Exploring Volunteered Geographic Information to Describe Place: Visualization of the Geograph British Isles Collection

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1 Exploring Volunteered Geographic Information to Describe Place: Visualization of the Geograph British Isles Collection Jason Dykes 1, Ross Purves 2, Alistair Edwardes 2 & Jo Wood 1 1 gicentre, School of Informatics, City University London, EC1V 0HB Tel. ( ) Fax ( ) jad7 /~ jad7 jwo 2 Department of Geography, University of Zurich, Switzerland Tel. ( ) Fax ( ) ross.purves KEYWORDS: volunteered geographic information, place, tag cloud, tree map, mashup 1. Introduction Increasing volumes of volunteered geographic information (VGI) provide a rich but complex data source for GIScience (Goodchild, 2007). For example, Edwardes and Purves (2007) consider multiple perspectives on place by analysing the text and images submitted to Geograph British Isles (Geograph, 2007). We extend this work by exploring how such perspectives vary spatially. We do so through visualization an informal method of data exploration that involves the development and use of interactive graphics to investigate data sets with spatial, temporal and other structure. Effective visualization results in ideation and the generation of hypotheses and the approach is increasingly popular for exploring large unknown data sets (Thomas and Cook, 2005). Visualization is particularly appropriate where exploration relies upon the synthesis of complex data that contain spatial relationships and tacit knowledge (MacEachren and Kraak, 2001). Thus we argue that visualization can assist in exploring the complexity of informal multi-authored VGI collections as we apply it to understand notions of place. We support this contention through a series of case studies that use visualization techniques to reveal spatial variations and help develop initial hypotheses relating to them. Each visualizes data volunteered to Geograph as of February 2007: a collection of 340,000 photographs (or geographs ) with titles, comments and other metadata. All geographs are georeferenced to at least 1km precision. Our interest is in use of the terms identified by Edwardes and Purves (2007) involving four basic levels or scene types (STs): beach, village, mountain and hill. We focus on the thirty most popular descriptive terms, or scene type descriptors (STDs), associated with each. The STDs are categorised into ten qualities (adjectives describing the scene), ten elements (objects found in the scene) and ten activities for each for the four STs.

2 2. Visualization of Term Co-occurrence Hierarchy This organisation of terms can be considered a hierarchy: ST - 4 nodes; category - 3 per ST = 12 nodes; STD - 10 per category = 120 nodes. Treemaps (Shneiderman, 1992, 2006) are a popular means of visualizing such hierarchies. 2.1 Static TreeMaps A treemap of the hierarchy of terms used in geograph titles and comments reveals some structure in the use of the English language in Geograph. Figure 1 contains a leaf for each of the 50,000 cases where one of our four STs is associated with a photograph. Each is coloured using an inherited random scheme (Wood and Dykes, submitted) where root nodes are allocated a random colour that is inherited by children with a minor colour mutation. Whilst the hues have no independent meaning, the structure of the graph is reflected in the colours as well as the spatial organisation. We can see that hill dominates over beach, village and mountain and that the none category (no STDs associated with an ST) dominates in each case. Elements co-occur more frequently with each ST than adjectives and activities. Steep and black are used more frequently with hill than mountain, etc. Our static treemaps permit zooming and panning. The 22,000 cases where STs occur in titles are shown in Figure 2 (top), again with colours that are randomised initially and so do not persist between figures. Figure 2 (bottom) removes cases where no STDs co-occur with STs in titles ( none ) showing the 5,000 remaining cases. Comparison of the treemaps generated from titles and comments and focussing on the co-occurrence of STs and STDs may provide general and specific insights into how Geograph contributors use language. For example: adjectives occur less frequently in titles than comments; ridge occurs less frequently with hill in titles than might be expected from its use in comments (compare with track ); village green is overused in titles, etc. 2.2 Interactive and Spatial Treemaps Treemaps provide a useful overview of the use of language in Geograph, but how does this vary spatially? We can add spatial symbolism to statistical graphics and information visualization (Dykes, 1995). In Figure 3 colour is used to depict location with eastings represented in red ( from the OSGB origin) and northings in green ( ). This allows us to explore the spatial structure of the use of co-occurring terms. Reds represent the south-east and browns the south-west, with colours lightening as we move north. Light greens and yellows relate to locations in the north-east and greens the north-west. Consistent colouring of the leaves in any node reveals spatial autocorrelation black and mountains are perhaps unsurprisingly used in the Black Mountains, but it is interesting to note that these terms do not co-occur elsewhere. Village has a southern and eastern bias with little use in Scotland. Summit and valley have very different geographies when used in conjunction with hill. The strong distinct colours associated with beach (see shingle ) relate to its peripheral geography. Consideration of the consistency, texture and dominant colours of any node may help relate geography and the language volunteered to Geograph. Dynamic links can help us explore the relationship between the photographs and text in geographs. Our treemaps provide dynamic links to the Geograph server enabling us to download image icons as leaves are brushed and display their associated text (Figure 3).

