مقاله انگلیسی رایگان در مورد طراحی سیستم های پشتیبانی برای شهرهای هوشمند – الزویر 2018

The integration of Information Communication and Technology (ICT) into cities over the past two decades has generated interest from urban analysts and theorists alike (Kitchin, 2014a, p. 1). Harrison and Donnelly (2011) list examples of the many potential benefits that can arise, such as: lower resource consumption, improving infrastructure capacity, and coordination of peak demands on energy, water and transportation to improve city resiliency. However, the concept of smarter city planning as enabled through big data, city analytics and modelling is one potential benefit which has not been given sufficient consideration. This paper endeavours to address this gap through reviewing recent case studies in the application of planning support systems in the context of Australia. When considering smarter cities planning one must first provide a suitable definition of smart cities. There are many and varied definitions. Kitchin (2014a) defines smart cities as those that address technology, economy, and governance – comprised of ubiquitous computing and driven by innovation. Other definitions focus on the various scales addressed by smart cities, such as that from Batty et al. (2012), referring to smart cities as both automated “routine functions serving individual persons, buildings, traffic systems” as well as “ways that enable us to monitor, understand, analyse and plan the city to improve the efficiency, equity and quality of life for its citizens in real time” (p.482). This paper focuses on the latter portion of Batty’s definition as we explore how the smart city movement has created renewed opportunity and interest in data-driven urban modelling to support land-use planning. Increased automation in the built environment gives rise to big data, which is creating new potential for pattern recognition within cities (Batty, 2015). The rapid growth of big data and its range of uses means it is difficult to define (Kitchin & McArdle, 2016), but recent academic dialogue has set it apart from other data in three areas, the ‘3 V’s’: volume, velocity, and variety (Laney, 2001). The components of the multi-V model often change depending on the report (Assunção et al., 2013); other authors have defined ‘5 V’s’ (Batty, 2016; Assunção et al., 2013) or ‘7 V’s’ (Khan, Uddin, & Gupta, 2014; McNulty, 2014), encompassing the following: • Volume – depth and breadth of data (Laney, 2001) • Velocity – speed of transmission (Miller, 2015) • Variety – type and kind of data (Batty, 2016) • Variability – degree of inconsistency in data representation (Batty, 2016) • Veracity – reliability or truthfulness of the data (Assunção et al., 2013; Khan et al., 2014) • Validity – accuracy of the data for its intended use or application (Khan et al., 2014) • Volatility – the relationship of the retention period for data sets with their associated storage and security needs (Khan et al., 2014) • Visualisation – presentation of the data (McNulty, 2014) • Value – the worth and insights derived from in-depth analysis of the data (Assunção et al., 2013; McNulty, 2014) This paper focuses on the interpretation of big data for urban planners, primarily the variety of data used to assess planning scenarios, its communication to stakeholders through visualisation methods, and the added value from its addition into traditional urban modelling approaches (Thakuriah, Dirks, & Keita, 2017, pp. 189–208). The variety of big data we investigate are those defined by Batty (2016): data produced from real-time sensors, spatial data sensed from satellites, and data based on population and economic forecasts. The potential to harness big data within the smart city movement creates opportunity for planners to better guide the expected 2.5 billion-person growth in the global urban population between 2014 and 2050 (United Nations, 2014). Planners can now combine population and economic forecasts with temporally and spatially sensed data through digital planning tools. Planning Support Science, a field that continuously develops and improves frameworks for big data sets, has emerged with the increased research and development of digital planning tools (Geertman, Allan, Pettit, & Stillwell, 2017). These tools, called planning support systems (PSS), use the growing presence of big data to help inform more sustainable, productive and resilient city scenarios through data mining, analysis, modelling and visualisation.