Development of Data Driven Smart Cities

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Yamuna Kaluarachchi	--	2744	2022-04-13 11:38:07	\|
2	format	Jason Zhu	Meta information modification	2744	2022-04-14 04:31:43	\|

This entry is adapted from the peer-reviewed paper 10.3390/smartcities5020025

Cities are investing in data-driven smart technologies to improve performance and efficiency and to generate a vast amount of data. Finding the opportunities to innovatively use this data help governments and authorities to forecast, respond, and plan for future scenarios. Access to real-time data and information can provide effective services that improve productivity, resulting in environmental, social, and economic benefits. It also assists in the decision-making process and provides opportunities for community engagement and participation by improving digital literacy and culture. Smart people, smart living, and smart governance methods that have come into practice at a later stage are as important as smart mobility, smart environments, and smart economy measures that were implemented early on, and cities are opening up to new, transparent participatory governance approaches where citizens play a key role. It also illustrates that the current new wave of smart cities with real time data are promoting citizen participation focusing on human, social capital as an essential component in future cities.

smart cities future cities data-driven applications

1. Introduction

Cities contain 54% of the world’s population and this number is expected to increase to 66% by 2050 ^[1]. This projected growth has led to investment plans in infrastructure for transport, water, electricity, and telecommunications to be estimated at $53 trillion from 2010 to 2030 ^[2]. Climate change will have severe repercussions for many cities, and resilience to both natural and man-made disasters continue to be an area of great concern ^[3]. In response to these major challenges, cities face increasing environmental stress and infrastructure needs, together with growing demands from residents to deliver and achieve a better quality of life ^[4]^[5].

There is a global drive to incorporate technology to improve functions and performances of urban cities. Since 1990, the development of the World Wide Web (WWW) and Information Communication Technologies (ICTs) have created opportunities for communication, engagement, and information sharing by local, regional, and national ^[6]. Many international institutions and forums believe in an ICT-driven form of development. The Intelligent Community Forum ^[7], for example, produces research on the local effects of the ICT revolution, and it now has a global spread. Many countries, especially European, have dedicated efforts to formulating strategies for achieving urban growth in a “smart” way for their metropolitan areas ^[8]. The role of innovation in ICT sectors is recognized, providing toolkits to develop and identify indicators, thus shaping a sound framework of analysis for researchers for urban innovations ^[9]. These innovations have resulted in introducing smart city movements, which have become an important part of the urban agenda and discourse ^[10]^[11]. Smart city is understood to be a fuzzy concept ^[8], and literature on smart cities provides several characterizations. A sustainable process of urban transformation into a smart city requires co-operation of many agencies, support by ICT infrastructures, and the integration of sustainable development, green growth, and collaborations between multi-stakeholders on multiple levels. The relationship between ICT infrastructure and economic performance has been extensively researched ^[12]. Other definitions stress the role of human capital and education in city growth ^[13], and they illustrate that the most rapid urban growth rates have been achieved in cities where an educated labor force is available. Berry and Glaeser ^[14] modeled the relationship between human capital and urban development by assuming that innovation is driven by entrepreneurs who innovate in industries and products that require an increasingly more skilled labor force. Holland ^[15] states that a smart city is utilization of networked infrastructure to improve economic and political efficiency that enable social, cultural, and urban development. Social and environmental sustainability is a major strategic component of smart cities, and smart city projects have illustrated a strong focus on achieving social inclusion of various urban residents in public services ^[16]. Researchers and policy makers have given attention to equitable urban growth, the extent to which social classes benefit from a technological integration of their urban fabric, together with “soft infrastructure” that includes knowledge networks, voluntary sector, safe urban environments, and night entertainment economy ^[17]. Smart cities also give attention to the role of social and relational capital in urban development. Communities need to learn, adapt, and innovate ^[18] and be able to use technology to benefit from it, which refers to the absorptive capacity ^[8], a concept that has been applied to different economic relations at different levels of spatial arrangements in cities ^[19].

Current and future cities have the potential to generate vast amounts of real-time data due to complex physical infrastructure and social networks ^[20] that are supported by data-driven applications. Due to the accessibility of real-time data, individuals and communities are increasingly presented with opportunities to engage in a variety of issues and processes that concern their lives. People form the nucleus of the city, and such opportunities have the potential to promote digital literacy and digital culture among citizens. Digital literacy can be loosely defined as the ability and skill to find, evaluate, utilize, share, and create content using information technologies and the internet ^[21], and digital culture is promoted by incorporating online technology into citizens’ work and lives.

