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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)
Correia, Ricardo Mendes, Guerreiro, Maria Rosália & Brandão, Filipe J.S. (2020). Data Driven Spatial Analysis of Urban Renewal. Network Kernel Density Estimation of Building Renovation. 5th International Symposium  Formal Methods in Architecture.
Exportar Referência (IEEE)
R. F. José et al.,  "Data Driven Spatial Analysis of Urban Renewal. Network Kernel Density Estimation of Building Renovation", in 5th Int. Symp.  Formal Methods in Architecture, Lisboa, 2020
Exportar BibTeX
@misc{josé2020_1711727214188,
	author = "Correia, Ricardo Mendes and Guerreiro, Maria Rosália and Brandão, Filipe J.S.",
	title = "Data Driven Spatial Analysis of Urban Renewal. Network Kernel Density Estimation of Building Renovation",
	year = "2020",
	doi = "ISBN 978-3-030-57509-0",
	howpublished = "Ambos (impresso e digital)"
}
Exportar RIS
TY  - CPAPER
TI  - Data Driven Spatial Analysis of Urban Renewal. Network Kernel Density Estimation of Building Renovation
T2  - 5th International Symposium  Formal Methods in Architecture
AU  - Correia, Ricardo Mendes
AU  - Guerreiro, Maria Rosália
AU  - Brandão, Filipe J.S.
PY  - 2020
DO  - ISBN 978-3-030-57509-0
CY  - Lisboa
AB  - Local and national governments are increasingly sharing openly large amounts of geo-referenced data related to city planning and administrative procedures. As such new opportunities arise for advanced data-oriented tools that are capable of providing insights on the spatio-temporal correla-tion of these phenomena. Kernel Density Estimation (KDE) appears to be an efficient tool for overcoming incomplete data, because not all urban re-habilitation needs to be reported to city hall services. Recently, new re-search has proposed Network Kernel Density Estimation (NKDE) as a more accurate alternative to estimate data in urban areas. This paper aims to provide a vision of the possibilities of integrating urban renewal dispersed datasets. We propose a method to measure the intensity of renovation in a network using the spatial database of building permits from the city of Lis-bon.
ER  -