RESUMO
Presently, China has the largest high-speed rail (HSR) system in the world. However, our understanding of the network structure of the world's largest HSR system remains largely incomplete due to the limited data available. In this study, a publicly available data source, namely, information from a ticketing website, was used to collect an exhaustive dataset on the stations and routes within the Chinese HSR system. The dataset included all 704 HSR stations that had been built as of June, 2016. A classical set of frequently used metrics based on complex network theory were analyzed, including degree centrality, betweenness centrality, and closeness centrality. The frequency distributions of all three metrics demonstrated highly consistent bimodal-like patterns, suggesting that the Chinese HSR network consists of two distinct regimes. The results indicate that the Chinese HSR system has a hierarchical structure, rather than a scale-free structure as has been commonly observed. To the best of our knowledge, such a network structure has not been found in other railway systems, or in transportation systems in general. Follow-up studies are needed to reveal the formation mechanisms of this hierarchical network structure.
Assuntos
Modelos Teóricos , Ferrovias , ChinaRESUMO
The composition and structure of urban landscape and human activity intensity are key factors shaping urban thermal fields, whereas the relative importance of influencing factors for urban thermal distribution remains unclear. We carried out a case study in Yixing City. Land surface temperature (LST), ecological infrastructure (including vegetation and water cover), building volume and point of interest data were extracted from the RS interpretation, field mapping and programming technique. Using Pearson correlation analysis, univariete regression analysis, multiple regression analysis and relative weight analysis, we quantitatively analyzed the relationships between urban land surface temperature to ecological infrastructure, building volume, POI density at multiple scales (500, 1000, 2000 m) as well as their relative importance. The results showed that ecological infrastructure had a significant cooling effect, and the building volume and POI density were positively correlated with LST. Among the influence factors of urban heat field, ecological infrastructure had the highest relative weight (21.3%-43.8%), followed by building volume (20.7%-22.6%) and POI density (13.7%-21.7%). Our results would help to understand the relative importance of factors driving urban thermal field and offer important reference for taking mitigation measures to alleviate urban heat island effect.
