The present study provides a reflection on the agent-based intelligence of urban spatial environments through the comparison of a formal quantitative approach, i.e., space syntax, and a qualitative experimentation based on the spatial cognition approach. Until recently, space syntax was adopted by urban planners and designers to support urban design and planning decisions, based on an analysis of the urban physical environment. Researchers in the cognitive science field have increased their attempts to address space syntax techniques to better understand the relationships of cognitive spatial agents with the spatial features of urban environments. In this context, the experimental approach focuses on the qualities of the environment as interacted, perceived and interpreted by cognitive agents and reflects on the role which it plays in affecting spatial decisions and route choices. The present paper aimed to explore the extent to which possible integration between the different approaches can provide insights on agent-based decisions in actions and behavioural processes in space for useful perspectives in urban analysis and planning. Findings suggest relevant correlations between the experimentation results and space syntax predictions when a correspondence of some aspects can be found. Conversely, interesting qualitative insights from the spatial cognition approach are pointed out to enrich the configurational analysis. The potential and constraints of each approach and the ways of combining these are presented. Evidence supports the suitability of the proposal outlined in the present paper within the framework of urban planning practice.

This paper explores the concept of evolutionary urban resilience by framing cities as complex, open, and adaptive Social-Ecological-Technological Systems (SETS), shaped by multi-scalar dynamics, systemic uncertainty, and interdependent crises. It challenges the reductionist view of resilience as a fixed capacity or linear sequence of risk management phases, and instead proposes a process-based paradigm rooted in learning, creativity, and the ability to navigate disequilibrium. The framework defines urban resilience as a continuous and iterative transformation process, supported by: (i) a combination of tangible and intangible qualities activated according to problem typology; (ii) cross-domain processes involving infrastructures, flows, governance, networks, and community dynamics; and (iii) the engagement of diverse agents in shared decision-making and coordinated action. These dimensions unfold across three incremental and interdependent scenarios—baseline, critical, and chronic crisis—forming a ladder of resilience that guides communities through escalating challenges. Special emphasis is placed on the role of Information and Communication Technologies (ICTs) as relational and adaptive tools enabling distributed intelligence and inclusive governance. The framework also outlines concrete operational and policy implications for cities aiming to build anticipatory and transformative resilience capacities. Applied to the case of Taranto, the approach offers insights into how structurally fragile communities facing conflicting adaptive trajectories can unlock transformative potential. Ultimately, the paper calls for a shift from government to governance, from control to co-creation, and from reactive adaptation to chaos generativity, recasting urban resilience as an evolving project of collective agency, systemic reconfiguration, and co-production of emergent urban futures.

Recent critical events brought attention to the increasing exposure of urban environments to both slow and rapid onset disasters, which arise from both anthropogenic and natural causes. These events have particularly severe effects on historic centres, which are characterized by high levels of vulnerability and valuable assets exposed to risk. To minimize the impact on tangible and intangible cultural heritage values, especially in outdoor public areas such as squares and streets, it is crucial to establish coherent mitigative and adaptive solutions for different types of hazards. This research presents a methodology aimed at defining levels of multi-vulnerabilities in historic districts in the Apulia Region (Italy), considering the recurrent hazards to which the latter is prone. It uses a multi-step process based on structured and non-structured methodologies and tools for single risks, examined in combination, to determine the main properties characterizing the vulnerability assessment. The dataset was analyzed in a GIS environment to evaluate the selected Apulian case study (Molfetta) in Multi-Asynchronous Hazard scenarios, showing the compounded levels of criticalities for open areas and streets. This information is intended to support authority and emergency managers in identifying priority interventions and increasing the resilience of the outdoor public places.

In a modern pandemic outbreak, where collective threats require global strategies and local operational defence applications, data-driven solutions for infection tracing and forecasting epidemic trends are crucial to achieve sustainable and socially resilient cities. Indeed, the need for monitoring, containing, and mitigating the ongoing COVID-19 pandemic has generated a great deal of interest in Digital Proximity Tracing Technology (DPTT) on smartphones, as well as their function and effectiveness and insights of population acceptance. This paper introduces and compares different Data-Driven Epidemic Intelligence Strategies (DDEIS) developed on DPTTs. It aims to clarify to what extent DDEIS could be effective and both technologically and socially suitable in reaching the objective of a swift return to normality for cities, guaranteeing public health safety and minimizing the risk of epidemic resurgence. It assesses key advantages and limits in supporting both individual decision-making and policy-making, considering the role of human behaviour. Specifically, an online survey carried out in Italy revealed user preferences for DPTTs and provided preliminary data for an SEIR (Susceptible–Exposed–Infectious–Recovered) epidemiological model. This was developed to evaluate the impact of DDEIS on COVID-19 spread dynamics, and results are presented together with an evaluation of potential drawbacks.