We present a methodology and a planning and design support software tool for evaluating walkability and pedestrian accessibility of places which are relevant for people’s capabilities, and thus an important component of quality of life in cities. A multicriteria evaluation model, at the core of the decision support system, is used to assign walkability scores to points in urban space. Walkability scores are obtained through algorithms which process spatial data and run the evaluation model in order to derive potential pedestrian routes along the street network, taking into account the quality of urban space on several attributes relevant for walkability. One of its notable characteristics is a certain reversal of perspective in evaluating walkability: the walkability score of a place does not reflect how that place is per se walkable, but instead how and where to can one walk from there, that is to say, what is the walkability the place is endowed with. This evaluation incorporates three intertwined elements: the number of destinations/opportunities reachable by foot, their walking distances, and the quality of the paths to these destinations. In this article, we furthermore demonstrate possible uses of the support system by reporting and discussing the results of a case-study assessment of a project for the Lisbon’s Segunda Circular (Second Ring Road). The software tool is made freely available for download

We argue that antifragility is a valuable and contentful goal for planning. We present a possible definition and outline the tenets and essential properties of an antifragile planning and compare it with approaches of urban resilience. We further present an argument for the legitimacy of an antifragile planning, by exploring its possible conceptualisation in terms of the capability approach. Hence the recommendation to incorporate antifragility into planning practice and content.

Improving urban quality of life is often stated as the main goal of urban policies, planning and management. However, there is no wide consensus on the theoretical and methodological framework that should be used to operatively define the concept of urban quality of life, so as to be useful for developing operational tools to measure it and for the evaluation of urban projects, plans and policies. We consider the capability approach an effective candidate for providing the kind of theoretical and methodological grounding necessary for the design of such tools. According to this theoretical perspective, individual wellbeing is not defined in terms of endowment of commodities, but rather in relation to a person’s capability ‘to function’. This means we must look at what a person actually is and does (functionings) and what they are effectively able to be and do (capabilities), given both their personal characteristics and their surrounding environment. We can therefore say that in the capability approach, the achievement of wellbeing is a process of interaction between the individual and their surrounding environment. Putting these ideas consistently to work in the design of tools for measuring urban quality of life means to evaluate urban quality of life on the basis of the actual possibilities each person has to ‘use’ the city in order to achieve functionings and capabilities, rather than just observing urban features

A significant class of decision making problems consists of choosing actions, to be carried out simultaneously, in order to achieve a trade-off between different objectives. When such decisions concern complex systems, decision support tools including formal methods of reasoning and probabilistic models are of noteworthy helpfulness. These models are often built through learning procedures, based on an available knowledge base. Nevertheless, in many fields of application (e.g. when dealing with complex political, economic and social systems), it is frequently not possible to determine the model automatically, and this must then largely be derived from the opinions and value judgements expressed by domain experts. The BayMODE decision support tool (Bayesian Multi Objective Decision Environment), which we describe in this paper, operates precisely in such contexts. The principal component of the program is a multi-objective Decision Network, where actions are executed simultaneously. If the noisy-OR assumptions are applicable, such a the model has a reasonably small number of parameters, even when actions are represented as non-binary variables. This makes the model building procedure accessible and easy. Moreover, BayMODE operates with a multi-objective approach, which provides the decision maker with a set of non-dominated solutions, computed using a multi-objective genetic algorithm. © Springer Science+Business Media, LLC 2007

No abstract