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Title:	Quantifying urban attractiveness from the distribution and density of digital footprints
Author:	Girardin, Fabien; Vaccari, Andrea; Gerber, Alexander; Biderman, Assaf; Ratti, Carlo
Other authors:	Universitat Pompeu Fabra
Abstract:	In the past, sensors networks in cities have been limited to fixed sensors, embedded in particular locations, under centralised control. Today, new applications can leverage wireless devices and use them as sensors to create aggregated information. In this paper, we show that the emerging patterns unveiled through the analysis of large sets of aggregated digital footprints can provide novel insights into how people experience the city and into some of the drivers behind these emerging patterns. We particularly explore the capacity to quantify the evolution of the attractiveness of urban space with a case study of in the area of the New York City Waterfalls, a public art project of four man-made waterfalls rising from the New York Harbor. Methods to study the impact of an event of this nature are traditionally based on the collection of static information such as surveys and ticket-based people counts, which allow to generate estimates about visitors’ presence in specific areas over time. In contrast, our contribution makes use of the dynamic data that visitors generate, such as the density and distribution of aggregate phone calls and photos taken in different areas of interest and over time. Our analysis provides novel ways to quantify the impact of a public event on the distribution of visitors and on the evolution of the attractiveness of the points of interest in proximity. This information has potential uses for local authorities, researchers, as well as service providers such as /nmobile network operators.
Subject(s):	-Sistema de posicionament global -- Estudi de casos -Turisme -- Nova York (Estats Units d'Amèrica) -- Estudi de casos -Comunicacions mòbils, Sistemes de -- Estudi de casos -Digital earth -Urban studies -Urban indicators -Reality mining -Digital footprints -Pervasive data mining
Rights:	This work is licensed under the Creative Commons Attribution-Non-commercial Works 3.0 http://creativecommons.org/licenses/by-nc-nd/3.0/
Document type:	Article Article - Published version
Published by:	Joint Research Centre of the European Commission
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