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.

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Building renovation,NKDE,urban renewal,spatial analysis