2. Open Data, Big Data, and Internet of Things (IoT): Technology That Facilitate Data Driven Applications

Technology, data, and fast connectivity are essential for a smart city to function. Smart technology has the capability to alter the nature, operations, and efficiencies of infrastructure and the capability to provide low-cost solutions for collecting information in relation to usage patterns. With unparalleled volumes of data, local authorities and service providers can find new ways of optimizing existing services. As cities become smarter, the reliance on computer networks and systems increases, and the need to combine this technology with human-centered, responsive solutions that improve the quality of life of citizens becomes crucial.

Cities today generate and act as vast repositories of information and real-time data. When collected and systematically organized, this data can be stored, shared, and applied to provide new ways of services and applications that can influence lifestyles. This capability of cities to collect data via sensors and other smart devices are resulting in large data bases that are difficult to manage and use ^[22]. Real-time data can be utilized to improve connectivity, information sharing, and performance, resulting in data driven cities and societies. There is a global movement to open up public data and make it more accessible to application innovation, to create novel mobile apps and services, and promote the transparency of governments ^[22].

Large amounts of publicly available data being continuously generated by many sources, including public and private, are defined as ‘Open Data’. This data is stored securely in protective databases or on electronic devices. The nature, variety, and depth of this data is growing as new and increasingly technological solutions are implemented to solve the problems of governments, businesses, and private citizens of smart cities. The potential benefits of data collection on such a scale are immense. Limiting the number of people who can access it limits the number of problems to which it can be applied and, in most cases, prevents access to the people best able to apply it ^[23]. Several challenges arise at the same time to uphold data security and privacy.

‘Big Data’ is defined as having volume (ecommerce, mobility, and social media that generate large amounts of data), velocity (generating new data at a rapid pace), and variety (data in many different formats: emails, documents, images, videos, etc.) ^[24]. Applications that use Big Data sources, incorporating real-time data into computations to direct the measurement process of an application system, are classified as Dynamic Data Driven Application Systems (DDDAS) ^[25]. DDDAS are vital to operate smart city concepts to combine many infrastructure systems that share portals and feed data into each other’s systems to achieve complex joined up performances and results ^[26].

The ‘Internet of Things’, or IoT, is a system of interrelated computing devices, mechanical and digital machines, objects, or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction ^[27]. IoT applications for smart cities include smart homes, smart parking lots, health care systems, water and weather monitoring systems, transportation and vehicular traffic, environmental pollution, and security surveillance systems. Some smart solutions can both respond to demand and involve the public in shaping it ^[28]. They can be inclusive and personalized to face local challenges and can play a major role in linking people with each other. IoT has the capability to provide long term sustainability solutions in environmental, social, and economic sectors. By installing networks at home, it is possible to manage energy consumption, monitor health situations of the elderly in their house reducing health costs, have surveillance of homes for safety and childcare, and social networking applications that can inform and engage communities at local events.

In an IoT environment, internet links diverse components and devices ^[29] according to their geographical positions and assess by applying analyzing systems ^[30]. Smart cities include sensor networks and the connection of intelligent appliances to the internet, which is essential to remotely monitor their treatment such as power usage monitoring to improve the electricity usage, lighting, and air conditioning management. To achieve this aim, sensors can be extended at various locations to gather and analyze data for utilization improvements ^[31]. Sensor services can be employed in ongoing projects regarding the monitoring of cyclists, vehicles, and parking lots. This data can feed into service applications that utilize an IoT substructure to simplify operations in air, noise pollution control, the movement of cars, as well as surveillance and supervision systems ^[30]. These concepts not only result in improved livability of cities, but also a more productive place for businesses to operate. There has been a remarkable growth of digital devices, such as sensors, smartphones, and smart appliances, that has complimented commercial objectives of the IoT, as it is possible to interconnect all devices and create communications between them through the Internet.

