The Experts below are selected from a list of 347535 Experts worldwide ranked by ideXlab platform
Liu Dong - One of the best experts on this subject based on the ideXlab platform.
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competition game of high speed rail and civil aviation and its Spatial Effect a case study of beijing shanghai high speed rail
Economic Geography, 2013Co-Authors: Liu DongAbstract:The development of high-speed rail had a serious impact on air transport.It is great significant to research the competitive relationship between high-speed railway and civil aviation on the background of rapid development of high-speed railway in China.By considered the indicators Effectiveness of economic,speed,comfortable,convenient and safety,this paper used market share model to calculate the market share situation of high-speed rail and air transport in different transport distance.The results showed that 500-900km is a significant distance for the competition game of high-speed rail and air transport,and 692km is a market boundary distance for high-speed rail and air transport.Taking Beijing-Shanghai high-speed rail for example,by analyzing the relationship of airports’size and location,we can get the general competition rules between high-speed rail and air transport: high-speed rail’s impact on small city airport is greater than the large city airport,and the impact on the city airport of middle zone is greater than the both ends.On this basis,this paper analyzed the Spatial Effect caused by competition of high-speed railway and civil aviation for quantitative and qualitative.The conclusion showed that competition between high-speed railway and civil aviation will lead regional Spatial structure and regional development advantages to change inevitably,and make regional economic development more balanced.
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Competition Game of High-speed Rail and Civil Aviation and Its Spatial Effect—A Case Study of Beijing-Shanghai High-speed Rail
Economic Geography, 2013Co-Authors: Liu DongAbstract:The development of high-speed rail had a serious impact on air transport.It is great significant to research the competitive relationship between high-speed railway and civil aviation on the background of rapid development of high-speed railway in China.By considered the indicators Effectiveness of economic,speed,comfortable,convenient and safety,this paper used market share model to calculate the market share situation of high-speed rail and air transport in different transport distance.The results showed that 500-900km is a significant distance for the competition game of high-speed rail and air transport,and 692km is a market boundary distance for high-speed rail and air transport.Taking Beijing-Shanghai high-speed rail for example,by analyzing the relationship of airports’size and location,we can get the general competition rules between high-speed rail and air transport: high-speed rail’s impact on small city airport is greater than the large city airport,and the impact on the city airport of middle zone is greater than the both ends.On this basis,this paper analyzed the Spatial Effect caused by competition of high-speed railway and civil aviation for quantitative and qualitative.The conclusion showed that competition between high-speed railway and civil aviation will lead regional Spatial structure and regional development advantages to change inevitably,and make regional economic development more balanced.
Thomas Plumper - One of the best experts on this subject based on the ideXlab platform.
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spspc stata module to create specific source or target contagion Spatial Effect variable for directed dyadic data
Research Papers in Economics, 2013Co-Authors: Eric Neumayer, Thomas PlumperAbstract:spspc generates a specific source or target contagion Spatial Effect variable for analysis of Spatial dependence in directed dyad data. It can create Spatial Effect variables for Spatial lag, Spatial-x and Spatial error models. See http://personal.lse.ac.uk/neumayer/Spatial.pdf for an explanation of what constitutes specific source and target contagion. Requires installation of the mmerge package (q.v.)
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Making Spatial Analysis Operational: Commands for Generating Spatial Effect Variables in Monadic and Dyadic Data
Social Science Research Network, 2010Co-Authors: Eric Neumayer, Thomas PlumperAbstract:Spatial dependence exists whenever the expected utility of one unit of analysis is affected by the decisions or behavior made by other units of analysis. If so, Spatial dependence is ubiquitous in social relations and interactions. Yet, there are surprisingly few social science studies accounting for Spatial dependence. This holds true for settings in which researchers use monadic data, where the unit of analysis is the individual unit, agent or actor, and even more so for dyadic data settings, where the unit of analysis is the pair or dyad representing an interaction or a relation between two individual units, agents or actors. Dyadic data offer more complex ways of modeling Spatial Effect variables than monadic data. The ado-files described in this article facilitate Spatial analysis by providing an easy tool for generating, with one command line, Spatial Effect variables for monadic contagion as well as for all possible forms of contagion in dyadic data.
