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The Digital Governance of Smart City Networks: Information Technology-driven Economy, Citizen-centered Big Data, and Sustainable Urban Development.

1. Introduction

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.

Author Contributions

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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Bart Hecht

The Bonn Center for IoT Economy

at AAER, Germany

Katarina Valaskova

Faculty of Operation and Economics of Transport and Communications, Department of Economics, University of Zilina, Zilina, Slovak Republic

Pavol Kral

Department of Economics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, Zilina, Slovak Republic

Zuzana Rowland

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 (%)


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


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


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
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