Exploring Geomorphometry through User Generated Content: Comparing an Unsupervised Geomorphometric Classification with Terms Attached to Georeferenced Images in Great Britain

User generated content such as the georeferenced images and their associated tags found in Flickr provides us with opportunities to explore how the world is described in the non-scientific, everyday language used by contributors. Geomorphometry, the quantitative study of landforms, provides methods to classify Digital Elevation Models (DEMs) according to attributes such as slope and convexity. In this article we compare the terms used in Flickr and Geograph in Great Britian to describe georeferenced images to a quantitative, unsupervised classification of a DEM, using a well established method, and explore the variation of terms across geomorphometric classes and space. Anthropogenic terms are primarily associated with more gentle slopes, while terms which refer to objects such as mountains and waterfalls are typical of steeper slopes. Terms vary both across and within classes, and the source of the user generated content has an influence on the type of term used with Geograph, a collection which aims to document the geography of Great Britain, dominated by features which might be observed on a map.

[1]  Mor Naaman,et al.  Methods for extracting place semantics from Flickr tags , 2009, TWEB.

[2]  Carsten Keßler,et al.  Bottom-Up Gazetteers: Learning from the Implicit Semantics of Geotags , 2009, GeoS.

[3]  Carlo Ratti,et al.  Quantifying urban attractiveness from the distribution and density of digital footprints , 2009, Int. J. Spatial Data Infrastructures Res..

[4]  Jon M. Kleinberg,et al.  Mapping the world's photos , 2009, WWW '09.

[5]  David Coleman,et al.  Volunteered Geographic Information: the nature and motivation of produsers , 2009, Int. J. Spatial Data Infrastructures Res..

[6]  John P. Wilson,et al.  DEM resolution dependencies of terrain attributes across a landscape , 2007, Int. J. Geogr. Inf. Sci..

[7]  Haklay.  How good is OpenStreetMap information ? A comparative study of OpenStreetMap and Ordnance Survey datasets for London and the rest of England , 2008 .

[8]  Jochen Schaab,et al.  Automated Footprint Generation from Geotags with Kernel Density Estimation and Support Vector Machines , 2009, Spatial Cogn. Comput..

[9]  M. Haklay.  How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets , 2010 .

[10]  Peter A. Burrough,et al.  High-resolution landform classification using fuzzy k-means , 2000, Fuzzy Sets Syst..

[11]  Abebe Rorissa,et al.  User-generated descriptions of individual images versus labels of groups of images: A comparison using basic level theory , 2008, Inf. Process. Manag..

[12]  M. Goodchild.  Citizens as sensors: the world of volunteered geography , 2007 .

[13]  T. Hengl,et al.  Chapter 1 Geomorphometry: A Brief Guide , 2009 .

[14]  Abebe Rorissa,et al.  A comparative study of Flickr tags and index terms in a general image collection , 2010, J. Assoc. Inf. Sci. Technol..

[15]  Mor Naaman,et al.  Why we tag: motivations for annotation in mobile and online media , 2007, CHI.

[16]  Y. Deng,et al.  New trends in digital terrain analysis: landform definition, representation, and classification , 2007 .

[17]  B. Tversky,et al.  Categories of environmental scenes , 1983, Cognitive Psychology.

[18]  Christian Gschwend,et al.  Comparing Flickr tags to a geomorphometric classification , 2011 .

[19]  Houda Bouamor,et al.  Mining a Multilingual Geographical Gazetteer from the Web , 2009, 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology.

[20]  Megan A. Winget,et al.  USER-DEFINED CLASSIFICATION ON THE ONLINE PHOTO SHARING SITE FLICKR…OR, HOW I LEARNED TO STOP WORRYING AND LOVE THE MILLION TYPING MONKEYS , 2006 .

[21]  Jochen Schaab,et al.  An Evaluation of Kernel Density Estimation and Support Vector Machines for Automated Generation of Footprints for Imprecise Regions from Geotags , 2008 .

[22]  David M. Mark,et al.  Naive Geography , 1995, COSIT.

[23]  Martin Tomko,et al.  Automatically generating keywords for georeferenced images , 2010 .

[24]  Jo Wood,et al.  Describing place through user generated content , 2011, First Monday.

[25]  David M. Mark,et al.  Landscape Categories in Yindjibarndi: Ontology, Environment, and Language , 2003, COSIT.

[26]  Joseph. Wood,et al.  The geomorphological characterisation of Digital Elevation Models , 1996 .

[27]  Ross Purves,et al.  Exploring place through user-generated content: Using Flickr tags to describe city cores , 2010, J. Spatial Inf. Sci..

[28]  Clare Davies,et al.  User Needs and Implications for Modelling Vague Named Places , 2009, Spatial Cogn. Comput..

[29]  Barry Smith,et al.  Do Mountains Exist? Towards an Ontology of Landforms , 2003 .

[30]  Marieke Guy,et al.  Folksonomies: Tidying Up Tags? , 2006, D Lib Mag..

[31]  Roelof van Zwol,et al.  Flickr tag recommendation based on collective knowledge , 2008, WWW.

[32]  Martin F. Porter,et al.  An algorithm for suffix stripping , 1997, Program.

[33]  Yuri Gorokhovich,et al.  Accuracy assessment of the processed SRTM-based elevation data by CGIAR using field data from USA and Thailand and its relation to the terrain characteristics , 2006 .

[34]  R. J. Pike,et al.  Automated classifications of topography from DEMs by an unsupervised nested-means algorithm and a three-part geometric signature , 2007 .

[35]  Ross Purves,et al.  A Theoretical Grounding for Semantic Descriptions of Place , 2007, W2GIS.

[36]  Keith C. Clarke,et al.  Interactive Visual Exploration of a Large Spatio-temporal Dataset: Reflections on a Geovisualization Mashup. , 2007, IEEE Transactions on Visualization and Computer Graphics.

[37]  Michael F. Goodchild,et al.  Where's Downtown?: Behavioral Methods for Determining Referents of Vague Spatial Queries , 2003 .