Problematizing data-driven urban practices: Insights from five Dutch ‘smart cities’

Bunders, Damion Jonathan; Varró, Krisztina

doi:10.1016/j.cities.2019.05.004

Cited by 51 publications

(26 citation statements)

References 45 publications

(62 reference statements)

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“…'Intelligent city' refers to the use of various information technologies or innovative concepts to connect and integrate urban systems and services to improve the efficiency of resource use optimize urban management and services and improve the quality of life of citizens [34]. Most cities have actively promoted the technological and information-based public service functions, which have also created objective conditions for the intelligentization of elderly care services [35,36]. Under the background of increasing aging, government and society strives to explore the potential of elderly care technology in the fields of biology psychology society and medical care and builds a comprehensive service platform for the elderly and health care to integrate medical services operators service providers and individuals.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Zhou

Tan

et al. 2020

Symmetry

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The neighborhood rough set theory was adopted for attributes reduction and the weight distribution of condition attributes based on the concept of importance level. Smart elderly care coverage rate is low in China. A decisive role in the adoption of smart elderly care is still a problem that needs to be addressed. This study contributes to the adoption of smart elderly care was selected as the decision attribute. The remaining attributes are used as conditional attributes and the multi-level symmetric attribute set for assessing acceptance of smart elderly care. Prior studies are not included smart elderly care adoption attributes in multi-levels; hence, this problem needs to be addressed. The results of this study indicate that the condition attribute of gender has the greatest influence on the decision attribute. The condition attribute of living expenses for smart elderly care has the second largest impact on decision attribute. Children’s support for the elderly decency of the novel elderly care system and the acceptance of non-traditional elderly care methods belong to the primary condition attribute of traditional concept. The result indicates traditional concepts have a certain impact on the adoption of smart elderly care and a condition attribute of residence also has a slight influence on the symmetric decision attribute. The sensitivity analysis shows the insights for uncertainties and provides as a basis for the analysis of the attributes in the smart elderly care service adoption.

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Section: Literature Reviewmentioning

confidence: 99%

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Zhou

Tan

et al. 2020

Symmetry

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“…In part, this confirms findings underlining the predominance of corporate power and visions in smart city development (Hollands 2015 ; Wiig 2015 ; Sadowski and Bendor 2019 ). At the same time, the City Brain case extends this because of the complex power geometries between city authorities and technology corporates (Bunders and Varró 2019 ). We wish to avoid presenting a straightforward narrative of corporate power normatively ‘parachuting’ smart city products into the city with little regard for geographical specificity, a point made in recent studies (Cowley and Caprotti 2019 ).…”

Section: Platforming and Experimenting With The Chinese Smart Citymentioning

confidence: 99%

Platform urbanism and the Chinese smart city: the co-production and territorialisation of Hangzhou City Brain

Caprotti

2020

GeoJournal

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We analyse an urban platform (Alibaba’s City Brain) to show how smart city development is evolving in urban China. In order to do so, we base our analysis on two strands of literature: that on platform urbanism, and on the experimental city. The paper identifies two processes that are shared across both bodies of work on platform urbanism and experimental cities: relational co-production and territorialisation. These processes can also be applied to the case of City Brain as both a platform and an urban experiment. We conclude by reflecting on the significance of urban platforms on the co-production of data-enabled urban governance; local urban context; and citizenship.

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“…Different drivers may lead to risks with smart city development. Based on the case of five smart cities, Bunders and Varro argue that big data can also bring development risks to smart cities [28]. Further, based on the reality of China, Zhu et al construct a disaster resilience evaluation index system including infrastructure, economy, society, the public, and the environment, and hold that the differences of above five aspects lead to the difference of disaster resilience capacity of smart cities in China [29].…”

Section: Datamentioning

confidence: 99%

Temporal and Spatial Differences in the Resilience of Smart Cities and Their Influencing Factors: Evidence from Non-Provincial Cities in China

et al. 2020

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Based on the sample data of 81 non-provincial smart cities in China in 2017, the comprehensive evaluation index of the resilience of sample cities is calculated by using the entropy method, and the spatial differences of different factors on the resiliency are analyzed by using the geographical weighted regression (GWR) model. The conclusions are as follows: Firstly, the comprehensive evaluation index of the resilience of smart cities presents a spatial distribution characteristic of decreasing from the east to the west. At the same time, the resilience comprehensive index, the public infrastructure resilience capacity index, the economic development resilience index, the social security resilience index, and the ecological environment resilience index of smart cities have obvious agglomeration effects on their geographical spaces. Secondly, the public infrastructure resilience capacity index and the ecological environment resilience index are both low with a discrete distribution, while the economic development resilience index and the social security resilience index are both high with a concentrated distribution. Thirdly, different factors have significantly positive effects on the resilience of smart cities. In particular, the public infrastructure capacity resilience index decreases from the north to the south with the spatial distribution pattern of concentration, the economic development resilience index and the ecological environment resilience index of smart cities decrease from the east to the west with a concentrated spatial distribution pattern, and the social security resilience index of smart cities decreases from the southwest to the northeast with a concentrated spatial distribution pattern. Therefore, it is necessary to enhance awareness of smart cities, strengthen the driving force of science and technology innovation, strengthen public infrastructure and service construction, and continuously improve the rapid resilience of smart cities.

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Problematizing data-driven urban practices: Insights from five Dutch ‘smart cities’

Cited by 51 publications

References 45 publications

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Using Neighborhood Rough Set Theory to Address the Smart Elderly Care in Multi-Level Attributes

Platform urbanism and the Chinese smart city: the co-production and territorialisation of Hangzhou City Brain

Temporal and Spatial Differences in the Resilience of Smart Cities and Their Influencing Factors: Evidence from Non-Provincial Cities in China

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