- freely available
ISPRS Int. J. Geo-Inf. 2016, 5(12), 232; doi:10.3390/ijgi5120232
1. Introduction and Problem Statement
2. Related Work
3. Methodology and Experimental Setup
- Selection of OSM tags for analysis (Section 3.1)
- Import of raw OSM data for each of the selected cities or regions (Section 3.2)
- Analysis of the raw OSM data where the patterns of tagging for each of the selected tags are extracted for each of the selected cities or regions (Section 3.3)
- Final statistical analysis of the output of step 3. This shall be explored in detail in Section 4
3.1. Selection of OSM Tags for Analysis
- The selected tag must have a dedicated Map Features Wiki page . This ensures that the tag is appropriate for our analysis and has OSM community support for its inclusion in OSM. Information about actual usage of the tag in mapping within OSM is provided on each dedicated Map Features Wiki page, which can help us to interpret our results for each selected tag.
- The selected tag must have at least two suggested tags offered as “Useful Combinations”. If there are not useful combinations with other tags suggested, then this tag is not considered.
- The value assigned to the key in the selected tag must not be “yes” or “no”. We strictly consider only tags where the key can have some value and does not only state the presence of the attribute described using that same key. While our methodology can easily support tags where the key can have only “yes” or “no” values, such tags can be found in multiple types of features, and it will be more difficult to consider their analysis.
- The selected tag is not listed as a suggested tag for any of the other selected tags. This ensures that we choose tags with higher importance for our analysis.
3.2. Import of Raw OSM Data for Each of the Selected Cities or Regions
3.3. Analysis of the Raw OSM Data
4. Experimental Analysis
4.1. Example of Compliance with Suggested Tags in OSM Map Features Wiki Page
4.2. Compliance for Target Tag highway=residential
- The name=* tag has excellent compliance in almost half of the cities analysed. One reason for this is that in many cases the name=* tag may have local variations to accommodate the official native language(s) for the corresponding city, such as name:fi for the Finnish name of a street in Helsinki.
- The overall compliance of oneway=* is not good in all of the cities. We believe this might result from confusion about how to apply the tag correctly. The OSM Map Features page indicates that it should only be used when the street object with the highway=residential tag is actually a one-way street (oneway=yes). If traffic flow movement is bidirectional, then this suggested tag should not be used. However, we feel that it would be interesting to check how many one-way streets are missing this tag. This task would require on-the-ground field verifiability, as this information is not always possible to extract correctly from aerial imagery.
- The only city that has at least AVERAGE compliance of the oneway key is the city of Milan, which is clearly noticeable by only looking at the OSM map of Milan.
4.3. Compliance for Target Tag natural=tree
- Compliance for this tag is low because the majority of cities have only small numbers of objects tagged or attributed as single or lone trees. Proper mapping of these features requires field verifiability and special domain knowledge, and usually a lot of time and effort.
- Very high compliance can be seen in the city of Vienna across all the keys, except for the key genus. In a few other cities, only some of the keys are used. For example, Bucharest has only high compliance for the keys height and species, while the other keys are not used at all. San Francisco shows high compliance of the taxon key, used with 3537 mapped trees.
- Vienna was subject to an open data import of trees, and contains over trees.
- In summary, only in the cities Bucharest, Duesseldorf, London, Lyon, Vienna, San Francisco, and Warsaw is there any real usage of the suggested tag key combinations for the target tag natural=tree.
4.4. Compliance for Target Tag highway=footway
- The OSM Map Features Wiki page  does indicate that there are different customs, rules, and signs in use in different countries for several kinds of ways available to pedestrian, bicycle, and horse users. Tagging them varied even before the introduction of highway=path, and has led to different conceptions of what each of the current tags actually mean.
- None of the cities in our analysis display EXCELLENT or GOOD compliance for any of the suggested tags.
- The low compliance of the keys is mostly coming from the need for field verifiability in order to properly map the suggested tags. The best compliance relates to the tag key surface, which is still low, but most probably only because mappers know the surface of footways in their neighborhood or can determine them from the aerial imagery.
- It is noticeable that most of the objects have poor compliance for the name key, which is expected since footways do not usually have proper naming. Only the city of Bogotá has higher compliance for the name key, since some the streets are actually being mapped using highway=footway.
