By Drew Bush
In two articles published this January, Geothink researcher Stéphane Roche and his doctoral student Teriitutea Quesnot argue that not all geosocial data is equivalent, and that better data on the social significance of a landmark could greatly enhance our understanding of human wayfinding behavior. A Professor of Geomatics at University of Laval, Roche’s research over the past five years has focused on how new forms of digital spatiality affect spatial reasoning skills, and the capacity of individuals to engage in the city.
Entitled “Measure of Landmark Semantic Salience through Geosocial Data Streams,” the first paper was published by Roche in the ISPRS International Journal of Geo-Information. The authors write that a lot of research “in wayfinding is done in order to enable individuals to reach as quickly as possible a desired destination, to help people with disabilities by designing cognitively appropriate orientation signs, and reduce the fact of being lost.”
Previous researchers in the field of geo-cognition have tried to characterize the salience of landmarks in human wayfinding behaviour. Most have classified differing landmarks by visual, structural and semantic cues. However, the social dimensions of a landmark, such as how they are practised or recognized by individuals or groups, had been excluded from its semantic salience (or often reduced to historical or cultural cues), according to the authors.
Instead, the authors follow in a tradition of research which utilizes text mining from the web to understand how places are expressed by Internet users rather than relying on how they are visually perceived. Such an approach has been made possible by social media and mobile communications technology that has resulted in vast user-generated databases that constitute “the most appropriate VGI data for the detection of global semantic landmarks.”
In conducting their research, the authors examined world famous landmarks and detected semantic landmarks in the cities of Vienna and Paris using data from Foursquare API v2 and Facebook API v2.1. from September 29, 2014 to November 15, 2014.
In a second paper entitled “Platial or Locational Data? Toward the Characterization of Social Location Sharing,” the authors expanded on this theme in arguing that not all geosocial data is equal. The paper was presented at 48th Hawaii International Conference on Systems Sciences this past January.
Some data, which the authors consider “platial,” relates more to users experiences of a given place while “spatial” data is tied to the actual coordinates of a place. In the context of geosocial data, spatial data might mean the exact location of the Eiffel tower while palatial could refer to a person passing by the Eiffel tower or taking a photo of it from another location.
Because each can potentially represent a very different kind of data point, they must be treated differently. As the authors write, “With the objective of a better understanding of urban dynamics, lots of research projects focused on the combination of geosocial data harvested from different social media platforms. Those analyses were mainly realized on a traditional GIS, which is a tool that does not take into account the platial component of spatial data. Yet, with the advent of Social Location Sharing, the inconvenience of relying on a classic GIS is that a large part of VGI is now more palatial than locational.”
Find links to each article along with their abstracts below.
Measure of Landmark Semantic Salience through Geosocial Data Streams
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.
Platial or Locational Data? Toward the Characterization of Social Location Sharing
Sharing “location” information on social media became commonplace since the advent of smartphones. Location-based social networks introduced a derivative form of Volunteered Geographic Information (VGI) known as Social Location Sharing (SLS). It consists of claiming “I am/was at that Place”. Since SLS represents a singular form of place-based (i.e. platial) communication, we argue that SLS data are more platial than locational. According to our data classification of VGI, locational data (e.g. a geotagged tweet which geographic dimension is limited to its coordinate information) are a reduced form of platial data (e.g. a Swarm check-in). Therefore, we believe these two kinds of data should not be analyzed on the same spatial level. This distinction needs to be clarified because a large part of geosocial data (i.e. spatial data published from social media) tends to be analyzed on the basis of a locational equivalence and not on a platial one.
If you have thoughts or questions about the article, get in touch with Drew Bush, Geothink’s digital journalist, at firstname.lastname@example.org.