Smart Cities and Big Data Analytics: A Data-Driven Decision-Making Use Case

Mohammad Khalid Imam Rahmani Shahid Husain Mohammed Arshad Khan + 1 lainnya

2022

In the data-driven world, data is created in huge volume and then analyzed by several organizations to get benefit from them. Smart city is one of the examples to use big data to offer improved services for its resident and tourist. However, some countries face certain obstacles to analyze the big data integration for sustainability in smart city development. Therefore, the purpose of this research paper is to identify and analyze the significant barriers related to sustainable smart city development. To accomplish this objective, fourteen barriers of big data analytics are selected through the combined approach of literature review and expert input. After that, these barriers are evaluated using the best worst method for obtaining deeper insights. The result of this study reveals that the most significant barrier is ‘lack of technologies for BDA’, ‘lack of BDA framework’, ‘nature of big data’, and ‘low availability of analytics platforms for big data’. These barriers need to address on priority to develop a sustainable smart city. This study is helpful to the urban planner, government, and consultancy agencies to decide on the adoption of BDA for sustainable smart city development. Further, they can also optimize their resources in the best possible manner to achieve the sustainable development of the existing smart cities.

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.

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.

Anestis Kousis Christos Tjortjis

2021

Smart cities connect people and places using innovative technologies such as Data Mining (DM), Machine Learning (ML), big data, and the Internet of Things (IoT). This paper presents a bibliometric analysis to provide a comprehensive overview of studies associated with DM technologies used in smart cities applications. The study aims to identify the main DM techniques used in the context of smart cities and how the research field of DM for smart cities evolves over time. We adopted both qualitative and quantitative methods to explore the topic. We used the Scopus database to find relative articles published in scientific journals. This study covers 197 articles published over the period from 2013 to 2021. For the bibliometric analysis, we used the Biliometrix library, developed in R. Our findings show that there is a wide range of DM technologies used in every layer of a smart city project. Several ML algorithms, supervised or unsupervised, are adopted for operating the instrumentation, middleware, and application layer. The bibliometric analysis shows that DM for smart cities is a fast-growing scientific field. Scientists from all over the world show a great interest in researching and collaborating on this interdisciplinary scientific field.

S. Hashim Mohsen Marjani A. Sali + 3 lainnya

2021

The notion of smart cities has remained under evolution as its global implementations are challenged by numerous technological, economic, and governmental obstacles. Moreover, the synergy of the Internet of Things (IoT) and big data technologies could result in promising horizons in terms of smart city development which has not been explored yet. Thus, the current research aims to address the essence of smart cities. To this end, first, the concept of smart cities is briefly overviewed; then, their properties and specifications as well as generic architecture, compositions, and real-world implementations are addressed. Furthermore, possible challenges and opportunities in the field of smart cities are described. Numerous issues and challenges such as analytics and using big data in smart cities introduced in this study offers an enhancement in developing applications of the above-mentioned technologies. Hence, this study paves the way for future research on the issues and challenges of big data applications in smart cities.