3. Data Driven Smart Applications Usage for City Functions

A city with robust communication networks can rapidly and safely transmit the data collected by smart devices and other sensors. Free Wi-Fi availability in a city is beneficial for visitors as well as residents. Cities are prioritizing faster mobile broadband speeds that are essential to support demanding data usage by citizens. Bandwidth refers to the maximum amount of data that is transferable in each period, and higher band-width applications provide faster connections ^[32]. Less band-width intensive smart city applications can benefit from low-power wide-area networks (LPWAN), which allow broad deployment of sensors with much lower operating costs ^[28]. Open Data platforms are another aspect of the data driven technology base and create large volumes of data that can become useful when built into smart applications. In most of these applications, data and connectivity are key in achieving efficient smooth operations. Effective networks and mobile coverage systems, including 4G and 5G, as well as LPWAN connectivity, are crucial in this process and long-term investment in resources are essential to achieve benefits.

In relation to city functions, Big Data and IoT improve operational cost efficiencies, help promote a data driven culture, provide new opportunities for innovation, create new competences and services, accelerate the placement of new services, launch new products and services, increase and provide new sources of revenues, and transform businesses according to future models of operations. Refurbishment and improvement of infrastructure and physical assets are an ideal time to retrofit data driven smart devices. Smart sensors for the detection of conditions and smart meters to accept smart payments are such examples. When city services such as lighting and security are improved, sensors can be introduced to procure installing and maintaining data driven applications. The essential difference between a smart sensor and a standard sensor is its intelligence capabilities ^[33]. The integrated microprocessor is used for digital processing, analog-to-digital or frequency-to-code conversions, calculations, and interfacing functions, which can facilitate self-diagnostics, self-identification, or decision-making functions ^[34]. Partnerships with research groups, local organizations, and other stakeholders will improve the local data ecosystem and can help cities manage the technical complexity as well as the funding and analysis needs of applications. These applications will collect citywide data including air quality, noise, weather, and traffic information. A clear data management strategy will provide opportunities to maximize data and collectively co-relate with similar data sets, often from other similar entities or sources. Rather than collecting silo independent data, it is beneficial to collaborate under overarching guidelines to gain cross-departmental insights. Data compatibility from one platform to another is a concern that cities have struggled with for years and can become worse when IoT and Big Data solutions generate masses of new information without a management plan. Developing clear standards for data collection, storage, and sharing can prevent this fragmentation and encourage collaborations and growth.

4. Challenges in Using Data-Driven Smart Applications

While there are many benefits to using data driven applications, stakeholders have to face several challenges in operating and managing data. Big Data involves complex technologies in obtaining, storing, and using data concerned with data protection, security, and privacy. The systems could be vulnerable to cross-site scripting (computer security vulnerability found in web applications), hacking, and data leakage. While some countries and sectors are advanced in Big Data and its management, a large majority do not have adequate capacity or knowledge about the technologies and analytical capabilities that are involved. Cities need to adopt serious measures to ensure the privacy and security of citizen’s data to avoid data breaches. Without this guarantee, citizens cannot trust the governance systems, and the collection of data and information can be challenging. All systems should be resistant against cyber-attacks, particularly the critical infrastructure and assets that are essential for the functioning of the city. These include heating, water supply, public health, transportation, security services, electricity generation, telecommunication, and financial services. Heterogeneity, reliability, storage and computational ability for large data sets, legal and social aspects that are combined with data usage, and Big Data transfers are the other challenges that are faced by smart city infrastructure networks.

Some of the key challenges are managing the large volume of data and dealing with the continuous data growth, as data needs to be retrieved, stored, and computed. Together with the rise in unstructured data, there has also been a rise in the number of data formats including social media data, audio, video, and smart device data, etc. To maximize the use of complex real-time data that are generated continuously, organizations need to be aware and ready with the necessary tools, capabilities, and insights. Assimilating different data sources and authenticating and securing Big Data are other functions that need to be fulfilled if businesses are to achieve the best results from this data.

Employing and retaining professionals who are skilled in data handling and analysis is another challenge the industry faces, as there is a shortage of skilled personnel with this expertise. This criterion has been cited by many industries pursuing to better utilize Big Data and develop more effective data analysis systems. Training personnel can be costly, and many are working on new areas such as machine learning and artificial intelligence to build insights, but this also takes well-trained staff or the outsourcing of skilled developers ^[35]. There can also be organizational resistance in adapting to a new kind of working and analysis, and in some cases, cultural challenges remain an impediment to successful business adoption ^[36]. A survey conducted in 2017, with 1000 senior business and technology decision-makers in USA, found out that 95% of the organizations that participated have undertaken significant investments in Big Data initiatives during the past five years, spanning from $100M to $1B ^[37].

