The Digital Governance of Smart City Networks: Information Technology-driven Economy, Citizen-centered Big Data, and Sustainable Urban Development.
Sensor technology is instrumental in the Internet of Things-enabled information processing as regards gathering and assessing data throughout urban spheres preceding their analysis. (Bibri, 2018) The notion of smart city is attained via realtime city associated intelligent decisions by examining the data collected from diverse smart urban systems, harnessing masses of linked sensors and devices that produce massive quantities of swift streaming information. (Rathore et al., 2018) Smart cities endeavor to furnish an enhanced standard of living to citizens, further economic growth, set up a sustainable approach to advancement, and provide coherent service delivery. (Alkhatib et al., 2019)
2. Conceptual Framework and Literature Review
The smart city notion focuses on the belief of incorporating advanced data and communication technology ways out in the structure of future cities to supply innovative and superior services to citizens while reducing the metropolitan management expenses in financial, social, and environmental terms. (Chiariotti et al., 2018) The urban big data facilitated by the Internet of Things are gradually becoming related totally with regularly and automatically sensed information, because established datasets are likely to be harmonized with repetitive and self-activating diagnosis. (Bibri, 2018) The production of city data at a distant place and then conveying it to central urban servers for examination purpose raises the issues of security and privacy. (Rathore et al., 2018)
3. Methodology and Empirical Analysis
Using and replicating data from ESI ThoughtLab and McKinsey, we performed analyses and made estimates regarding the impact of data-driven smart city applications in distinct urban settings. Data were analyzed using structural equation modeling.
4. Results and Discussion
Communication technologies are controlled by the smart city services, furnishing the procedures to gather and process the information required to make the services operate. (Chiariotti et al., 2018) The epoch of urban digitalization have generated a massive quantity of datasets and data flows, related to the metropolitan settings, information that must be collected and inspected from diverse resources in smart cities. (Honarvar and Sami, 2018) The capacity to legitimize and encompass citizens is decisive in revealing types of smart-sustainable urban advancement that highlight environmental protection and social justice, instead of simply supporting neoliberal kinds of urban development. (Martin et al., 2018) (Tables 1-6)
5. Conclusions and Implications
Smart cities have embraced Internet of Things as a way to stimulate coherence and operation of urban fabrics. (Allam and Dhunny, 2019) Fog and edge computing is a complementary information processing pattern to cloud computing in the framework of smart sustainable cities as regards the Internet of Things-enabled big data applications. (Bibri, 2018) Supplying protection to big data streaming necessitates a fast-moving security system that can operate in a real-time setting without generating any discontinuation that may impede the entire functioning of the smart city system. (Rathore et al., 2018) A groundbreaking crucial regulation for greening and smarting cities, to cut down the environmental impact of their operation, raise employment and economic feasibility (Androniceanu and Popescu, 2017; Balica, 2018; De Gregorio Hurtado, 2017; Douglas, 2018; Grossman, 2018; Popescu, 2018; Popescu Ljungholm, 2018; Radulescu, 2018; Vochozka et al., 2018), and to improve the standard of living, entails a complete evaluation of sustainability and smart urban performance. (Shmelev and Shmeleva, 2018)
This paper was supported by Grant GE-1753672 from the Digital Dynamics Laboratory, Miami, FL.
