Conceptualization of Place via Spatial Clustering and Co-occurrence Analysis

1. 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

2. 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

3. 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

4. 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

5. 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?

6. 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

7. Tags Tags Tags Tags Photos with tags = locations with tags

8. 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

9. Photos and Tags in Flickr Tags Geo-Tag Time-Tag

10. Selected photos from Flickr

11. Where is the beef? • 2008 amsterdam canaleuropehollandnetherlandsnoordhollandnorthtravel The most frequently occurring 20%

12. Tags Tags Tags Tags Steps for extracting conceptualization of place crawling database geotagged & tagged photos Spatial clustering Co-occurrence analysis Place concepts

13. 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

14. 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

15. 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.

16. Density-Based Clustering: Results

17. 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.

18. Co-occurrence matrix • The element at (i,j) is the tag count or frequency of the i’th tag in the j’th photos

19. 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

20. Co-occurrence correlations tag-tag correlation matrix Photo-tag matrix

21. The correlation between the tag “amsterdam" and the tags of several landmarks associated to Amsterdam Correlation coefficient Distance

22. Conceptualizing places in 2500 meters

23. Conceptualizing places 150 meters

24. Conceptualizing places in 75 meters

25. Schiphol airport

26. Anne Frank House

27. Rijksmuseum

28. 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

29. Thank you for your attention!Dongpo Dengdeng@itc.nl