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

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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

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Social tools

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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€

Tag(k) ≡ ∀Keywords(k)

∧∃o(Object(o)∧associatedTo(k,o))

∧∃u(User(u)∧createdBy(k,u))

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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?

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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

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Photos with tags = locations with tags

Tags

Tags

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

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Photos and Tags in Flickr

Tags

Geo-Tag

Time-Tag

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Selected photos from Flickr

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Where is the beef?

2008 amsterdam canal europe holland netherlands noordholland north travel

The most frequently occurring 20%

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Steps for extracting conceptualization of place

Tags

Tags

Tags

Tags

crawling

geotagged & tagged photos

database

Spatial clustering Co-occurrence analysis Place 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

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pq

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 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.

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p

qp1

p q

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.

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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.

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Co-occurrence matrix

The element at (i,j) is the tag count or frequency of the i’th tag in the j’th photos

jd

mnm

nTi

xx

xx

t

,1,

,11,1

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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

][ ,1, niiTi xxt

jm

j

j

x

x

d

,

,1

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Co-occurrence correlations

Photo-tag matrix

tag-tag correlation matrix

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The correlation between the tag “amsterdam" and the tags of several landmarks associated to Amsterdam

Distance

Correlation coefficient

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Conceptualizing places in 2500 meters

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Conceptualizing places 150 meters

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Conceptualizing places in 75 meters

Schiphol airport

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Anne Frank House

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Rijksmuseum

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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 Dengdeng@itc.nl