Exportar Publicação

A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Pappa, A., Paio, A., Duering, S. & Chronis, A. (2022). Understanding participation through a data-driven approach. In Herrera, P. C., Dreifuss-Serrano, C., Arris Calderón, L. F., and Gómez Zamora, P. (Ed.), SIGraDi 2022: Critical Appropriations. (pp. 77-88). Santiago de Surco, Peru: Universidad Peruana de Ciencias Aplicadas (UPC).
Exportar Referência (IEEE) 
A. Pappa et al.,  "Understanding participation through a data-driven approach", in SIGraDi 2022: Critical Appropriations, Herrera, P. C., Dreifuss-Serrano, C., Arris Calderón, L. F., and Gómez Zamora, P., Ed., Santiago de Surco, Peru, Universidad Peruana de Ciencias Aplicadas (UPC), 2022, pp. 77-88
Exportar BibTeX 
@inproceedings{pappa2022_1734897336163,
	author = "Pappa, A. and Paio, A. and Duering, S. and Chronis, A.",
	title = "Understanding participation through a data-driven approach",
	booktitle = "SIGraDi 2022: Critical Appropriations",
	year = "2022",
	editor = "Herrera, P. C., Dreifuss-Serrano, C., Arris Calderón, L. F., and Gómez Zamora, P.",
	volume = "",
	number = "",
	series = "",
	pages = "77-88",
	publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
	address = "Santiago de Surco, Peru",
	organization = "Universidad Peruana de Ciencias Aplicadas (UPC) School of Architecture",
	url = "http://hdl.handle.net/10757/667182"
}
Exportar RIS 
TY  - CPAPER
TI  - Understanding participation through a data-driven approach
T2  - SIGraDi 2022: Critical Appropriations
AU  - Pappa, A.
AU  - Paio, A.
AU  - Duering, S.
AU  - Chronis, A.
PY  - 2022
SP  - 77-88
CY  - Santiago de Surco, Peru
UR  - http://hdl.handle.net/10757/667182
AB  - 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.

ER  -