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 present a survey of operational methods for walkability analysis and evaluation, which we hold show promise as decision-support tools for sustainability-oriented planning and urban design. An initial overview of the literature revealed a subdivision of walkability studies into three main lines of research: transport and land use, urban health, and livable cities. A further selection of articles from the Scopus and Web of Science databases focused on scientific papers that deal with walkability evaluation methods and their suitability as planning and decision-support tools. This led to the definition of a taxonomy to systematize and compare the methods with regard to factors of walkability, scale of analysis, attention on profiling, aggregation methods, spatialization and sources of data used for calibration and validation. The proposed systematization aspires to offer to non-specialist but competent urban analysts a guide and an orienteering, to help them integrate walkability analysis and evaluation into their research and practice.

We construct a method to evaluate the neighbourhood park vitality potential (NPV-potential), inspired by Jane Jacobs’s theory of urban and park vitality. The evaluation model produces an aggregate score of NPV-potential by combining information on the extrinsic factors of vitality, related to the park’s surrounding urban area, with evaluative judgements on the intrinsic factors, related to the park’s internal organisation and design. To showcase and submit the evaluation model to a preliminary test drive, we further present the results of an application on three parks in the city of Cagliari, Italy. The computed NPV-potential and the effective use of the three parks, obtained from direct observation, show a good degree of agreement. While far from a robust validation, which would require more extensive empirical studies with larger and more internally variable samples of parks, the reported agreement between the potential and the observed vitality on the ground is a preliminary indication of the possible usefulness of the proposed evaluation method for urban planning and design.

The objective of this computational study was to investigate to which extent the availability and the way of use of historical maps may affect the quality of the calibration process of cellular automata (CA) urban models. The numerical experiments are based on a constrained CA applied to a case study. Since the model depends on a large number of parameters, we optimize the CA using cooperative coevolutionary particle swarms, which is an approach known for its ability to operate effectively in search spaces with a high number of dimensions. To cope with the relevant computational cost related to the high number of CA simulations required by our study, we use a parallelized CA model that takes advantage of the computing power of graphics processing units. The study has shown that the accuracy of simulations can be significantly influenced by both the number and position in time of the historical maps involved in the calibration