Businesses are working with Big Data, using powerful analytics to drive decision-making, identify opportunities, and boost performance ^[36]. Data that are collected from a variety of sources can pose potential security problems for cities. There are measures and tools to guard data and analytic processes from attacks, theft, or other malicious activities that could harm or negatively affect them ^[38]. These data security measures must be introduced from an early stage to collect, store, and retrieve in order to deter any hacking of data that is related to city infrastructure. Cyber-attacks on data storage can cause harm to city service operations and could cause significant monetary consequences such as financial losses, legal actions, or penalties. While none of the Big Data security tools are new, their scalability and the ability to secure multiple types of data in different stages have improved. Encryption, user access control, intrusion detection and prevention, as well as physical security are key factors in this process and relate to the ‘cloud’. The ‘cloud’ refers to servers that are accessed over the internet and the software and databases that run on those servers ^[39].

References

UN. United Nations Department of Economic and Social Affairs, World Urbanization Prospects, 2014 Revision; United Nations: New York, NY, USA, 2014.
OECD. International Futures Programme, Strategic Transport Infrastructure Needs to 2030; OECD Publishing: Paris, France, 2011.
National Academies. Committee on Increasing National Resilience to Hazards and Disasters. In Disaster Resilience: A National Imperative; The National Academies Press: Washington, DC, USA, 2012.
Hunter, M.; Guensler, R.; Guin, A.; Saroj, A.; Roy, S. Smart Cities Atlanta—North Avenue. City of Atlanta Research Project (2019). Available online: http://realtime.ce.gatech.edu/RenewAtlanta-GeorgiaTech-Final-Report.pdf (accessed on 16 March 2022).
Desouza, K.C.; Hunter, M.; Jacob, B.; Yigitcanlar, T. Pathways to the making of prosperous smart cities: An exploratory study on the best practice. J. Urban Technol. 2020, 27, 3–32.
UCLG/CGLU. Smart Cities Study, UCLG Committee Digital and Knowledge-Based Cities; UCLG/CGLU: Bilbao, Spain, 2017.
Intelligent Community Forum. 2015. Available online: https://www.intelligentcommunity.org/ (accessed on 16 March 2022).
Caragliu, A.; Del Bo, C.; Nijkamp, P. Smart Cities in Europe. J. Urban Technol. 2011, 18, 65–82.
OECD–EUROSTAT. Oslo Manual; OECD Publishing: Paris, France, 2005.
Kitchin, R. Making sense of smart cities: Addressing present shortcomings. Camb. J. Reg. Econ. Soc. 2015, 8, 131–136.
Husár, M.; Ondrejička, V.; Varış, S.C. Smart Cities and the Idea of Smartness in Urban Development—A Critical Review. In IOP Conference Series: Materials Science and Engineering; IOP Publishing Ltd.: Bristol, UK, 2017; Volume 245.
Roller, L.H.; Waverman, L. Telecommunication Infrastructure and Economic Development: A Simultaneous Approach. Am. Econ. Rev. 2001, 91, 909–923.
Glaeser, E.L.; Berry, C.R. Why Are Smart Places Getting Smarter? Taubman Center for State and Local Government: Cambridge, MA, USA, 2006; PB-2006-2.
Berry, C.R.; Glaeser, E.L. The Divergence of Human Capital Levels across Cities. Pap. Reg. Sci. 2005, 84, 407–444.
Hollands, R.G. Will the Real Smart City Please Stand Up? Intelligent, Progressive, or Entrepreneurial? City 2008, 12, 303–320.
Southampton City Council, Southampton On-Line. 2006. Available online: http://www.southampton.gov.uk/thecouncil/you-and-council/smartcities/ (accessed on 7 October 2020).
Glaeser, E.L. A Review of Richard Florida’s The Rise of The Creative Class. Reg. Sci. Urban Econ. 2005, 35, 593–596.
Coe, A.; Paquet, G.; Roy, J. E-Governance and Smart Communities: A Social Learning Challenge. Soc. Sci. Comput. Rev. 2001, 19, 80–93.
