Abstract
During the past years Web 2.0 technologies have caused the emergence of platforms where users can share data related to their activities which in some cases are then publicly released with open licenses. Popular categories for this include community platforms where users can upload GPS tracks collected during slow travel activities (e.g. hiking, biking and horse riding) and platforms where users share their geolocated photos. However, due to the high heterogeneity of the information available on the Web, the sole use of these user-generated contents makes it an ambitious challenge to understand slow mobility flows as well as to detect the most visited locations in a region. Exploiting the available data on community sharing websites allows to collect near real-time open data streams and enables rigorous spatial-temporal analysis. This work presents an approach for collecting, unifying and analysing pointwise geolocated open data available from different sources with the aim of identifying the main locations and destinations of slow mobility activities. For this purpose, we collected pointwise open data from the Wikiloc platform, Twitter, Flickr and Foursquare. The analysis was confined to the data uploaded in Lombardy Region (Northern Italy) – corresponding to millions of pointwise data. Collected data was processed through the use of Free and Open Source Software (FOSS) in order to organize them into a suitable database. This allowed to run statistical analyses on data distribution in both time and space by enabling the detection of users’ slow mobility preferences as well as places of interest at a regional scale.
Highlights
The diffusion of GPS-enabled mobile devices has boosted an exponential growth of social networks as well as a new dimension to community-based web platforms through which an increasing number of users interact and share geolocated information about their activities through photos, location check-ins, relevant opinions etc
We present here an experimental procedure to highlight locations of interest as well as mobility patterns for the Lombardy Region through the analysis of geolocated content generated by the users -and available as open data- from different web platforms
We presented an experimental approach to exploit usergenerated content in order to highlight users’ mobility preferences and locations of interest for the Lombardy Region
Summary
The diffusion of GPS-enabled mobile devices has boosted an exponential growth of social networks as well as a new dimension to community-based web platforms through which an increasing number of users interact and share geolocated information about their activities through photos, location check-ins, relevant opinions etc. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B2, 2016 XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic environments ranging from highly populated cities to the alpine glaciers, passing through its famous sub alpine lakes (e.g. Como Lake) and wide plains (e.g. the Po River valley) Thanks to this territorial variety, the Lombardy Region is a good candidate for studying slow mobility through its wide range of different environments to practice these kind of activities. An effective knowledge of the ongoing activities within the territory is a relevant issue to be tackled in order to better exploit this peculiarity in terms of territorial accessibility, environmental protection as well as tourism promotion According to this purpose, we present here an experimental procedure to highlight locations of interest as well as mobility patterns for the Lombardy Region through the analysis of geolocated content generated by the users -and available as open data- from different web platforms
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