The impact of urban characteristics and residents’ income on commuting in China

Zhu, Zhenya; Li, Zhigang; Liu, Ye; Chen, Hongsheng; Zeng, Jun

doi:10.1016/j.trd.2017.09.015

Cited by 60 publications

(55 citation statements)

References 20 publications

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“…Based on group dimensions, as a high-income group, cluster 3 has a lower correlation between income and commuting distance, as marked by bold. This indicates that high-income passengers may have a longer trip to work; this relationship has been indicated in previous studies as well [11,26,27]. Further, the correlation with shopping expenditure is lower or even negative, which indicates that high-income passengers do not prefer to take the metro to shop.…”

Section: Discussionsupporting

confidence: 63%

“…In addition, traditional trip survey data has shown that passengers' income is correlated with their commuting distance [11,26]. Because the urbanization rate significantly impacts the commuting time for different income levels [27], different relationships have been found in studies of different countries [11]. Therefore, this relationship provides a method to infer individual economic attributes using data without user profiles.…”

Section: Literature Reviewmentioning

confidence: 99%

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Inferring the Economic Attributes of Urban Rail Transit Passengers Based on Individual Mobility Using Multisource Data

Zhu

Feng

et al. 2018

Sustainability

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Socioeconomic attributes are essential characteristics of people, and many studies on economic attribute inference focus on data that contain user profile information. For data without user profiles, like smart card data, there is no validated method for inferring individual economic attributes. This study aims to bridge this gap by formulating a mobility to attribute framework to infer passengers’ economic attributes based on the relationship between individual mobility and personal attributes. This framework integrates shop consumer prices, house prices, and smart card data using three steps: individual mobility extraction, location feature identification, and economic attribute inference. Each passenger’s individual mobility is extracted by smart card data. Economic features of stations are described using house price and shop consumer price data. Then, each passenger’s comprehensive consumption indicator set is formulated by integrating these data. Finally, individual economic levels are classified. From the case study of Beijing, commuting distance and trip frequency using the metro have a negative correlation with passengers’ income and the results confirm that metro passengers are mainly in the low- and middle-income groups. This study improves on passenger information extracted from data without user profile information and provides a method to integrate multisource big data mining for more information.

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Section: Discussionsupporting

confidence: 63%

Section: Literature Reviewmentioning

confidence: 99%

Inferring the Economic Attributes of Urban Rail Transit Passengers Based on Individual Mobility Using Multisource Data

Zhu

Feng

et al. 2018

Sustainability

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“…Other industry accounted for 17% of the total emissions in 2002, and this figure continually declined into 13% in 2012, while general & special equipment emitted 5% of the total emissions in 2002, and this percentage consistently increased to 8% in 2012. The portion resulting is reasonable, as consumers in China become more prosperous than before, and they have larger purchasing power for durable goods, like computers, electrical equipment and transportation equipment, etc (Zhang et al, 2017c;Zhu et al, 2017). Meanwhile, following the improvement of Chinese residents' energy conservation awareness, more energy-saving products, like electricity saving lamps, energy-saving building materials, etc, were utilized (Lo, 2013;Lin and Wang, 2015;Sun et al, 2017), resulting in the less significance for pollutant emissions from other industry.…”

Section: Production-and Consumption-based So2 Emissions During 2002-2012mentioning

confidence: 99%

The temporal variation of SO2 emissions embodied in Chinese supply chains, 2002–2012

Yang

Zhang

Fan

et al. 2018

Environmental Pollution

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Whilst attention is increasingly being focused on embodied pollutant emissions along supply chains in China, relatively little attention has been paid to dynamic changes in this process. This study utilized environmental extended input-output analysis (EEIOA) and structural path analysis (SPA) to investigate the dynamic variation of the SO emissions embodied in 28 economic sectors in Chinese supply chains during 2002-2012. The main conclusions are summarized as follows: (1) The dominant SO emission sectors differed under production and consumption perspectives. Electricity and heat production dominated SO emissions from the point of view of production, while construction contributed most from the consumption perspective. (2) The embodied SO emissions tended to change from the path (staring from consumption side to production side): "Services→Services→Power" in 2002 to the path: "Construction and Manufacturing→Metal and Nonmetal→Power" in 2012. (3) Metal-driven emissions raised dramatically from 15% in 2002 to 22% in 2012, due to increasing demand for metal products in construction and manufacturing activities. (4) Power generation was found to result in the greatest volume of production-based emissions, a burden it tended to transfer to upstream sectors in 2012. Controlling construction activities and cutting down end-of-pipe discharges in the process of power generation represent the most radical interventions in reducing Chinese SO emissions. This study shed light on changes in SO emissions in the supply chain, providing a range of policy implications from both production and consumption perspectives.

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“…Looking at the five approaches that discuss urban islands, in principle, the researchers argue that urban island is not only seen from the size of a city but can also be seen from its role and function as a city. Where the characteristics of a city include the level of urbanization, population density, and urban expansion [35].…”

Section: B Review Of Articles In Urban Island Studiesmentioning

confidence: 99%

Urban Island Concept and The Relevance for Archipelagic States: Case of Ambon City-Maluku Province

Nurhaeny¹,

Miharja²,

Pradono³

2019

Proceedings of the International Conference on Maritime and Archipelago (ICoMA 2018)

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The recent studies of Island tend to focus on peripheral, isolated, and marginality aspects of the island community in mainland context. However, the understanding about interactions of several elements in archipelago development has to encompass broader context, including marine space and the others island which create a spatial system called urban island concept. The formation process of an urban island, comprising "strongly urbanized small islands or archipelagos" or "major population centers of large islands or archipelagos" are also essential issues that have not been fully explored. The issues of urban islands have become the concern of island researchers, such as in Copenhagen,

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The impact of urban characteristics and residents’ income on commuting in China

Cited by 60 publications

References 20 publications

Inferring the Economic Attributes of Urban Rail Transit Passengers Based on Individual Mobility Using Multisource Data

Inferring the Economic Attributes of Urban Rail Transit Passengers Based on Individual Mobility Using Multisource Data

The temporal variation of SO2 emissions embodied in Chinese supply chains, 2002–2012

Urban Island Concept and The Relevance for Archipelagic States: Case of Ambon City-Maluku Province

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