Guest editorial: Big data-driven analytics for smart cities: technology-based insight
| DOI | https://doi.org/10.1108/IMDS-10-2022-813 |
| Published date | 02 November 2022 |
| Date | 02 November 2022 |
| Pages | 2145-2150 |
| Subject Matter | Information & knowledge management,Information systems,Data management systems,Knowledge management,Knowledge sharing,Management science & operations,Supply chain management,Supply chain information systems,Logistics,Quality management/systems |
| Author | Yue Guo,Lean Yu,Yichuan Ding,Leandro C. Coelho |
Guest editorial: Big data-driven
analytics for smart cities:
technology-based insight
1. Introduction
The smart city is an overarching framework for thinking about the future in which ecological,
social and economic factors are all taken into account in the quest to better human life
(Khansari et al., 2014;Chu et al., 2021). Smart cities use heterogeneous network infrastructure,
pervasive sensing devices, big data processing and smart control systems (Zhang et al., 2017).
Prior studies have examined the operation and management of a smart city from various
perspectives, such as charging management for electric vehicles (Shuai et al., 2016), car
sharing and air pollution (Barnes et al., 2020) and healthcare management (Xu et al., 2018).
However, most of them mainly rely on well-established research methodologies, such as
surveys, descriptive analysis and linear regression models. Solving smart city issues is
undoubtedly costly and complicated and requires us to develop novel research approaches
based on empirical data to provide more appropriate solutions to build smart city plans.
In recent years, the expansion of digital infrastructures such as the Internet of Things
(IoT) and information and communication technologies (ICT) has enabled the rapid
proliferation of city-level big data (Batty, 2013). With the assistance of big data analytics and
operations research, our study methodologies and insights can be considerably enhanced by
utilizing such spontaneous, objective and vast data. Therefore, there is broad interest in
academics and practice in learning how to apply big data analytics and optimization for the
operation and management of a smart city from a variety of viewpoints, including urban
planning and layout (Wang et al., 2017,2018), smart energy management (Barbry et al., 2019),
tourism (Guo et al., 2014) and healthcare services (Ding et al., 2019).
How do novel approaches powered by big data analytics contribute to developing a
smart city with high efficiency? Motivated by this concern and aware of the potential
contribution of big data management and application research to the development of smart
cities, we dedicate a special issue of Industrial Data and Management Systems to data-
driven analytics applied to smart city-related issues. This special issue aims to publish the
insights and perspectives of academics regarding solutions to smart city issues and
challenges from various perspectives, such as the resource allocation and optimization of
power stations, t he location and cap acity for shared c ar parking space, transportation
strategy and operation management of car sharing. Twelve papers in this special issue
tackle the subject of data-driven analytics developme nts for smart citie s from various
angles. These studies can be categorized into four primary categories: the enhancement of
key performance p rediction models , urban planning an d layout optimiz ation, smart
hospitality and tourism and demand management. In these papers, recent advancements in
data-driven prediction and optimization analytics research focusing on various aspects of a
smart city are discussed, such as battery storage management, hospital performance
prediction, economic development in a smart city, urban rain gauge network and spatial
Guest editorial
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Generous financial support was provided by the National Natural Science Foundation of China [grant
numbers 71872061] and the Key Program of NSFC-FRQSC Joint Project (NSFC No. 72061127002, FRQSC
No. 295837). This work is partially supported by Shenzhen Humanities & Social Sciences Key Research
Bases.
Industrial Management & Data
Systems
Vol. 122 No. 10, 2022
pp. 2145-2150
© Emerald Publishing Limited
0263-5577
DOI 10.1108/IMDS-10-2022-813
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