Abstract
Research in the area of spatial cognition demonstrated that references to landmarks are essential in the communication and the interpretation of wayfinding instructions for human being. In order to detect landmarks, a model for the assessment of their salience has been previously developed by Raubal and Winter. According to their model, landmark salience is divided into three categories: visual, structural, and semantic. Several solutions have been proposed to automatically detect landmarks on the basis of these categories. Due to a lack of relevant data, semantic salience has been frequently reduced to objects’ historical and cultural significance. Social dimension (i.e., the way an object is practiced and recognized by a person or a group of people) is systematically excluded from the measure of landmark semantic salience even though it represents an important component. Since the advent of mobile Internet and smartphones, the production of geolocated content from social web platforms—also described as geosocial data—became commonplace. Actually, these data allow us to have a better understanding of the local geographic knowledge. Therefore, we argue that geosocial data, especially Social Location Sharing datasets, represent a reliable source of information to precisely measure landmark semantic salience in urban area.
Highlights
Researchers of MIT’s Senseable City Lab who worked on Real-Time Cities brought the potential of location-based datasets into focus in the understanding of urban dynamics [1,2,3,4,5,6]
Landmark detection is based on the same global approach: (1) first of all, a neighborhood analysis is performed at each choice point; (2) among the buildings identified, various analysis of their attributes are performed in order to determine an outlier; and (3) this outlier is selected as the landmark candidate [44]
We agree with this point of view and argue that social location sharing datasets generated by users of online social networks can be effectively exploited to enhance the measure of landmark semantic salience
Summary
Researchers of MIT’s Senseable City Lab who worked on Real-Time Cities brought the potential of location-based datasets into focus in the understanding of urban dynamics [1,2,3,4,5,6]. Data are transmitted in a more or less constant flow (e.g., when users of smartphones leave the geolocation functionality activated) while in the second one, users are producing spatial information deliberately (e.g., a Swarm check-in or a geolocated Facebook publication). The analysis of such data in this context matches with the concept of smart city The latter represents for the appropriate solution facing the steady increase of global urbanization. Check-ins datasets can be used to improve human wayfinding and smart mobility by detecting relevant semantic landmarks. In order to defend our argument, we detail a brief state of art related to the concept of wayfinding We focus both on landmarks and systems designed for their automatic detection.
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