Understanding participation through a data-driven approach

• Main Author: Pappa, A.
• Publication Date: 2022
• Other Authors: Paio, A., Duering, S., Chronis, A.
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10071/28830
• topic: Participatory strategies; Participation evaluation; Data-driven evaluation; Unsupervised learning; Data visualization

Participatory models of urban regeneration have been increasingly integrated in local agendas. Yet there is still a need for evaluation methodologies of those models and their impact. This paper presents a data-driven and computational methodology to measure the impact of the BIP/ZIP Program in Lisbon. Using qualitative coding, data integration, unsupervised machine learning models for data clustering and interactive visualization dashboards the study aims to explore the large and complex dataset of the projects of the BIP/ZIP program and identify correlation patterns between their areas of implementation, the networks of project partners and the identified activities of the projects. The proposed methodology is a first step towards the development of a generalizable evaluation framework for participatory models and aims to inform the further development of similar participatory models of urban regeneration.