The Social Sustainability of Smart Cities: Urban Technological Innovation, Big Data Management, and the Cognitive Internet of Things.
The key feature of current and envisioned data-based smart city advancements is not information and communications technology, statistics, or intelligent infrastructure, but the cutting-edge applications for value production for stakeholders. (Lim et al., 2018) An urban area can be transformed into a smart city by enhancing citizens' standard of living, sustainability, and work productivity through fashionable information and communication technology and Internet of Things. (Malik et al., 2018) Smart cities expand far and wide and materialize with comparable characteristics and interdependencies internationally. Smart cities represent a local experience, as each urban area is beyond compare, has distinct issues, and should tackle them with distinctive ways out. (Dameri et al., 2019)
2. Conceptual Framework and Literature Review
Big data analytics are decisive in carrying out the essential green characteristics of smart sustainable cities, i.e. the coherence of processes and services, the improvement of natural resources, and the judicious administration of infrastructures and facilities. (Bibri, 2018) Automated system supervising undertakings for a smart city need to be flexible to real-time information processing issues to carry out data analytics swiftly and precisely. (Malik et al., 2018) Fast expansion places large-scale requirements on city services and road and rail networks that require groundbreaking and sustainable ways out which gradually entail smooth monitoring, gathering, storage and interpretation of vast, disparate data. (Alabdulatif et al., 2019) Smart cities are established in standards of democratic representation, although the connection between political entities and citizens is not completely advanced because of the absence of mechanisms genuinely furthering the involvement and voicing of the general public. (Nesti and Graziano, 2019)
3. Methodology and Empirical Analysis
Building my argument by drawing on data collected from Black & Veatch, ESI ThoughtLab, Grand View Research, McKinsey, and Statista, I performed analyses and made estimates regarding the top three major challenges teams are facing with the current distribution system automation and communication capabilities (%), Internet of Things units installed base within smart cities in 2018 (by subgroup), EU smart governance market share (by sub-application, %), and potential improvement through current generation of smart city applications, from time of implementation (%). Multivariate statistics techniques have been applied for data analyses (e.g. structural equation modeling).
4. Results and Discussion
The big data applications facilitated by the Internet of Things may be adequate to an array of the spheres of smart sustainable cities in connection with their operational functioning, administration, and design in the framework of environmental sustainability. (Bibri, 2018) Significant frequency and amount of big data entailed in the smart city necessitate sustainable statistical projection while preserving its representation for generating real-time inferencing and analytical outcomes. (Malik et al., 2018) City processes can be enhanced by putting into effect ways out through adopting such applications, but building up data accessibility in association with robust analytic tools intensifies the risk of privacy breaches. (Curzon et al., 2019) Social and organizational features of smart cities security develop out of opposing concerns of distinct participants, high degrees of connection, and social and political intricacy. (Vitunskaite et al., 2019) (Tables 1-8)
5. Conclusions and Implications
The adoption of urban big data is instrumental in the production of statistics for stakeholders to carry out their processes more thoroughly and generate value. (Lim et al., 2018) The function of big data analytics is noticeable not only as regards bringing on and expanding the green development operations of smart sustainable cities, but also with reference to grasping, examining, evaluating, and planning such metropolitan areas in manners that judiciously enhance their input to the objectives of ecologically sustainable society. (Bibri, 2018) Information modeling transformed into remarkably appropriate patterns for inferencing and analytics represents a demanding and expensive undertaking (Georgiou and Rocco, 2017; Grossman, 2018; Havu, 2017; Michailidou, 2017; Nica et al., 2017; Ohanyan and Androniceanu, 2017; Popescu, 2018; Sion, 2018a, b) taking into account time limitations. Their natural grids are satisfactorily harmonized for real-time information analytics and inferencing in Internet of Things-based data online. (Malik et al., 2018)
This paper was supported by Grant GE-1795342 from the Social Analytics Laboratory, Los Angeles, CA.