- The footway key has low compliance, but its usual values are either sidewalk or crossing, which should not be applied to all objects tagged with highway=footway.
4.5. Compliance for Target Tag highway=path
- None of the cities in our analysis display EXCELLENT or GOOD compliance for any of the suggested tags.
- sac_scale and trail_visibility are part of a classification scheme for hiking trails regarding trail difficulty and trail visibility/orientation, respectively. These tag keys draw their values on the Klassifikation des Swiss Alpine Club (SAC) (de), since there is no internationally standardized classification schema. Only Oslo (trail_visibility) and Christchurch (sac_scale) display FAIR compliance for these two tag keys. The domain-specific nature of these tag keys might be difficult for non specialists to understand and subsequently apply as tags.
- Boston, Christchurch, Frankfurt, Johannesburg, Milan, Nis, and Sydney have AVERAGE compliance for the surface key. We were not able to find any relation among these cities in this context, which points out that mapping of the surface in these situations may be down to the tagging practice of the local communities.
4.6. Compliance for Target Tag highway=tertiary
- Buenos Aires, Lagos, Madrid, Mexico City, Sao Paulo, and Vancouver have GOOD compliance for the oneway key, which indicates that if this tag has been correctly applied, the majority of the highway=tertiary objects in these cities are actually one-way roads. Since 16 cities have FAIR compliance with oneway while 12 cities have AVERAGE compliance for this key, we believe that the objects with tag highway=tertiary are the result of different local tagging practices in these cities.
- Only Ottawa and Boston score EXCELLENT for lanes=*. We think that this is because tertiary roads in the USA and Canada usually have more than one lane. This does not typically happen in other parts of the world or in Europe for instance.
- Only Lyon and Prague have at least GOOD compliance.
- New Delhi is the only city to score POOR on the name=*
4.7. Compliance for Target Tag amenity=parking
- From the data, it can be seen that fee key is usually applied with values yes or no, while these tags are rarely applied. We believe that this omission does not mean that parking fees do not apply, but probably requires a mapper to posses local knowledge.
- For the tag amenity=parking, none of our 40 cities display EXCELLENT compliance for any of the suggested tags.
- The name=* tag is surprisingly under-used. Singapore shows AVERAGE compliance of use of this suggested tag, while Dublin and Kyoto show FAIR compliance. Based on this, it would appear that OSM contributors have difficulty in providing names for car parking spaces.
- The higher availability of parking-related data could help for better navigation and automatic parking suggestions based on the parameters of the stay and payment options, based on the operator. Even for residential parking places, information about operator or the lack of it could indicate availability for visitor parking. While mapping maxstay or operator tags can require field verifiability, some authors have suggested mapping these directly from online imagery such as Mapillary ()
4.8. Compliance for Target Tag highway=primary
- The only two cities that have FAIR compliance for the name key are Bamako and Lagos. If we closely check data in Bamako, we can conclude that the reason for this low compliance of the name key comes from the mapping practice where highway=primary objects are actually made out of multiple disjoint polylines, out of which only some are tagged with name tag. This can be deduced from the values of the ref tag.
- The high difference between the level of compliance of the keys co-occurring with the highway=primary tag, as well as the number of different tags applied and the number of objects—compared to the city size—suggests that very different tagging practices exist throughout the world for this particular tag. Since these objects are usually mapped in the beginning of the mapping in urban areas , it might be expected that these features are mapped in various stages of the OSM development. Therefore, observed practices are very heterogeneous, since some of the data were probably mapped before the OSM community agreed on the approach for tagging these objects. Given the importance of these objects and their possible usage in various applications highway=primary, tags could be revisited to check their tagging and annotation.
- Cities with POOR compliance for lanes=* include Oslo, Lyon, Manchester, Dublin, and Mexico City. The cities which display POOR compliance with the ref=* tag key include Astana, Bogotá, Nairobi, Sao Paolo, Singapore, and Sydney.
4.9. Compliance for Target Tag highway=bus-stop
- Only three cities have POOR compliance with use of the name=* tag, and more than half of the cities have EXCELLENT compliance with the use of this tag. The cities that have POOR compliance of this tag are Christchurch, Monrovia, and Sao-Paulo.
