Data Mining Algorithms for Smart Cities: A Bibliometric Analysis

A. Salih S. F. Kak Wafaa M. Abdullah + 4 lainnya

29 April 2021

Many policymakers envisage using a community model and Big Data technology to achieve the sustainability demanded by intelligent city components and raise living standards. Smart cities use different technology to make their residents more successful in their health, housing, electricity, learning, and water supplies. This involves reducing prices and the utilization of resources and communicating more effectively and creatively for our employees. Extensive data analysis is a comparatively modern technology that is capable of expanding intelligent urban facilities. Digital extraction has resulted in the processing of large volumes of data that can be used in several valuable areas since digitalization is an essential part of daily life. In many businesses and utility domains, including the intelligent urban domain, successful exploitation and multiple data use is critical. This paper examines how big data can be used for more innovative societies. It explores the possibilities, challenges, and benefits of applying big data systems in intelligent cities and compares and contrasts different intelligent cities and big data ideas. It also seeks to define criteria for the creation of big data applications for innovative city services.

Z. Allam B. Feizizadeh Ayyoob Sharifi + 1 lainnya

25 Juni 2021

The concept of smart cities has gained significant momentum in science and policy circles over the past decade. This study aims to provide an overview of the structure and trends in the literature on smart cities. Bibliometric analysis and science mapping techniques using VOSviewer and CiteSpace are used to identify the thematic focus of over 5000 articles indexed in the Web of Science since 1991. In addition to providing insights into the thematic evolution of the field, the three-decade study period is divided into two sub-periods (1991–2015 and 2016–2021). While splitting the dataset into more sub-periods would have been desirable, we decided to only examine two sub-periods as only very few papers have been published until 2010. The annual number of publications has progressively increased since then, with a surge in the annual number of publications observable from 2015 onwards. The thematic analysis showed that the intellectual base of the field has been very limited during the first period, but has expanded significantly since 2015. Over time, some thematic evolutions, such as further attention to linkages to climate change and resilience, and more emphasis on security and privacy issues, have been made. The thematic analysis shows that existing research on smart cities is dominated by either conceptual issues or underlying technical aspects. It is, therefore, essential to do more research on the implementation of smart cities and actual and/or potential contributions of smart cities to solving societal issues. In addition to elaborating on thematic focus, the study also highlights major authors, journals, references, countries, and institutions that have contributed to the development of the smart cities literature.

Shamneesh Sharma In-Ho Ra Isha Batra + 3 lainnya

2022

Smart cities are a current worldwide topic requiring much scientific investigation. This research instigates the necessity of an organized review to a heedful insight of the research trends and patterns prevailing in this domain. The string is formulated to extract the corpus from Scopus largest database of publications. The corpus of 8320 articles published from 2010 to 2022 is processed using Latent Dirichlet Allocation. Two, five, and ten topics have been extracted to provide the recent trends for IoT in smart cities. There has been an increased recognition that more attention needs to be paid to the area of smart cities so a complete overview of the topic of smart cities research, including the most prominent nations (institutions, sources, and authors) and noteworthy research directions has been presented in this paper. The scientific collaboration across countries (regions), organizations, and authors has also been widely discussed. A detailed and comprehensive overview and visualization of the trends and research patterns used to integrate the Internet of Things in Smart Cities. This data based experimental study signifies a roadmap of the research trends in Smart Cities by implementing topic modeling technique that has never been used in this domain. Based upon the topic modeling using LDA, authors have formulated three research questions and answered those question based on the in-depth research. At the end this study concludes the areas suggested are at the growing phase and need more insight for their growth.

Makó Csaba Amir H. Mosavi Mehdi Sookhak + 7 lainnya

2022

The manuscript represents a comeprehensive and systematic literature review on the machine learning methods in the emerging applications of smart city. Application domains include the essential aspect of the smart cities including the energy, healthcare, transportation, security, and pollution. The methodology presents the state-of-the-art, taxonomy, evaluation and model performance. The study concludes that the hybrid models and ensembles are the best performers since they exhibit both high accuracy and not-costly complexity. On the other hand, the deep learning (DL) techniques had higher accuracy than the hybrid models and ensembles, but they demanded relatively higher computation power. Moreover, all these advanced ML methods had a slower processing speed than the single methods. Likewise, the support vector machine (SVM) and decision tree (DT) generally outperformed the artificial neural network (ANN) for accuracy and other metrics. However, since the difference is negligible, it can be concluded that using either of them is appropriate. The study’s findings identify the pros and cons of the methods in each application for future researchers, practitioners, and policy-makers for the right problem within the context of smart cities.

Eugenio Cesario

12 Mei 2023

Urban environments continuously generate larger and larger volumes of data, whose analysis can provide descriptive and predictive models as valuable support to inspire and develop data-driven Smart City applications. To this aim, Big data analysis and machine learning algorithms can play a fundamental role to bring improvements in city policies and urban issues. This paper introduces how Big Data analysis can be exploited to design and develop data-driven smart city services, and provides an overview on the most important Smart City applications, grouped in several categories. Then, it presents three real-case studies showing how data analysis methodologies can provide innovative solutions to deal with smart city issues. The first one is an approach for spatio-temporal crime forecasting (tested on Chicago crime data), the second one is methodology to discover mobility hotsposts and trajectory patterns from GPS data (tested on Beijing taxi traces), the third one is an approach to discover predictive epidemic patterns from mobility and infection data (tested on real COVID-19 data). The presented real-world cases prove that data analytics models can effectively support city managers in tackling smart city challenges and improving urban applications.