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Article

Deep Learning to Unveil Correlations between Urban Landscape and Population Health

1
Department of Electrical, Computer and Biomedical Engineering, via Ferrata 5, 27100 Pavia, Italy
2
IRCCS ICS Maugeri, via S. Maugeri 2, 27100 Pavia, Italy
3
Department of Civil Engineering and Architecture, via Ferrata 3, 27100 Pavia, Italy
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in 17th International Conference on Smart Living and Public Health (iCOST).
Sensors 2020, 20(7), 2105; https://doi.org/10.3390/s20072105
Received: 29 February 2020 / Revised: 4 April 2020 / Accepted: 6 April 2020 / Published: 8 April 2020
The global healthcare landscape is continuously changing throughout the world as technology advances, leading to a gradual change in lifestyle. Several diseases such as asthma and cardiovascular conditions are becoming more diffuse, due to a rise in pollution exposure and a more sedentary lifestyle. Healthcare providers deal with increasing new challenges, and thanks to fast-developing big data technologies, they can be faced with systems that provide direct support to citizens. In this context, within the EU-funded Participatory Urban Living for Sustainable Environments (PULSE) project, we are implementing a data analytic platform designed to provide public health decision makers with advanced approaches, to jointly analyze maps and geospatial information with healthcare and air pollution data. In this paper we describe a component of such platforms, which couples deep learning analysis of urban geospatial images with healthcare indexes collected by the 500 Cities project. By applying a pre-learned deep Neural Network architecture, satellite images of New York City are analyzed and latent feature variables are extracted. These features are used to derive clusters, which are correlated with healthcare indicators by means of a multivariate classification model. Thanks to this pipeline, it is possible to show that, in New York City, health care indexes are significantly correlated to the urban landscape. This pipeline can serve as a basis to ease urban planning, since the same interventions can be organized on similar areas, even if geographically distant. View Full-Text
Keywords: transfer learning; deep learning; urban landscape; health indexes; public health; convolutional neural networks transfer learning; deep learning; urban landscape; health indexes; public health; convolutional neural networks
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MDPI and ACS Style

Pala, D.; Caldarone, A.A.; Franzini, M.; Malovini, A.; Larizza, C.; Casella, V.; Bellazzi, R. Deep Learning to Unveil Correlations between Urban Landscape and Population Health. Sensors 2020, 20, 2105. https://doi.org/10.3390/s20072105

AMA Style

Pala D, Caldarone AA, Franzini M, Malovini A, Larizza C, Casella V, Bellazzi R. Deep Learning to Unveil Correlations between Urban Landscape and Population Health. Sensors. 2020; 20(7):2105. https://doi.org/10.3390/s20072105

Chicago/Turabian Style

Pala, Daniele, Alessandro A. Caldarone, Marica Franzini, Alberto Malovini, Cristiana Larizza, Vittorio Casella, and Riccardo Bellazzi. 2020. "Deep Learning to Unveil Correlations between Urban Landscape and Population Health" Sensors 20, no. 7: 2105. https://doi.org/10.3390/s20072105

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