Abstract
Abstract. Nowadays, cities have to withstand multiple challenges such as climate change, increasing urban population, economic cycles, health crises, traffic congestion, rising levels of energy consumption and citizens' expectations. Recently, the smart city has been proposed as a model to face such challenges. Underneath its digital skin, which provides ubiquitous high-tech solutions, the smart city is often presented in continuity with the traditional city, while getting inspired by the modernist or international paradigm (free standing towers, high density development and functional zoning). Is this actually the case? To understand the features of urban form of new smart developments and how these relate to existing approaches in city planning, we propose a quantitative methodology to compare features of multiple city areas, which is based on the computation of a set of 3D morphometrics of the urban environment, and descriptive statistics for each metric. We applied this methodology to compare Nice Meridia, a French example of smart city development, with a traditional neighbourhood, located in the same city. Outcomes show that the smart city neighbourhood shares some morphological aspects with the traditional city, but differs significantly in other respects, showing the interest of quantitative 3D metrics.
Highlights
These rules are written in Computer Generated Architecture (CGA) language, which is a formal programming language that allows to build any 3D object in the study area and calculate 3D Key Performance Indicators (KPI)
We show the masterplan by Devillers of Nice Meridia neighborhood for 2030, built by the authors as 3D parametric model at floor level in the ESRI platform CityEngine
To understand the features of urban form of new smart developments and how these relate to existing approaches in city planning, we propose a quantitative methodology to compare features of multiple city areas, which is based on computation of a set of 3D morphometrics of the urban environment and descriptive statistics for each metric
Summary
1.1 Smart CityThe “Smart City” concept received an increasing attention in urban planning and governance in the last two decades: it is a response to recent urban challenges, such as the rapid expansion of urban population, traffic congestion, global energy consumption and greenhouse gas emissions, economic competitiveness and rising expectations of citizens (Washburn and Sindhu, 2010).In geography and urban planning, the term “smart” refers more to the concept of intelligent growth (Batty et al, 2012), which implies greater urban efficiency, thanks to the coordination between conservation and economic development, optimizing resources and improving the life quality of citizens.At the same time, we are talking about a city that exploits new opportunities such as technological advances in information and communication field (Lee et al, 2013). Certain experts have doubts about Smart City initiatives, presenting them as celebrating labels for cities (Hollands, 2008), or artefacts of the information and communication industry (Anthopoulos, 2017). In today planning debate, is an accurate reflection on the design model underneath this digital skin (Rabari and Storper, 2014) of smart cities. They are often presented in continuity with the traditional city, while getting inspired by the modernist or international paradigm (free standing towers and functional zoning) (Venerandi et al, 2020). The present paper intends to investigate the forms of the smart city further, through set of 3D morphometrics
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