Cohen, W.; Levinthal, D. Absorptive Capacity: A New Perspective on Learning and Innovation. Adm. Sci. Q. 1990, 35, 80–93.
Foth, M.; Choi, J.H.J.; Satchell, C. Urban Informatics. In Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work, Hangzhou, China, 19–23 March 2011; pp. 1–8.
BBC Learning English. Digital Literacy. 5 December 2017. Available online: https://www.bbc.co.uk/learningenglish/english/ (accessed on 15 September 2021).
Galang, A. ENGL 794: Transmedia, Smart Cities and Big Data. 2013. Available online: https://annegalang.wordpress.com/2013/10/29/smart-cities-and-big-data-installation/ (accessed on 2 January 2021).
Smith, L. Benefits of Open Data for Smart Cities, Smart City Solutions Successfully Tackling Urban Challenges and Problems. 2017. Available online: https://hub.beesmart.city/en/solutions/tag/open-data (accessed on 16 March 2022).
Kitchin, R. Big Data, new epistemologies and paradigm shifts. Big Data Soc. 2014, 1, 2053951714528481.
Darema, F. Dynamic Data Driven Applications Systems: A New Paradigm for Application Simulations and Measurements. In International Conference on Computational Science; Springer: Berlin/Heidelberg, Germany, 2004.
Fujimoto, R.M.; Celik, N.; Damgacioglu, H.; Hunter, M.; Jin, D.; Son, Y.; Xu, J. Dynamic Data Driven Application Systems for Smart Cities and Urban Infrastructures. In Proceedings of the 2016 Winter Simulation Conference, Arlington, VA, USA, 11–14 December 2016; ISBN 978-1-5090-4484-9.
Rouse, M. Internet of things (IoT); IOT Agenda: Newton, MA, USA, 2019.
Woetzel, J.; Remes, J.; Boland, B.; Lv, K.; Sinha, S.; Strube, G.; Means, J.; Law, J.; Cadena, A.; Von Der Tann, V. Smart Cities: Digital Solutions for a More Livable Future; McKinsey & Company: Boston, MA, USA, 2018.
Rathore, M.M.; Ahmad, A.; Paul, A.; Rho, S. Urban planning and building smart cities based on the Internet of Things using Big Dataanalytics. Comput. Netw. 2016, 101, 63–80.
Talari, S.; Shafie-Khah, M.; Siano, P.; Loia, V.; Tommasetti, A.; Catalão, J.P.S. A Review of Smart Cities Based on the Internet of Things Concept. Energies 2017, 10, 421.
Botta, A.; de Donato, W.; Persico, V.; Pescapé, A. Integration of Cloud computing and Internet of Things: A survey. Future Gener. Comput. Syst. 2016, 56, 684–700.
Domains. Blog Post—Difference Between Bandwidth and Data Transfer. 2020. Available online: https://www.discountdomainsuk.com/web-hosting/difference-between-bandwidth-and-data-transfer-957.html (accessed on 16 March 2022).
Spencer, B.F.; Ruiz-Sandoval, M.E.; Kurata, N. Smart sensing technology: Opportunities and challenges. Struct. Control Health Monit. 2004, 11, 349–368.
Kirianaki, N.V.; Yurish, S.Y.; Shpak, N.O.; Deynega, V.P. Data Acquisition and Signal Processing for Smart Sensors, 1st ed.; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2002; pp. 1–8.
Vaghela, Y. Four Common Big Data Challenges; Dataversity Digital LLC: Studio City, CA, USA, 2018.
Harvey, C. Big Data Challenges. 5 June 2017. Available online: https://www.datamation.com/big-data/big-data-challenges/ (accessed on 16 March 2022).
NVP. Big Data Executive Survey 2017; New Vantage Partners: New York, NY, USA, 2017.
RDA-Research Data Alliance. Big Data Security—Issues, Challenges, Tech & Concerns. 2007. Available online: https://www.rd-alliance.org/ (accessed on 3 January 2021).
Cloudfare. What is the Cloud? Cloud Definition. 2019. Available online: https://www.cloudflare.com/learning/cloud/what-is-the-cloud/ (accessed on 16 March 2022).

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Architecture And Design

Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Yamuna Kaluarachchi

View Times: 899

Update Date: 14 Apr 2022

Table of Contents

Video Upload Options

Confirm