- The suggested tags operator=* and public_transport are not well used, with only 6 cities showing EXCELLENT compliance with usage of these tags with highway=bus_stop, namely: Beijing, Boston, Chicago, Madrid, Ottawa, and Vienna.
- The operator key has POOR compliance in 28 cities, but, based on the explanation on empirical evidence, this might be because the bus stops are operated by one common public or private company. In such cases, the OSM Map Features Wiki pages indicate that only transport operators that are not common to that urban area should be mapped.
- The bus_stop name mapping requires field verifiability and local knowledge, unless there is a detailed open-licensed transportation map from which this information can be derived.
4.10. Compliance for Target Tag railway=rail
- Key tunnel has POOR compliance, while the key bridge has POOR compliance in most the cities. This does not mean that the overall compliance is actually POOR, since the omission of these tags actually means that particular part of the rail is not on a bridge nor inside of a tunnel.
- The POOR compliance of the gauge key in some cities may come from the lack of domain knowledge from the mappers. Usually, gauge size does not change across the urban area or even a country, so there is the potential that the gauge tag could be updated automatically, based on the standard gauge in use.
- The gauge=* tag is POOR in Beijing, Bucharest, Mexico, and Ottawa, while Sydney is the only city to score EXCELLENT on voltage=* and frequency=*.
- Similar commentary can be given for the other domain-specific keys (such as frequency or voltage) that can be applied based on existing tag values or by applying the values that are country or regional standards.
4.11. Compliance for Target Tag leisure=pitch
- The surface tag is hardly used at all, and all cities show POOR compliance. The surface key can take values of grass, earth, astro-turf, asphalt, etc.
- The sport tag shows very good application in over 30 of the cities. This tag will indicate which sport is played on this pitch. We speculate that the reason for the high compliance for this tag key arises from the ability to derive the sport played on a pitch from aerial imagery, as the markings on the surface would indicate the sport played there.
- The sport=* tag is POOR in Astana and Kyoto.
- The sport=* tag is AVERAGE in Beijing, Helsinki, Kyiv, Vilnius, Saint Petersberg, Oslo, and Warsaw.
4.12. Overall Summary of Results Tables
5. Conclusions and Future Work
- What is the influence on tagging patterns from major contributors to OSM in cities and urban areas? In work by [2,17,40] and others, we see that in OSM, there is a small percentage of all contributors (between and ) who perform almost all (between and ) of the tagging in the OSM database. Future work will identify these major contributors in a selection of cities and urban areas. By considering the edits from these major contributors, it would be very useful to analyse what tagging patterns or structures they are using. Are they following advice from the OSM Map Features pages? Are they using the default tags as suggested by OSM editing software (such as JOSM and the web-based iD editor)? Perhaps they are tagging objects based on their own conceptual idea of how a particular object in a given geographical context should be tagged and annotated?
- Using a cluster-based analysis, could we detect and identify emerging regional-based tagging practices. With the OSM Map Features Wiki pages, there is an idea that all objects in OSM should and could be annotated and tagged in a homogeneous fashion. There is flexibility in the tagging structure in OSM to allow for local variations based on language and alphabets used and cultural influences. This future work could help to identify if specific objects are tagged differently in different regions of the world. For example, are parking areas (amenity=parking) tagged and annotated differently in Europe in comparison to North America?
- In some of our previous work [33,41], we performed an analysis of the OSM History data. In future work from this paper, we will investigate if it is possible to detect the historical evolution of tagging patterns or practices over time, using the OSM History data. If on a regional scale some key was specifically co-occurring with some specific tag, could we conclude what induced such behaviour? Was it a preference tag of some major influencer (mapper with high contribution to OSM), a newly agreed key within the OSM community, or a new feature like “electric car plug”? If there is a possibility to detect such patterns, could it influence changes of the “useful combinations” section of OSM Map Features Wiki page or even induce the development of regional variations of “useful combinations” sections?