3 Figure 1. Treemaps of terms occurring in geograph titles and comments for STs. Node sizes represent term occurrence. Colours emphasize the ST / category / STD hierarchy with an inherited random scheme.

4 Figure 2. Treemaps of terms occurring in geograph titles for STs. Node sizes represent term occurrence. Colours emphasize the ST / category / STD hierarchy with an inherited random scheme. Treemaps show all occurrences of STs in titles (top) and the subset of occurrences of STs in titles where they co-occur with STDs (bottom). The second treemap is thus a focussed version of the first, with the none category removed. 2.3 Image Treemap Treemaps can be used in creative ways to associate hierarchical structure with complex information summaries - for example Wattenburg s art exhibit on the colour of language (Wattenburg, 2005). Figure 4 provides a direct link between Geograph term hierarchy and photographs through an image treemap that offers considerable scope for exploring the relationships between ST, STD, text and imagery.

5 Figure 3. Treemap of terms occurring in geograph titles for STs. Node sizes represent term occurrence. Colours use geographic shading. Image Copyright Claire Pegrum and licensed for reuse under Attribution-Share Alike 2.0 Generic Creative Commons Licence (

6 Figure 4. Treemap of terms occurring in geograph titles for STs (top) with detail (bottom). Node sizes represent term occurrence. Images Copyright Geograph British Isles Photographers ( and licensed for reuse under Attribution-Share Alike 2.0 Generic Creative Commons Licence (

7 3. Exploring the Spatial Variation in Term Co-occurrence Mapping individual terms and term co-occurrences spatially allows us to explore these relationships further. We use two techniques to do so. 3.1 Exploring National Trends A chi statistic relates observations of phenomena with some expectation and mapping the results helps us to explore spatial variations in observations (Wood et al., 2007). The initial assumption that any STD is equally likely to occur with any ST allows us to generate chi statistics for pairs of co-occurring STs and STDs. We can do so at a number of spatial resolutions and have developed software to support the visualization of the results. Maps of the national variation in the co-occurrence of particular combinations of STs and STDs found in titles, comments or both of these can be interactively selected at three sampling resolutions. For example, Figure 5 shows that shingle as a descriptor of beach has a spatial bias towards the coasts of the north-west and south-east and away from the east coast and the south-west when considered at a resolution of 10km. The persistence of the pattern can be explored at other resolutions. Figure 5. An exploratory interface to chi statistic maps of national trends. The three maps show: Left - ST density; Centre - STD density; Right - chi statistic for the selected ST / STD combination. The chi statistic is mapped using a diverging scheme with blues representing fewer observations than expected and reds representing more. Darker colours relate to more extreme observations.

8 The spatial relationship between black and mountain persists both at a number of scales and whether title, comment or both are considered. The maps shown in Figure 6 show that where mountain is used to describe geographs in mountainous areas close to the Black Mountains the term black is employed as a descriptor less than expected. This may suggest a possible geographic shielding effect, and may also reflect a difference in the predominant language used in north and mid- Wales. Our interactive tools and direct links between hierarchy, geography and text help us explore the detail. The co-occurrence of the terms hill and summit and hill and valley also have patterns that persist through the resolutions considered here. The former have a northern bias, the latter are concentrated in the south and south-west. The case of hill and summit is interesting as the spatial distribution varies according to whether title or comment is considered. The former case biases the distribution towards the Scottish Borders, where many toponyms include Hill, as opposed to the Highlands where hill or mountain names are typically Gaelic (Figure 7). Figure 6. The geography of black and mountain : Left - density of mountain ST; Centre - density of mountain ST with black STD; Right - chi statistic of mountain with black STD ST. Sampling resolution 10km. Terms found in geograph titles or comments. 3.2 Exploring Local Variation We can explore local variation in the terms used through a geovisualization mashup (Wood et al., 2007). Here we use spatial tag clouds (Slingsby et al., 2007) that are updated dynamically as we explore through a geobrowser to reveal geographic emphases. The tag cloud of all terms used in geographs located around the Manchester area shown in Figure 8 has a distinctly local flavour. Tag clouds for each of the STs show how STDs are used in the selected locality (Figure 8, top right). The tag clouds are linked to the maps so that clicking a term will reveal the associated geograph locations and selecting a location links to the geograph (Figure 8, bottom right). The mashup allows us to relate geographs, their locations and descriptions to ancillary data including formal spatial data sets, other forms of VGI and our own derivatives such as the chi surfaces.

