The varying effects of accessing high-speed rail system on China’s county development: A geographically weighted panel regression analysis

Yu, Danlin; Zhang, Yaojun; Wu, Xiwei; Li, Ding

doi:10.1016/j.landusepol.2020.104935

Cited by 21 publications

(10 citation statements)

References 61 publications

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“…Based on these four categories of social determinants of health and demographic background information, we collected data of the total population (demographic category), local economic status (prefecture GDP per capita, portion of the secondary industry in GDP, portion of the tertiary industry in GDP, local financial income per capita, local financial expenditure per capita, socioeconomic category), health care system status (the number of hospitals per 10,000 individuals, the number of doctors per 10,000 individuals, the number of hospital beds per 10,000 individuals, health system category), and infrastructure status (the density of roads, and the average road network travel time to the nearest high-speed rail station, infrastructure category). All data are collected from the 2019 China's statistical yearbook or calculated based on the 2019 open street map road network ( openstreetmap.org ) and high-speed rail information, the most recent such data ( Yu, Murakami, et al, 2020 ; Yu, Zhang, Wu, Li, & Li, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Seeing the forest and the trees: Holistic view of social distancing on the spread of COVID-19 in China

Zhang

Meng

et al. 2023

Applied Geography

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Section: Methodsmentioning

confidence: 99%

Seeing the forest and the trees: Holistic view of social distancing on the spread of COVID-19 in China

Zhang

Meng

et al. 2023

Applied Geography

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“…The GWR is a typical local model because it does not assume that the relationships between the dependent and independent variables are spatially stationary 38 , 52 , 53 . Simply speaking, the GWR model divides the total sample into several subsamples by the spatial contexts of observations.…”

Section: Methodsmentioning

confidence: 99%

Land cover matters to human well-being

Managi

2021

Sci Rep

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People migrate from rural to urban areas. In the meantime, the benefits of staying in greener areas are also known. People’s preferences might be different by area that is composed of several land types. If so, the effect of particular land cover on human well-being is different spatially. The spatial analysis is required to formulate effective land-use policies. Here we show that urban land, water, and grassland are positively related to human well-being, whereas bare land is negatively associated in Japan. A 1 $${\mathrm{m}}^{2}$$ m 2 increase in the area of urban land per capita in a city is equivalent to an about 346 USD increase in the individual annual income of all the people in the city. Additionally, monetary values of areas of water, crops, and bare land per capita are 102, − 30, and − 268 $$\mathrm{USD}/\mathrm{Capita }{\mathrm{m}}^{2}$$ USD / Capita m 2 . Furthermore, the spatial context matters to the relationship between land cover and human well-being. This paper investigates the monetary values of several land types and their spatial variability, which provides insights to make better usage for land cover.

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“…It is also recognized as a useful tool for regional analysis and policymaking. Over the past years, GWR technique has been applied to many scientific fields such as social sciences (Powers et al, 2021) ,health (Wang and Wu, 2020) urban development and planning (Yu et al, 2021), climatology (Xu and Zhang, 2021) and transportation (Pagliara andMauriello, 2020, Yu andPeng, 2019) . Although many researchers agree that spatial characteristics often influence travel behaviour (Stead, 2001, Lloyd and Shuttleworth, 2005, Cardozo et al, 2012, very few studies consider the relative importance and significance of spatial circumstances specifically on travel demand steps.…”

Section: Introductionmentioning

confidence: 99%

Explanatory Analyses of Work Trip Generation Using Mixed Geographically Weighted Regression (Mgwr)

Shahri

Ghannadi²

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In transportation planning, forecasts have commonly followed the sequential four-step model in which, trip generation (production and attraction) plays a critical role. Among the methods applied to model trip generation, regression with Gaussian distribution of errors are recognized as the most prevailing techniques to describe the relationships between production/attraction and explanatory variables by estimating the global, fixed coefficients. Considering that, trip generation is almost impressed by spatial factors which vary over the study area; the main objective of this research is to apply Mixed Geographically Weighted Regression (MGWR) on 253 traffic analysis zones (TAZs) in Mashhad, Iran, by applying travel demand data and relating factors in 2018 to investigate the spatial non-stationarity which are not revealed when global specifications are applied. The influence of certain explanatory variables on response variables may be global, whereas others are local, accordingly, MGWR performs better compared with geographically weighted regression. The results of Moran’s I as spatial autocorrelation index performing on residuals of global, mixed models proved the reliability of the proposed model over the traditional one. The spatial model indicated an improvement in model performance using goodness-of-fit criteria with the coefficient of determination varying from 0.84–0.95 compared with 0.76 and 0.6 in the conventional model. The results of such analysis can provide descriptive and predictive tools at the planning-level for decision-makers.

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The varying effects of accessing high-speed rail system on China’s county development: A geographically weighted panel regression analysis

Cited by 21 publications

References 61 publications

Seeing the forest and the trees: Holistic view of social distancing on the spread of COVID-19 in China

Seeing the forest and the trees: Holistic view of social distancing on the spread of COVID-19 in China

Land cover matters to human well-being

Explanatory Analyses of Work Trip Generation Using Mixed Geographically Weighted Regression (Mgwr)

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