- At the conclusion of this work, we can say that the quantitative analysis of potential spatial variation in the compliance with suggested tags and/or combinations of tags is a difficult problem. While the results in this paper indicate that there is noticeable variance in the overall compliance with suggested tags and/or combinations of tags, we were not able to quantitatively indicate any distinctive patterns. There are a number of possible pathways to further investigation for this problem. In , the authors considered comparing social tags and subject terms in the domain of information science between Chinese and English sources. The authors used traditional methods such as the Jaccard similarity coefficient and the Spearman correlation coefficients of the two ranked sets to compare these tag sets. In , the authors analysed the application of over 1200 tags on StackOverflow for different topics and conversations. The authors evaluated the performance of their method using the standard information retrieval measures of precision, recall, and F (F-score or F-measure). In works by [44,45], the authors use an overlap coefficient lexical similarity measure between sets of terms of tags. The overlap coefficient is a metric that describes how much of the smaller of the vocabulary is included in the larger and is not sensitive to the relative sizes of the two vocabularies. An earlier work  suggests that for the analysis of tagsets, one should consider splitting up the sets into smaller chunks for a more fine-grained analysis. In , the authors propose six dimensions of conceptual quality for VGI. The dimension compliance is the most applicable to our work here, where this dimension considers the adherence of an attribute, feature, or set of features to some given source. is easily calculated, and can be implemented into our analysis software. We believe that future work that considers these approaches may be able to provide quantitative evidence of differences in compliance between different cities and regions.
- Analysis of the correctness of tags used in combination with target tags is required. In Section 4, we provided the results of the testing of usage or compliance of cities with suggested tag key combinations with a given target tag. An object in a city is deemed compliant if a given target tag is accompanied by a suggested tag key. In this paper, we do not check or analyse the value of the suggested tag keys. Our methodology is flexible and will allow us to integrate this type of checking in future work. Such checking of the correctness or validity of the values assigned to suggested tag keys could allow us to make more informed statements about the quality of the tagging on objects for specific target tags. The correctness of tag application must also be considered within the applicability of the Map Features Wiki suggestions for each city. We did not consider these variations in this work. Each city or region may exhibit differences in how applicable a given suggestion is. For example, sac:scale or mtb:scale for the highway=path may not always be generally applicable to every urban area. These local variations must be taken into consideration in any assessment of overall tag compliance. A further suggestion here would be to consider how this type of checking could be performed efficiently in real-time, given that OSM can legitimately be considered as geographic Big Data . Our approach outlined in this paper has been developed as an offline process. The implementation of our methodology as a component of an online stream processing engine  will require additional software development.
Conflicts of Interest
- See, L.; Mooney, P.; Foody, G.; Bastin, L.; Comber, A.; Estima, J.; Fritz, S.; Kerle, N.; Jiang, B.; Laakso, M.; et al. Crowdsourcing, citizen science or volunteered geographic information? The current state of crowdsourced geographic information. ISPRS Int. J. Geo-Inf. 2016, 5, 55. [Google Scholar] [CrossRef]
- Neis, P.; Zielstra, D. Recent developments and future trends in volunteered geographic information research: The case of OpenStreetMap. Future Int. 2014, 6, 76–106. [Google Scholar] [CrossRef]
- Barron, C.; Neis, P.; Zipf, A. A comprehensive framework for intrinsic OpenStreetMap quality analysis. Trans. GIS 2013. [Google Scholar] [CrossRef]
- Ballatore, A.; Mooney, P. Conceptualising the geographic world: The dimensions of negotiation in crowdsourced cartography. Int. J. Geogr. Inf. Sci. 2015, 29, 2310–2327. [Google Scholar] [CrossRef]
- Ballatore, A.; Zipf, A. A conceptual quality framework for Volunteered Geographic Information. In Spatial Information Theory; Springer International Publishing: New York, NY, USA, 2015; pp. 89–107. [Google Scholar]
- Mooney, P.; Corcoran, P. The annotation process in OpenStreetMap. Trans. GIS 2012, 16, 561–579. [Google Scholar] [CrossRef]
- OpenStreetMapWiki. The OpenStreetMap Map Features Wiki. Available online: http://wiki.openstreetmap.org/wiki/Map_Features (accessed on 29 August 2016).
- TagInfo. TagInfo: An OSM Service to Showing Statistics about which Tags are Actually in the OSM Database. Available online: https://taginfo.openstreetmap.org/ (accessed on 29 August 2016).
