Understanding participation though a data-driven approach

Abstract

Participatory models in urban regeneration are 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 BIP/ZIP and identify correlation patterns between their data and especially the areas of implementation, the networks of partners and the identified activities. Departing from the pilot-case of BIP/ZIP, the proposed methodology is a first step towards the development of a generalizable evaluation framework for participatory models in urban regeneration, that considers them as urban practices and hence evaluates them based on appropriate urban tools.