Shanghai: Urban Development and Regional Integration Through Mega Projects

Shen, Jianfa; Kee, Gordon

doi:10.1007/978-3-319-46421-3_5

Cited by 5 publications

(3 citation statements)

References 21 publications

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“…Changning, Huangpu, Putuo, Hongkou, Xuhui, Jingan, and Yangpu are situated in Puxi (Huangpu West). All of these seven districts are collectively called the downtown area or the city-center of Shanghai [26,37], as shown in Figure 1.…”

Section: Study Area and Datasetmentioning

confidence: 99%

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Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai

et al. 2020

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In recent decades, a large amount of research has been carried out to analyze location-based social network data to highlight their application. These location-based social network datasets can be used to propose models and techniques that can analyze and reproduce the spatiotemporal structures and symmetries in user activities as well as density estimations. In the current study, different density estimation techniques are utilized to analyze the check-in frequency of users in more detail from location-based social network dataset acquired from Sina-Weibo, also referred as Weibo, over a specific period in 10 different districts of Shanghai, China. The aim of this study is to analyze the density of users in Shanghai city from geolocation data of Weibo as well as to compare their density through univariate and bivariate density estimation techniques; i.e., point density and kernel density estimation (KDE) respectively. The main findings of the study include the following: (i) characteristics of users’ spatial behavior, the center of activity based on their check-ins, (ii) the feasibility of check-in data to explain the relationship between users and social media, and (iii) the presentation of evident results for regulatory or managing authorities for urban planning. The current study shows that the point density and kernel density estimation. KDE methods provide useful insights for modeling spatial patterns using geo-spatial dataset. Finally, we can conclude that, by utilizing the KDE technique, we can examine the check-in behavior in more detail for an individual as well as broader patterns in the population as a whole for the development of smart city. The purpose of this article is to figure out the denser places so that the authorities can divide the mobility of people from the same routes or at least they can control the situation from any further inconvenience.

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Section: Study Area and Datasetmentioning

confidence: 99%

“…Electronics 2020, 9, x FOR PEER REVIEW 4 of 16 districts are collectively called the downtown area or the city-center of Shanghai [26,37], as shown in Figure 1. The dataset used in our study is gathered from the Chinese microblog "Weibo."…”

Section: Study Area and Datasetmentioning

confidence: 99%

Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai

et al. 2020

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“…On the one hand, although Hong Kong and other leading Chinese cities serve differentiated roles in networking Chinese and global economies (Lai, 2012;Zhao, 2013), Hong Kong is increasingly facing direct competition with mainland Chinese cities, Beijing and Shanghai in particular. As a case in point, a national pilot Free Trade Zone (FTZ) was set up in Shanghai to pioneer financial reform, facilitate international trade, and attract foreign investment (Shen and Kee, 2017). Shanghai FTZ is directly modelled after Hong Kong and this new FTZ is expected to have direct impacts on Hong Kong's trading, tourism, and financial sectors (Ji et al, 2016).…”

Section: Change In World City Network 2010-2016mentioning

confidence: 99%

Changing Connectivities of Chinese Cities in the World City Network, 2010–2016

et al. 2018

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Against the backdrop of the sizable economic growth of China in recent years, this paper uses the most recent data gathering of the Globalization and World Cities (GaWC) research network to update and supplement earlier research on the shifting global connectivity of Chinese cities. The update consists of an evaluation of the connectivity of Chinese cities in 2016, the supplement of an analysis of the changing position of Chinese cities in the world city network between 2010 and 2016. To this end, we build on a specification of the world city network as an 'interlocking network' in which producer services firms play the crucial role in city network formation. Information about the presence of leading producer services firms in cities in 2010 and 2016 is used as the input to a bipartite network projection algorithm in order to measure cities' network connectivity. The first set of results discusses the geographies of urban connectivity in the world city network in 2016. The second set of results discusses standardized measures of change to reveal the major dimensions of the transformations between 2010 and 2016. We find that, with the exception of Hong Kong, Macau and Kaohsiung of Taiwan, all Chinese cities record connectivity gains. This wholesale rise in connectivity is nonetheless geographically uneven, with above all Beijing, Chengdu/Chongqing and Changsha/Wuhan becoming more connected. We conclude that the wholesale rise of Chinese cities in the world city network and their changing trajectories in the post-crisis era are embedded in shifting external and internal political economies.

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Appraisal of mixed-use developments through multiple criteria decision making: insights from Shanghai’s megacity context

Valero

2024

Cogent Business & Management

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Shanghai: Urban Development and Regional Integration Through Mega Projects

Cited by 5 publications

References 21 publications

Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai

Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai

Changing Connectivities of Chinese Cities in the World City Network, 2010–2016

Appraisal of mixed-use developments through multiple criteria decision making: insights from Shanghai’s megacity context

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