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spdir stata module to create directed dyad contagion Spatial Effect variable
Statistical Software Components, 2009Co-Authors: Eric Neumayer, Thomas PlumperAbstract:spdir generates a directed dyad contagion Spatial Effect variable for analysis of Spatial dependence in directed dyad data. It can create Spatial Effect variables for Spatial lag, Spatial-x and Spatial error models. See http://personal.lse.ac.uk/neumayer/Spatial.pdf for an explanation of what constitutes directed dyad contagion. Requires installation of the mmerge package (q.v.)
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SPMON: Stata module to create Spatial Effect variable for monadic data
Statistical Software Components, 2009Co-Authors: Eric Neumayer, Thomas PlumperAbstract:spmon generates a Spatial Effect variable for analysis of Spatial dependence in monadic data, i.e. where the estimation dataset consists of individual units (as in the vast majority of datasets used in the social sciences), rather than of dyads (pairs of units). It can create Spatial Effect variables for Spatial lag, Spatial-x and Spatial error models.
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spagg stata module to create aggregate source or target contagion Spatial Effect variable for directed dyadic data
Statistical Software Components, 2009Co-Authors: Eric Neumayer, Thomas PlumperAbstract:spagg generates an aggregate source or target contagion Spatial Effect variable for analysis of Spatial dependence in directed dyad data. It can create Spatial Effect variables for Spatial lag, Spatial-x and Spatial error models. See http://personal.lse.ac.uk/neumayer/Spatial.pdf for an explanation of what constitutes aggregate source and target contagion. Requires installation of the mmerge package (q.v.)
Eric Neumayer - One of the best experts on this subject based on the ideXlab platform.
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spspc stata module to create specific source or target contagion Spatial Effect variable for directed dyadic data
Research Papers in Economics, 2013Co-Authors: Eric Neumayer, Thomas PlumperAbstract:spspc generates a specific source or target contagion Spatial Effect variable for analysis of Spatial dependence in directed dyad data. It can create Spatial Effect variables for Spatial lag, Spatial-x and Spatial error models. See http://personal.lse.ac.uk/neumayer/Spatial.pdf for an explanation of what constitutes specific source and target contagion. Requires installation of the mmerge package (q.v.)
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Making Spatial Analysis Operational: Commands for Generating Spatial Effect Variables in Monadic and Dyadic Data
Social Science Research Network, 2010Co-Authors: Eric Neumayer, Thomas PlumperAbstract:Spatial dependence exists whenever the expected utility of one unit of analysis is affected by the decisions or behavior made by other units of analysis. If so, Spatial dependence is ubiquitous in social relations and interactions. Yet, there are surprisingly few social science studies accounting for Spatial dependence. This holds true for settings in which researchers use monadic data, where the unit of analysis is the individual unit, agent or actor, and even more so for dyadic data settings, where the unit of analysis is the pair or dyad representing an interaction or a relation between two individual units, agents or actors. Dyadic data offer more complex ways of modeling Spatial Effect variables than monadic data. The ado-files described in this article facilitate Spatial analysis by providing an easy tool for generating, with one command line, Spatial Effect variables for monadic contagion as well as for all possible forms of contagion in dyadic data.
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spdir stata module to create directed dyad contagion Spatial Effect variable
Statistical Software Components, 2009Co-Authors: Eric Neumayer, Thomas PlumperAbstract:spdir generates a directed dyad contagion Spatial Effect variable for analysis of Spatial dependence in directed dyad data. It can create Spatial Effect variables for Spatial lag, Spatial-x and Spatial error models. See http://personal.lse.ac.uk/neumayer/Spatial.pdf for an explanation of what constitutes directed dyad contagion. Requires installation of the mmerge package (q.v.)
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SPMON: Stata module to create Spatial Effect variable for monadic data
Statistical Software Components, 2009Co-Authors: Eric Neumayer, Thomas PlumperAbstract:spmon generates a Spatial Effect variable for analysis of Spatial dependence in monadic data, i.e. where the estimation dataset consists of individual units (as in the vast majority of datasets used in the social sciences), rather than of dyads (pairs of units). It can create Spatial Effect variables for Spatial lag, Spatial-x and Spatial error models.
