Italian historic centres face critical challenges balancing heritage conservation with contemporary needs, particularly in regions like Calabria where smaller settlements experience depopulation, decay, and inadequate services. Traditional smart city frameworks have inadequately addressed these contexts, focusing primarily on contemporary urban environments with adaptable infrastructure. This study examines how GeoAI-enabled urban analysis and participatory design methodologies can enhance urban well-being while preserving cultural heritage in small and medium-sized historic centres. The research develops a replicable methodological framework combining advanced technologies (AI, big data, wearable devices) with Living Lab participatory processes. The approach operationalizes "urban well-being" through three measurable dimensions: physical comfort (route optimization based on weather, terrain, facilities), cultural access (personalized itineraries considering tourist density), and perceived safety (recommendations using social media sentiment, lighting data, population density). Data governance follows GDPR protocols ensuring privacy protection and algorithmic transparency through Explainable AI (XAI). Pilot sites in Calabria represent diverse typologies: peripheral centres, high-tourism destinations, isolated villages, coastal settlements, and centres near natural parks. Expected impacts span individual (enhanced comfort, safety), community (social cohesion, participation), territorial (sustainable tourism, economic vitality), and governance (data-driven resource allocation) levels. The study demonstrates that technology, integrated within strategic vision and participatory practices, can support heritage-respectful urban regeneration oriented toward collective well-being.

CITISENSE. Enhancing urban well-being through smart design, data and AI in Italy's historic centres / Margherita Tufarelli. - In: TEMA. - ISSN 1970-9870. - ELETTRONICO. - 19:(2026), pp. 151-170. [10.6093/1970-9870/11551]

CITISENSE. Enhancing urban well-being through smart design, data and AI in Italy's historic centres

Margherita Tufarelli
2026

Abstract

Italian historic centres face critical challenges balancing heritage conservation with contemporary needs, particularly in regions like Calabria where smaller settlements experience depopulation, decay, and inadequate services. Traditional smart city frameworks have inadequately addressed these contexts, focusing primarily on contemporary urban environments with adaptable infrastructure. This study examines how GeoAI-enabled urban analysis and participatory design methodologies can enhance urban well-being while preserving cultural heritage in small and medium-sized historic centres. The research develops a replicable methodological framework combining advanced technologies (AI, big data, wearable devices) with Living Lab participatory processes. The approach operationalizes "urban well-being" through three measurable dimensions: physical comfort (route optimization based on weather, terrain, facilities), cultural access (personalized itineraries considering tourist density), and perceived safety (recommendations using social media sentiment, lighting data, population density). Data governance follows GDPR protocols ensuring privacy protection and algorithmic transparency through Explainable AI (XAI). Pilot sites in Calabria represent diverse typologies: peripheral centres, high-tourism destinations, isolated villages, coastal settlements, and centres near natural parks. Expected impacts span individual (enhanced comfort, safety), community (social cohesion, participation), territorial (sustainable tourism, economic vitality), and governance (data-driven resource allocation) levels. The study demonstrates that technology, integrated within strategic vision and participatory practices, can support heritage-respectful urban regeneration oriented toward collective well-being.
2026
19
151
170
Margherita Tufarelli
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1474375
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