Exploring spatial relationship non-stationary based on GWR and GIS

Qin, Wenzhong; Wang, Jianmei

doi:10.1117/12.712983

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…Therefore, it will be appropriate to consider mixed GWR method in which some coefficients are global (i.e. they are not spatially varying) and the rest are local (which are expected to be functions of locations) [see, for instance, Fotheringham et al (2002) and Qin and Wang (2006)]. We also examine whether using GWR and mixed GWR methods provides a gain or not compared to global OLS methodology in terms of model performance.…”

Section: Introductionmentioning

confidence: 99%

Local and global determinants of office rents in Istanbul

Bera

Uyar

2019

JERER

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Purpose This paper presents a hedonic office rent model under the decentralized structure of Istanbul Office Market. The data set in the study includes 2,348 office spaces for the first quarter of 2018. This study aims to find determinants that affect the level of rent and examine whether the effects of office rent determinants are global or not. Design/methodology/approach To consider both global and local effects, the paper uses mixed geographically weighted regression approach in hedonic office rent analysis. Findings The empirical results indicate that office rent determinants such as physical, locational, neighborhood and market operational characteristics have significant impacts on the level of the rent. The findings also show that one of the office rent determinants has a global effect and the other determinants have local effects. According to the estimation results, local effects and statistical significances of these determinants vary from lower quartiles to upper quartiles. Originality/value To the best of the authors’ knowledge, this is the first paper to consider global and local effects of office rent determinants on the level of rent, with mixed geographically weighted regression approach. The paper provides new insights into the hedonic valuation of commercial real estates, especially for decentralized office markets.

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Section: Introductionmentioning

confidence: 99%

Local and global determinants of office rents in Istanbul

Bera

Uyar

2019

JERER

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“…However, in reality, the accurate modeling of complex geographic relationships requires increasingly nonstationary solution accuracy and computing power. If GWR is used, the model needs further improvements in proximity analysis, calculation of kernel weights, and optimization of bandwidth parameters, among other areas [38,57].…”

Section: Methodological Reviewmentioning

confidence: 99%

Local or Neighborhood? Examining the Relationship between Traffic Accidents and Land Use Using a Gradient Boosting Machine Learning Method: The Case of Suzhou Industrial Park, China

Yang

Chung

Kim

2021

Journal of Advanced Transportation

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In cities, road traffic accidents are critical endangerment to people’s safety. A vast number of studies which are designed to understand these accidents’ leading causes and mechanisms exist. The widely held view is that emerging analysis methods can be a critical tool for understanding the complex interactions between land use and urban transportation. Using a case study of Suzhou Industrial Park (SIP) in Suzhou, China, this paper examines the relationship between different land use types and traffic accidents using a gradient boosting model (GBM) machine learning method. The results show that the GBM can be used as an effective accident model for a variety of research and analysis methods by (1) ranking the influential factors, (2) testing the degree of interpretation of each variable as the complexity of iterations changes, and (3) obtaining partial dependence plots, among other methods. The findings of this study also suggest that land use types—including facility points—demonstrate differing degrees of influence at two geographical scales: local level and neighborhood level. In the ranking of relative importance at both scales, the variables of education institutions, traffic lights, and service institutions are all ranked high—with a more significant influence on the occurrence of accidents. However, residential land and land use mix variables differed significantly in both scales and showed a significant deviation compared to the other results. When adjusting the complexity of the decision tree, the local level is more suitable for measuring variables such as residential areas and green parks where pedestrians and vehicles have fixed mobility periods and moderate flows. On the contrary, the nearest neighborhood level is more suitable to a small number of variables related to public service facilities at fixed locations, such as traffic lights and bus stops. In the partial dependence plots, all variables, except educational institutions and residences, show a positive correlation for accidents in the fitting process. The results of this study can ideally help inform transportation planners to reconsider transport accident occurrence rates in the context of the proximity to various land use types and public service facilities.

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“…GWR is a common method for exploring spatial relationship non-stationarity. In GWR, all coefficients vary over space, and the parameter estimates are made using an approach in which the contribution of a sample to the analysis is weighted based on its spatial proximity to the specific location under consideration [21,22]. The GWR model is considered to be a very effective spatial data analysis tool.…”

Section: Gwrmentioning

confidence: 99%

Influencing Factors for the Promotion of International Vocational Qualification and Certification: Evidences from International Project Manager Professionals in China

et al. 2020

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Globalization has driven the promotion of international vocational qualification and certification (IVQC) to unify certification systems and standards. We explore IVQC promotion paths through the introduction and development of China's International Project Manager Professional (IPMP) certification and identify and analyse IVQC processes' influencing factors. Four factors (economic level, education level, employment level, sex ratio) are proposed; their impacts are hypothesized. Geographically weighted regression (GWR) is employed to identify factor impact relationships and validate assumptions. The results show that the four factors are positive for the promotion of IVQC. Economic level, employment level, and sex ratio contribute to the promotion of IVQC; employment level contributes most. Education level has relatively small impact. Therefore, IVQC is more likely to enter areas with developed economies, high employment rates, and more males. The promotion of IVQC can be facilitated by continuous social progress and international development. However, areas where salient factor levels are too low still present challenges.

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Exploring spatial relationship non-stationary based on GWR and GIS

Cited by 4 publications

References 6 publications

Local and global determinants of office rents in Istanbul

Local and global determinants of office rents in Istanbul

Local or Neighborhood? Examining the Relationship between Traffic Accidents and Land Use Using a Gradient Boosting Machine Learning Method: The Case of Suzhou Industrial Park, China

Influencing Factors for the Promotion of International Vocational Qualification and Certification: Evidences from International Project Manager Professionals in China

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