All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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The Bonn Center for IoT Economy
at AAER, Germany
Faculty of Operation and Economics of Transport and Communications, Department of Economics, University of Zilina, Zilina, Slovak Republic
Department of Economics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, Zilina, Slovak Republic
The Institute of Technology and Business in Ceske Budejovice, The School of Expertness and Valuation, Czech Republic
How to cite: Hecht, Bart, Katarina Valaskova, Pavol Kral, and Zuzana Rowland (2019). "The Digital Governance of Smart City Networks: Information Technology-driven Economy, Citizen-centered Big Data, and Sustainable Urban Development," Geopolitics, History, and International Relations 11(1): 128-133. doi:10.22381/GHIR111201910
Received 24 December 2018 * Received in revised form 27 May 2019
Accepted 28 May 2019 * Available online 1 June 2019
Table 1 Which of the following digital technologies does your city currently actively use to support operations? (%) Now 3 years Cloud-based technology 93 95 Mobile apps 86 90 City-wide data platform 71 79 IoT/Sensors/Wearables 60 90 Biometrics/Facial recognition 57 76 Geospatial technology 54 81 Low-powered area wide networks 51 67 Collaborative open source platforms 49 67 Telematics 29 69 Chatbots/Natural language processing 22 47 Smart beacons/Near field communications 16 56 V2X 11 35 Artificial intelligence/Machine learning 10 39 Augmented and virtual reality 7 53 Drones and robots 5 28 Blockchain 4 39 Sources: ESI ThoughtLab; our survey among 5,600 individuals conducted November 2018. Table 2 Please rate the following obstacles that your city faces when implementing smart city plans (%) Beginner Little sense of urgency 33.6 Complexity of procurement 31.7 Political and union challenges 28.4 Lack of culture to drive innovations 27.2 Uncertain ROI 24.4 Transitioning Concerns about cybersecurity 45.2 Uncertain ROI 30.8 Complexity of procurement 26.4 Difficulty in coordinating across departments 21.8 Desire to avoid disruption in operations 19.3 Leader Uncertain ROI 51.4 Concerns about cybersecurity 38.6 Difficulty in coordinating across departments 37.4 Inadequate infrastructure/inflexible legacy systems 29.4 Smart city initiatives seen as helping the rich, not the poor 29.2 Sources: ESI ThoughtLab; our survey among 5,600 individuals conducted November 2018. Table 3 What level of priority does your city place on each of the following smart city dimensions? (%) Beginner Transitioning Leader Smart mobility 80 86 99 Smart environment 54 87 85 Smart governance 42 82 92 Smart public safety 50 77 87 Smart infrastructure 47 76 62 Smart economy 45 71 91 Smart public health 34 76 87 Smart payment systems 19 52 74 Smart talent/education 21 49 62 Smart financing/budget 17 48 53 Sources: ESI ThoughtLab; our survey among 5,600 individuals conducted Table 4 How are your smart city investments distributed across the following areas? (%) Beginner Transitioning Leader Mobility 14.9 14.9 15.4 Environment 13.2 14.4 14.7 Governance 12.9 14.2 15.9 Infrastructure 14.4 9.7 9.2 Economy 7.2 8.3 9.4 Public safety 8.1 7.8 10.8 Health 7.8 7.7 8.6 Budget 7.1 7.6 6.7 Payments 6.5 7.4 7.7 Talent 7.2 6.2 5.8 Sources: ESI ThoughtLab; our survey among 5,600 individuals conducted November 2018. Table 5 Which of the following best describes your city's use of technology? (%) Beginner Transitioning Leader Broadband 9 54 95 Connected assets 2 41 93 Digital transformation process 1 40 84 Innovation hub 16 53 91 Inter-operability/Shared architecture 6 44 90 Scalable 2 32 87 Stakeholder interactions 5 35 88 Technology resources 4 39 83 Technology standards 2 41 86 Technology procurement 1 44 84 Sources: ESI ThoughtLab; our survey among 5,600 individuals conducted November 2018. Table 6 The impact of data-driven smart city applications in distinct urban settings Baseline characteristics North America South America Income High Medium Fatalities rate Low High Crime incidents rate Low High Average emergency response time Low Medium Average commute time Low Medium Average time in government Low High and healthcare Overall disease burden per capita Low Medium GHG emissions per capita High Low Water consumption per capita High Medium Unrecycled waste per capita High Medium Formal employment rate High Medium Average annual household expenditures High Medium Baseline characteristics Africa Income Low Fatalities rate High Crime incidents rate High Average emergency response time High Average commute time High Average time in government Medium and healthcare Overall disease burden per capita High GHG emissions per capita Low Water consumption per capita Low Unrecycled waste per capita Low Formal employment rate Low Average annual household expenditures Low Sources: McKinsey; our 2018 data.
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|Author:||Hecht, Bart; Valaskova, Katarina; Kral, Pavol; Rowland, Zuzana|
|Publication:||Geopolitics, History, and International Relations|
|Date:||Jun 1, 2019|
|Previous Article:||The Sustainable Development of Data-driven Smart Cities: Citizen-centered Urban Governance and Networked Digital Technologies.|