9 Figure 7. The geography of hill and summit : Left - density of hill ST; Centre - density of hill ST with summit STD; Right - chi statistic of hill ST with summit STD. Sampling resolution 20km. Compare terms found in geograph title (top) and comment (bottom) fields. 4. Conclusion Purves and Edwardes (submitted) argue that analysis of the Geograph collection allows us to begin addressing the description of place. Edwardes and Purves (2007) compare the frequencies of terms derived in empirical research with those occurring in the Geograph VGI and consider the cooccurrence of terms and selected descriptors. We use visualization to explore ways in which the cooccurrence of these terms and descriptors varies geographically. Our preliminary results demonstrate ways in which visualization can contribute to this process by providing insights into the nature, structure and geography of VGI. Further developments of the treemap layout algortithm, particularly arranging nodes to reflect their geographic location (Wood and Dykes, submitted) should allow refined visual assessment of spatial pattern in co-occurrences.

10 Figure 8. Linked graphics showing spatial tag clouds of terms used to describe geographs in selected area. Left - all terms used; Top right - STDs used with the four STs under consideration; Bottom right - STDs used with four STs linked to geograph locations and Geograph details. Image Copyright Chris Shaw and licensed for reuse under Attribution-Share Alike 2.0 Generic Creative Commons Licence (

11 There is scope for enhancing the work by exploring our assumptions and performing more sophisticated analysis of the text by, for example, reducing the rate of false positives and investigating the effects of weighting our ST / STD occurrence count towards cases where terms are used in close combination. Accounting for the effects of individuals or coordinated activity is an important generic issue relating to the use of VGI that visualization can help us detect. Work to explore Geograph further through the development of additional visualization functionality is ongoing as are efforts to compare the spatial distributions revealed in Geograph with other sources of VGI including the Flickr photographic database. 5. Acknowledgments RSP and AJE s research for this paper is part of the project TRIPOD supported by the European Commission under contract We would also like to gratefully acknowledge Barry Hunter and contributors to Geograph British Isles, see whose work is made available under the following Creative Commons Attribution-ShareAlike 2.5 Licence ( References Dykes, J. A. (1997). Techniques for Adding Spatial Information to Statistical Plots of Geographical Data. EuroStat: New Techniques and Technologies for Statistics II. W. Kloesgen. Amsterdam, IOS Press: Edwardes, A. J. and R. S. Purves (2007). A theoretical grounding for semantic descriptions of place. M. Ware and G. Taylor (eds.) LNCS: Proceedings of 7th Intl. Workshop on Web and Wireless GIS, W2GIS 2007, Berlin, Springer, Geograph - the 3658 Geograph British Isles Photographers (2007). Geograph British Isles [available 21/12/07] Goodchild, M. F. (2007). "Citizens as sensors: the world of volunteered geography." Geojournal 69: MacEachren, A. M. and M.-J. Kraak (2001). "Research challenges in geovisualization." Cartography and Geographic Information Science 28(1): Purves, R. S. and A. J. Edwardes (submitted). Exploiting Volunteered Geographic Information to describe Place. GISRUK 2008 the 15th national GIS research conference for the UK, Manchester Shneiderman, B. (1992). "Tree visualization with tree-maps: 2-d space-filling approach." ACM Transactions on Graphics 11(1): Shneiderman, B. (2006). Treemaps for space-constrained visualization of hierarchies [available 20/12/07] Slingsby, A., J. Dykes, et al. (2007). Interactive Tag Maps and Tag Clouds for the Multiscale Exploration of Large Spatio-temporal Datasets. E. Banissi (ed.) 11th International Conference on Information Visualization 07, Zurich, IEEE Computer Society, Thomas, J. J. and K. A. Cook, Eds. (2005). Illuminating the Path: Research and Development Agenda for Visual Analytics, IEEE Press. Wattenburg M. (2005). The Color Project, Loop: AIGA Journal of Interactive Design Education [available 21/12/07] Wood, J. and Dykes, J. (submitted). From slice and dice to hierarchical cartograms: Spatial referencing of treemaps. GISRUK 2008 the 15th national GIS research conference for the UK, Manchester

12 Wood, J., J. Dykes, et al. (2007). "Interactive visual exploration of a large spatio-temporal dataset: reflections on a geovisualization mashup." IEEE Transactions on Visualization and Computer Graphics (Proceedings Visualization / Information Visualization 2007) 13(6): Biographies Jason Dykes and Jo Wood are senior lecturers in geographic information at the gicentre, City University London with interests in developing and using interactive graphics to explore complex structured data sets. Jo is particularly interested in analysing surfaces and hierarchies. He wrote LandSerf and LandScript through which the surfaces were generated and implemented the Treemap algorithm. Jason is particularly interested in using developing technologies and data to augment this activity. He did the database stuff, the SVG and KML bits and wrote the LandScript. Ross Purves and Alistair Edwardes work at the Department of Geography of the University of Zurich and have interests in geographic information retrieval and volunteered geographic information. They identified the scene types and descriptors and their research questions, analysis, ideas and insights inspired many of the techniques implemented here. All four of the above participated in heated discussion about the nature of the data and the graphics generated from them, hypothesized at length and learned a lot about Geograph and VGI.