The author confirms being the sole contributor of this work and approved it for publication.
Conflict of Interest Statement
The author declares 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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University of Social Sciences, Lodz, Poland; The Bucharest University of Economic Studies, Romania
How to cite: Androniceanu, Armenia (2019). "The Social Sustainability of Smart Cities: Urban Technological Innovation, Big Data Management, and the Cognitive Internet of Things," Geopolitics, History, and International Relations 11(1): 110-115. doi:10.22381/GHIR11120197
Received 1 December 2018 * Received in revised form 13 May 2019
Accepted 19 May 2019 * Available online 1 June 2019
Table 1 What role is your utility playing in your municipality's smart city initiatives? (%, Select one choice.) Support role 31.2 Leadership role 20.4 Not involved 19.8 Don't know 28.6 Sources: Black & Veatch; my survey among 5,200 individuals conducted November 2018. Table 2 The top three major challenges teams are facing with the current distribution system automation and communication capabilities (%, Select top three choices.) Cybersecurity 47.1 Old and obsolete equipment 35.7 Lack of staff to support future needs and requirements 35.4 Sources: Black & Veatch; my survey among 5,200 individuals conducted November 2018. Table 3 Internet of Things units installed base within smart cities in 2018 (by subgroup) Smart homes 1,074.2 Smart commercial buildings 1,066.1 Transport 518.6 Utilities 464.8 Public services 168.1 Others 33.3 Healthcare 13.9 Sources: Statista; my 2018 data. Table 4 Please tell us your city's stage of development in the use of data and data analytics in the following areas (%): Beginner Transitioning Leader Collecting data 4 58 97 Extracting data 4 49 93 Integrating data 1 41 99 Analyzing data 2 47 98 Providing a mix 16 53 96 of data Making data 2 50 84 accessible and usable Monetizing data 2 33 70 Sources: ESI ThoughtLab; my survey among 5,200 individuals conducted November 2018. Table 5 When adopting new technologies, what approach is your city most likely to take? (%) Beginner Transitioning Leader Outsource implementation 8.4 22.4 83.6 to consultants Partner with technology 30.1 51.8 77.1 providers Buy the technology 8.7 42.8 60.2 License the technology 28.2 41.9 59.4 Partner with academic 11.2 34.1 30.6 institutions Partner with service providers 36.2 32.2 24.9 Public-private partnerships 17.4 16.4 12.6 Develop/Operate systems 14.3 23.8 11.7 internally Outsource development 14.1 27.2 6.2 Sources: ESI ThoughtLab; my survey among 5,200 individuals conducted November 2018. Table 6 EU smart governance market share (by sub-application, %) City surveillance 27 Command and control solution 5 E-governance 24 Smart lighting 25 Smart infrastructure market 19 Sources: Grand View Research; my 2018 data. Table 7 Potential improvement through current generation of smart city applications, from time of implementation (%) Health Disease burden 13 Time and convenience Commute time 17 Time spent interacting with 60 healthcare and government Safety Fatalities 10 Crime incidents 35 Emergency response time 30 Cost of living Citizen expenditures 3 Jobs Formal employment 2 Social connectedness and Citizens feel connected to their local 16 civic participation community Citizens feel connected to their 20 local government Environmental quality GHG emissions 14 Water consumption 36 Unrecycled waste 15 Table 8 Which of the following types of data is your city currently using to drive smart city initiatives, and which do you plan to use over the next three years? (%) Technology Now 3 years Internet of Things 69 94 Real-time 62 91 Administrative 59 74 Local business 53 62 Social media 51 63 Geospatial 49 73 Behavioral 44 70 Channel use 43 57 Predictive data 36 64 Crowd-sourced 30 52 Psychographic 27 47 Artificial intelligence 12 66 Sources: ESI ThoughtLab; my survey among 5,200 individuals conducted November 2018.
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|Publication:||Geopolitics, History, and International Relations|
|Date:||Jun 1, 2019|
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