Statistical modelling of spatial consensus through RT-GSCS: A web-based open platform for decision-making and foresight contexts

The spatial decision-making process in urban planning, policy development, and territorial program implementation is fundamentally connected with the concept of location within a geographical context. Given the internal spatial complexity arising from numerous interconnected variables such as natural resources, infrastructure, environmental factors, cultural elements, and security challenges, accurately identifying their spatial positions is crucial. Assuming such complexity of geographical spaces, the spatial decision-making process should adopt an interdisciplinary approach. This approach should consider the opinions of different social actors to achieve a common consensus in the final decision. This dissertation presents applied research aimed at developing a new Spatial Decision Support System to enhance the decision-making process. This system facilitates the acquisition, analysis, and visualisation of expert spatial input to determine optimal locations for goods, services, and events in a territory. The proposed system is based on a spatial modification of the Delphi method, a well-established and robust technique in the literature that aggregates expert judgments to address specific problems, aiming to achieve consensus and stability. Considering the specific technical framework, computational algorithms, and advanced spatial analysis, a web-based open system called the “Real-Time Geo-Spatial Consensus System” (RT-GSCS) is developed. The system enables the administration of a spatial questionnaire in real-time, allowing experts to answer a series of questions by entering spatial information on a dynamic map. They can also consider real-time spatial and textual analyses, along with convergence metrics. The final aim is to achieve a spatial consensus, thereby facilitating the optimal location of goods, services, and events within the territory. To validate the proposed system, we present three major case studies in different geographical areas. The first case study involves developing future spatial scenarios in the climate context for ten coastal European cities. The second case study focuses on the transport sector, specifically identifying logistics facilities such as parcel lockers and loading bays in the city of Dublin. The third case study aims to identify suitable locations for active mobility pathways in Dublin. The primary findings demonstrate the system’s effectiveness in real-time acquisition of spatial judgments from multidisciplinary groups of experts, even those without prior experience with geo-technology tools. This approach supports collective spatial analysis for making spatial decisions, particularly in the realms of planning, organisation, and resource utilisation within a territory.