The spatial characteristics and influencing factors of modal accessibility gaps: A case study for Guangzhou, China

Yang, Wenyue; Chen, Bi Yu; Cao, Xiaoshu; Li, Tao; Li, Peng

doi:10.1016/j.jtrangeo.2017.02.005

Cited by 73 publications

(41 citation statements)

References 39 publications

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“…We first estimate a standard linear panel data model devoid of spatial effects. This model can be used as a reference for the estimation results of spatial panel data models as well as to check the robustness of these estimation results (Yang, Chen, Cao, Li, & Li, 2017). The formulation of a standard linear regression model (SLM) is as follows (Lan, 2012;Tatoglu, 2012)…”

Section: Methodsmentioning

confidence: 99%

“…= + ′ + + = λWε + , (5) In the above equation, corresponding to various research functions and needs, SWM W1 and SWM W2 (spatial weight matrix) can be the same or different (LeSage, 2008;Yang et al, 2017). In this study, we used the same SWM to estimate the model, that is, 1 = 2 = , and the residual terms are the same as those revealed above.…”

Section: Methodsmentioning

confidence: 99%

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Determining the spatial effects of COVID-19 using the spatial panel data model

2020

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This study investigates the propagation power and effects of the coronavirus disease 2019 in light of published data. We examine the factors affecting COVID-19 together with the spatial effects, and use spatial panel data models to determine the relationship among the variables including their spatial effects. Using spatial panel models, we analyse the relationship between confirmed cases of COVID-19, deaths thereof, and recovered cases due to treatment. We accordingly determine and include the spatial effects in this examination after establishing the appropriate model for COVID-19. The most efficient and consistent model is interpreted with direct and indirect spatial effects.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Determining the spatial effects of COVID-19 using the spatial panel data model

2020

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“…Areas with high surface vitality tend to exchange more intense energy streams and produce more NO 2 emissions. (6) For DEM, different terrain conditions (elevation) inevitably affect the intensity of human activity and the level of NO 2 emissions [52]. Besides, since the economic statistics are published on an annual basis, the NO 2 column concentration and monthly nighttime light data are synthesized annually in the impact factors analysis.…”

Section: Filtration Of Impact Factorsmentioning

confidence: 99%

Spatial Variation of NO2 and Its Impact Factors in China: An Application of Sentinel-5P Products

et al. 2019

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As an important tropospheric trace gas and precursor of photochemical smog, the accumulation of NO2 will cause serious air pollution. China, as the largest developing country in the world, has experienced a large amount of NO2 emissions in recent decades due to the rapid economic growth. Compared with the traditional air pollution monitoring technology, the rapid development of the remote sensing monitoring method of atmospheric satellite has gradually become the critical technical means of global atmospheric environmental monitoring. To reveal the NO2 pollution situation in China, based on the latest NO2 products from Sentinel-5P TROPOMI, the spatial–temporal characteristics and impact factors of troposphere NO2 column concentration of mainland China in the past year (February 2018 to January 2019) were analyzed on two administrative levels for the first time. Results show that the monthly fluctuation of tropospheric NO2 column concentration has obvious characteristics of “high in winter and low in summer", while the spatial distribution forms a "high in East and low in west” pattern, bounded by Hu Line. The comparison of Coefficient of Variation (CV) and spatial autocorrelation models at two kinds of administrative scales indicates that although the spatial heterogeneity of NO2 column concentration is less affected by the observed scale, there is a “delayed effect” of about one month in the process of NO2 column concentration fluctuation. Besides, the impact factors analysis based on Spatial Lag Model (SLM) and Geographic Weighted Regression (GWR) reveals that there is a positive correlation between nighttime light intensity, the secondary and tertiary industries proportion and NO2 column concentration. Furthermore, for regions with serious NO2 pollution in North China Plain, the whole society electricity consumption and vehicle ownership also play a positive role in increasing the NO2 column concentration. This study will enlighten the government and policy makers to formulate policies tailored to local conditions, to more effectively implement NO2 emission reduction and air pollution prevention.

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“…Place-based accessibility measures, such as cumulative opportunity and gravitybased measures, are valuable and require only a few aggregated data (Geurs and van Wee 2004;B. Y. Chen, Yuan, et al 2017;Yang et al 2017). These place-based measures, however, consider only land use and transportation components and have been criticized for ignoring distinct individual human mobility patterns of different socioeconomic groups.…”

mentioning

confidence: 99%