understand the attributes of land value, are global models that could be misleading in examining the spatially varying relationships, such as transport accessibility and land value. By using the Tyne and Wear region in the United Kingdom as a case study, the study revealed that nonstationarity existing in the relationship between transport accessibility and land value indicates that transport accessibility may have a positive effect on land value in some areas but a negative or no effect in others; this suggests that a uniform land value capture would be inappropriate. The use of GWR allows such spatially varying relationships to be revealed, leading to a better understanding of the factors determining positive land value uplift and the implications of spatially dependent transport access premiums in housing values in the context of value capture policies.