ABSTRACT

Cities worldwide are attempting to transform themselves into smart cities. Recent cases and studies show that a key factor in this transformation is the use of urban big data from stakeholders and physical objects in cities. However, the knowledge and framework for data use for smart cities remain relatively unknown. This paper reports findings from an analysis of various use cases of big data in cities worldwide and the authors’ four projects with government organizations toward developing smart cities. Specifically, this paper classifies the urban data use cases into four reference models and identifies six challenges in transforming data into information for smart cities. Furthermore, building upon the relevant literature, this paper proposes five considerations for addressing the challenges in implementing the reference models in real-world applications. The reference models, challenges, and considerations collectively form a framework for data use for smart cities. This paper will contribute to urban planning and policy development in the modern data-rich economy.

Introduction

A smart city is composed of and monitored by pervasive ICT (Neirotti, De Marco, Cagliano, Mangano, & Scorrano, 2014). “In the last two decades, the concept of smart city has become more and more popular in scientific literature and international policies” (Albino, Berardi, & Dangelico, 2015). This popularity could be attributed to the traction that the smart city concept has gained as a vision for improving the economy, mobility, environment, people, living standards, and governance of cities (Abella, Ortiz-de-Urbina-Criado, & De-PablosHeredero, 2017; Angelidou, 2015; Caragliu, Del Bo, & Nijkamp, 2011; Vanolo, 2014). IBM accomplished > 100 smart city projects worldwide in 2010–۲۰۱۷, projects whose themes included administration, citizen engagement, economic development, education and workforce, the environment, public safety, social services, transportation, and urban planning (IBM Smarter Cities Challenge, 2017). The recent proliferation of big data has contributed to smart city transformation (Barns, 2016; Bibri, 2018b; Hashem et al., 2016; Kitchin, 2014; Rabari & Storper, 2015). “Big data” generally refers to large and complex sets of data that represent digital traces of human activities and may be defined in terms of scale or volume, analysis methods (Chen, Chiang, & Storey, 2012), or effect on organizations (McAfee & Brynjolfsson, 2012). Cities around the world collect massive quantities of data related to urban living from objects (e.g., energy infrastructure) and stakeholders (e.g., energy-using residents). Use of these data contributes to the creation of useful content for various stakeholders, including citizens, visitors, local government, and companies. For instance, the Seoul government collects data related to public health, transportation, and residence and has made them available for data scientists to produce meaningful knowledge for the city and its citizens. As a result, the Seoul government identified the patterns and demands of the usage of the city bus at midnight and subsequently improved midnight public bus services (NIA, 2013). Similarly, the San Francisco government analyzed crime records to improve public security services (Lee, 2013), and the Rio de Janeiro government used data from cameras and sensors to address various concerns of the city such as weather, energy, and safety (Kitchin, 2014). Other cases include Santander in Spain (Díaz-Díaz, Muñoz, & Pérez-González, 2017) and Cosenza in Italy (Cicirelli, Guerrieri, Spezzano, & Vinci, 2017).