INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION Conceptualization of Place via Spatial Clustering and Co- occurrence Analysis.

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Presentation transcript:

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION Conceptualization of Place via Spatial Clustering and Co- occurrence Analysis 2009 International Workshop on Location Based Social Networks (LBSN’09) 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 2 11/03/2009 From

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 3 11/03/2009 Social tools

4 11/03/2009 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 =  geo:lon=longitude e.g. geolong= 4.269

5 11/03/2009 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 11/03/2009 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 11/03/2009 Photos with tags = locations with tags 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 8 11/03/2009

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

10 11/03/2009 Selected photos from Flickr

11 11/03/2009 Where is the beef?  2008 amsterdam canal europe holland netherlands noordholland north travel The most frequently occurring 20%

12 11/03/2009 Steps for extracting conceptualization of place Tags crawling geotagged & tagged photos database Spatial clusteringCo-occurrence analysisPlace concepts

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 13 11/03/2009 p q MinPts = 5 Eps = 1 cm

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 p 1, …, p n, p 1 = q, p n = p such that p i+1 is directly density-reachable from p i  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 /03/2009 p q p1p1 pq o

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 /03/2009

16 11/03/2009 Density-Based Clustering: Results

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 /03/2009

18 11/03/2009 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 11/03/2009 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 11/03/2009 Co-occurrence correlations Photo-tag matrix tag-tag correlation matrix

21 11/03/2009 The correlation between the tag “amsterdam" and the tags of several landmarks associated to Amsterdam Distance Correlation coefficient

22 11/03/2009 Conceptualizing places in 2500 meters

23 11/03/2009 Conceptualizing places 150 meters

24 11/03/2009 Conceptualizing places in 75 meters

Schiphol airport 25 11/03/2009

Anne Frank House 26 11/03/2009

Rijksmuseum 27 11/03/2009

28 11/03/2009 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

INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION Thank you for your attention! Dongpo Deng