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2009 International Workshop on Location Based Social Networks (LBSN’09). Conceptualization of Place via Spatial Clustering and Co-occurrence Analysis. Dong–Po Deng; Tyng–Ruey Chuang; Rob Lemmens. Nov. 3, 2009, Seattle, WA, USA. GeoInformation is increasing on the Web.
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2009 International Workshop on Location Based Social Networks (LBSN’09) Conceptualization of Place via Spatial Clustering and Co-occurrence Analysis Dong–Po Deng; Tyng–Ruey Chuang; Rob Lemmens Nov. 3, 2009, Seattle, WA, USA
GeoInformation is increasing on the Web • It’s a common activity for people to search and share geo-referenced information and resource on the Web From http://www.datenform.de/mapeng.html
Folksonomy • A tagging system allows users to classify objects of interests by keywords or terms • Folksonomy = practice of personal tagging of information and objects in social environment while people consume the information and use the objects Social tools
Tags and Geo-tags • Tagging is a process that is established by keywords (k), users (u), and objects (o) • Geotag • geo:lat=latitude e.g. geo:lat = 51.758 • geo:lon=longitude e.g. geolong= 4.269
Questions are … • Is geospatial data created in a social network a valuable production for a geospatial society in general? • How to extract the geospatial information from user-generated contents in a social network?
Places as artifacts • Place is a center of meaning constructed by experiences • Place may be significant to any individual or group, and may exist at any scale • Locations become places only when activities occur that cause them to become imbued with meaning • Place provides the conditions of possibility for creative social practice
Tags Tags Tags Tags Photos with tags = locations with tags
Collective intelligence • Tags should give rise to emergent semantics and shared conceptualization • Accumulation of tags on shared objects often express common consensus • Patterns and trends emerge from the collaboration and competition of many individuals are able to turn out structured information from tag-based system despite the lack of ontology and priori defined semantics
Photos and Tags in Flickr Tags Geo-Tag Time-Tag
Where is the beef? • 2008 amsterdam canaleuropehollandnetherlandsnoordhollandnorthtravel The most frequently occurring 20%
Tags Tags Tags Tags Steps for extracting conceptualization of place crawling database geotagged & tagged photos Spatial clustering Co-occurrence analysis Place concepts
p MinPts = 5 Eps = 1 cm q DBSCAN is a density-based algorithm • Two global parameters: • Eps: Maximum radius of the neighbourhood • MinPts: Minimum number of points in an Eps-neighbourhood of that point • Core Object: object with at least MinPts objects within a radius ‘Eps-neighborhood’ • Border Object: object that on the border of a cluster
p q o Density-Based Clustering: Background • Density-reachable • A point p is density-reachable from a point q wrt Eps, MinPts if there is a chain of points p1, …, pn, p1 = q, pn = p such that pi+1 is directly density-reachable from pi • Density-connected • A point p is density-connected to a point q wrt. Eps, MinPts if there is a point o such that both, p and q are density-reachable from o wrt. Eps and MinPts. p p1 q
DBSCAN: The Algorithm • Arbitrary select a point p • Retrieve all points density-reachable from p wrt Eps and MinPts. • If p is a core point, a cluster is formed. • If p is a border point, no points are density-reachable from p and DBSCAN visits the next point of the database. • Continue the process until all of the points have been processed.
Co-occurrence analysis • Co-occurrence can be interpreted as an indicator of semantic similarity or an idiomatic expression. • Co-occurrence assumes interdependency of the two terms • Semantic similarity is a concept whereby a set of documents or terms within term lists are assigned a metric based on the likeness of their meaning / semantic content.
Co-occurrence matrix • The element at (i,j) is the tag count or frequency of the i’th tag in the j’th photos
Co-occurrence matrix • A row in the matrix is a vector of the tag’s occurrence in all photos: • While a column is a vector of the occurrence of all tags in a photo
Co-occurrence correlations tag-tag correlation matrix Photo-tag matrix
The correlation between the tag “amsterdam" and the tags of several landmarks associated to Amsterdam Correlation coefficient Distance
Conclusions and future works • Without the use of suitable spatial clustering, detailed information about a place is veiled by high frequency tags • A conceptualization of place is unveiled by tag co-occurrences at a suitable spatial scale • Location-based applications can be developed to suggest tags to users as they take photos • In the future we will ground the semantics between pairs of tags via the use of gazetteers or dictionaries