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spagg stata module to create aggregate source or target contagion Spatial Effect variable for directed dyadic data
Statistical Software Components, 2009Co-Authors: Eric Neumayer, Thomas PlumperAbstract:spagg generates an aggregate source or target contagion Spatial Effect variable for analysis of Spatial dependence in directed dyad data. It can create Spatial Effect variables for Spatial lag, Spatial-x and Spatial error models. See http://personal.lse.ac.uk/neumayer/Spatial.pdf for an explanation of what constitutes aggregate source and target contagion. Requires installation of the mmerge package (q.v.)
Chunmei Mao - One of the best experts on this subject based on the ideXlab platform.
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Spatial Effect of Industrial Energy Consumption Structure and Transportation on Haze Pollution in Beijing-Tianjin-Hebei Region.
International journal of environmental research and public health, 2020Co-Authors: Chunmei MaoAbstract:Haze pollution has a serious impact on China's economic development and people's livelihood. We used data on PM2.5 concentration, industrial energy consumption structure, economic development and transportation in Beijing-Tianjin-Hebei and surrounding cities from 2000 to 2017, and analyzed the Spatial Effect of industrial energy consumption structure and traffic factors on haze pollution by using Spatial autoregressive model (SAR) and Spatial error model (SEM). The results indicated that: (1) The global Spatial correlation analysis showed that haze pollution had a significant positive Spatial correlation, and the local Spatial correlation analysis showed that the high-high clusters of PM2.5 were located in the south and middle of the region; (2) The change of industrial energy consumption structure was highly correlated with haze pollution, namely, the increase of industrial energy consumption led to the deterioration of environmental quality; (3) The change of economic development was highly correlated with haze pollution. There was no clear EKC relationship between haze pollution and economic development in Beijing-Tianjin-Hebei region and surrounding cities. However, the relationship was similar to inverted U-shaped curve; (4) The change of traffic jam was highly correlated with haze pollution, namely, the increase of fuel consumption per unit road area led to the deterioration of environmental quality. Based on the above results, from the perspective of space, the long-term measures for haze control in Beijing-Tianjin-Hebei and surrounding cities can be explored from the aspects of energy conservation and emission reduction, industrial transfer, vehicle emission control, traffic restrictions and purchase restrictions.
Zhihua Wang - One of the best experts on this subject based on the ideXlab platform.
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Spatial Effect Analysis on Shaking Table Tests of Subway Station Structure in Liquefiable Ground
Applied Mechanics and Materials, 2011Co-Authors: Guo Xing Chen, Xi Zuo, Zhihua WangAbstract:Based on the test data of shaking table tests of subway station structure in liquefiable ground under both near-field and far-field earthquakes, the Spatial Effects of dynamic pore water pressure (PWP)and peak ground acceleration (PGA)of liquefiable ground as well as peak strain response of the subway station structure are analyzed. The results show that there exists time-lag phenomenon of dynamic PWP ratio of each measuring point on different observation planes. The characteristic of input ground motion has a noticeable influence on the Spatial Effect of dynamic PWP ratio. The PWP ratio obtained on the major observation plane presents to be larger than that on the minor one when under far-field Songpan wave. Meanwhile, the peak acceleration of measuring points on both major and minor planes increase with the growing peak acceleration of earthquake. The law of PGA and frequency spectral character of measuring points on different observation planes or at different depth varies with each other, and there present remarkable Spatial Effect. The peak strain of central cylinders at the top and middle floors on the major plane appear larger than that on the minor planes. However, the peak strain of central cylinders at the bottom floor are more remarkable than that of the top and middle floors, There are sharp contrasts among the peak strain responses in different Spatial positions of the station structure.
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Spatial Effect Analysis on Shaking Table Tests of Subway Station Structure in Soft Ground
Advanced Materials Research, 2011Co-Authors: Xi Zuo, Guo Xing Chen, Zhihua WangAbstract:Based on the test data of shaking table tests of subway station structure in soft ground under both near-field and far-field earthquakes, the Spatial Effects of peak ground acceleration (PGA) of soft ground as well as peak strain response of the subway station structure are analyzed. The results show that the peak acceleration of measuring points on both major and minor planes increases with the growing peak acceleration of earthquake. The law of PGA and frequency spectral character of measuring points on different observation planes or at different depth varies with each other, and there presents remarkable Spatial Effect. The peak strain of central cylinders on the major plane appears larger than that on the minor planes. There are sharp contrasts among the peak strain responses in different Spatial positions of the station structure.
