Abstract

CIVITAS is a search engine for urban conditions, developed to allow stakeholders to identify qualities of liveability and urban experiences that suit their tacit desires and explicit requirements. While using CIVITAS to study three global cities for bespoke end users in 2015–16, the authors interpreted the metric of “accessibility to amenities” to suggest that, while the global profile of cities varied, the local neighbourhoods preferred by certain end users turned out to be very similar. Further studies were initiated across more cities and neighbourhoods, with more diverse metrics in order to validate the initial suspicion. Metrics pertaining to urban structure and demographics were added to “amenity provision,” and two types of comparative profiles were produced for insights. The findings are not as unambiguous as the initial data suggested for the initially targeted category, but another pattern emerged that supports assumptions in planning guidance for “liveable” cities, and relates urban structure to density.

Introduction

In 2008, the authors developed a proof-of concept model to simulate sustainable urban densification. The two cities of Dubai and London were used as cases to demonstrate the difference of densification when a new tall building is inserted into the urban fabric. Two dependencies formed the basis for the simulation: land-use provision for commercial buildings and accessibility to the predominant transport mode. Dubai was then primarily using a vehicular transport system, while London primarily then relied on the underground transport system for commuting. The model would then generate the amount of area required to accommodate additional land uses that would support a new tall building with a set floor area. The multi-layered feedback model clearly illustrated the difference in levels of sprawl and densities seen in cities with either a (dense) public underground transport system like London or a car-dependent transport system like Dubai (see Figure 1).

 

 

Since that time, open-source urban data has become widely available. From 2014 onwards, the discourse about socio-spatial sustainability of cities has shifted from its design to its assessment, quantifying conditions and scrutinizing governance through the analysis of big urban data. Indicative of this transition are the growing numbers of online city indices that attempt to rank global cities according to “liveability”, “governance” or “economic opportunity,” based on an ever-increasing mix of metrics. However, no notable new urban design guidelines have been established since then. Such indices of “liveability” include the Mercer’s Quality of Living Cities Index, The Economist’s Global Liveability Ranking, and Monocle’s Quality of Life Survey. For “economic opportunity” there are annual reports, such as PwC’s Cities of Opportunity, Knight Frank’s Prime Global Cities Index, Savills’ Tech Cities, JLL’s City Momentum Index, the Global Innovation Index (GII) and ATKearney’s Global Cities, to name but a few. Reports by UN Habitat, such as the Urban Patterns for a Green Economy series, have become nearly the single source that attempts to balance economic performance with livability and to deduce design objectives for sustainable cities, such as A New

Strategy of Sustainable Neighbourhood Planning: Five Principles (UN Habitat 2014).

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