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Abstract

Every day millions of social media users upload information as texts, pictures or likes. These online posts are nowadays mainly uploaded via a smartphone, that adds automatically complementary pieces of information such as the device’s location and orientation. This additional material is valuable for public services, and can be used to reinforce knowledge provided by typical methods. This study aims to inquire this additional material to observe the influence of city features on public behavior. A semi-automated workflow is introduced to combine two large datasets: the flickR geo-referenced photos (associated with their shooting orientation) and the OpenStreetMap streets’ network. The study is conducted in the city of Lausanne, Switzerland. This workflow promotes a novel approach to download, filter, compute and visualize large cluttered datasets. The investigations showed a significant difference between South/North photos’ orientation with a South dominance. Furthermore, the photographs’ orientation appears to be related to the street network, or city elements (such as remarkable buildings, fountains) only at a local scale; no connection was established at a larger scale. These results can be useful in urban planning for the diagnosis of a public place practice by its users (i.e., residents, tourists, etc.). An improved diagnosis promotes a better knowledge of a public space’s remarkable elements (by their attractiveness or unsightliness), easing the decision on conservation or transformation of these elements. Other applications are also outlined, notably in the touristic sector or the landscape preservation.

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