- Strohmaier, M.; Körner, C.; Kern, R. Understanding why users tag: A survey of tagging motivation literature and results from an empirical study. Web Semant. 2012, 17, 1–11. [Google Scholar] [CrossRef] [PubMed]
- Golder, S.A.; Huberman, B.A. Usage patterns of collaborative tagging systems. J. Inform. Sci. 2006, 32, 198–208. [Google Scholar] [CrossRef]
- Špiranec, S.; Ivanjko, T. Experts vs. novices tagging behavior: An exploratory analysis. Procedia Soc. Behav. Sci. 2013, 73, 456–459. [Google Scholar] [CrossRef]
- Comito, C.; Falcone, D.; Talia, D. Mining human mobility patterns from social geo-tagged data. Pervasive Mob. Comput. 2016, in press. [Google Scholar] [CrossRef]
- Simon, R.; Haslhofer, B.; Jung, J. Annotations, tags and linked data. Metadata enrichment in online map collections through Volunteer-Contributed Information. e-Perimetron 2011, 6, 129–137. [Google Scholar]
- Google Inc. Google Map Maker. Available online: https://www.google.com/mapmaker/ (accessed on 29 August 2016).
- Johnson, B.A.; Iizuka, K. Integrating OpenStreetMap crowdsourced data and Landsat time-series imagery for rapid land use/land cover (LULC) mapping: Case study of the Laguna de Bay area of the Philippines. Appl. Geogr. 2016, 67, 140–149. [Google Scholar] [CrossRef]
- Arsanjani, J.J.; Zipf, A.; Mooney, P.; Helbich, M. An introduction to OpenStreetMap in geographic information science: Experiences, research, and applications. In OpenStreetMap in GIScience; Arsanjani, J.J., Zipf, A., Mooney, P., Helbich, M., Eds.; Springer International Publishing: New York, NY, USA, 2015; pp. 1–15. [Google Scholar]
- Mooney, P.; Corcoran, P. Characteristics of heavily edited objects in OpenStreetMap. Future Int. 2012, 4, 285–305. [Google Scholar] [CrossRef]
- Juhasz, L.; Hochmair, H. Cross linkage between mapillary street level photos and OSM edits. In Geospatial Data in a Changing World: Selected Papers of the 19th AGILE Conference on Geographic Information Science; Sarjakoski, T., Santos, M.Y., Sarjakoski, T., Eds.; Springer: Helsinki, Finland, 2016; pp. 141–156. [Google Scholar]
- Quinn, S.; Yapa, L. OpenStreetMap and food security: A case study in the city of Philadelphia. Prof. Geogr. 2016, 68, 271–280. [Google Scholar] [CrossRef]
- El-Safty, A.; Schmitz, B.; Ertl, T. An OpenStreetMap editing interface for visually impaired users based on Geo-semantic information. In Computers Helping People with Special Needs; Miesenberger, K., Fels, D., Archambault, D., Peňáz, P., Zagler, W., Eds.; Springer International Publishing: New York, NY, USA, 2014; pp. 116–119. [Google Scholar]
- Wolfensberger, M.; Richter, K.F. A mobile application for a user-generated collection of Landmarks. In Web and Wireless Geographical Information Systems; Gensel, J., Tomko, M., Eds.; Springer International Publishing: New York, NY, USA, 2015; pp. 3–19. [Google Scholar]
- Neis, P.; Goetz, M.; Zipf, A. Towards automatic vandalism detection in OpenStreetMap. ISPRS Int. J. Geo-Inf. 2012, 1, 315–332. [Google Scholar] [CrossRef]
- Feick, R.; Robertson, C. A multi-scale approach to exploring urban places in geotagged photographs. Comput. Environ. Urban Syst. 2015, 53, 96–109. [Google Scholar] [CrossRef]
- TagInfo - The List of the Most Common Tags in the Database. Available online: https://taginfo.openstreetmap.org/tags (accessed on 1 December 2016).
- MapZen - Extracts of OpenStreetMap Data. Available online: https://mapzen.com/data/metro-extracts/ (accessed on 29 August 2016).
- osm2pgsql is a Command-Line Based Program that Converts OpenStreetMap Data to postGIS-enabled PostgreSQL Databases. Available online: http://wiki.openstreetmap.org/wiki/Osm2pgsql (accessed on 29 August 2016).
- OSM Wiki Page for Tag Amenity=Restaurant. Available online: http://wiki.openstreetmap.org/wiki/Tag:amenity=parking (accessed on 1 December 2016).
- Wikipedia. Likert Scale—Wikipedia, The Free Encyclopedia, 2016. Available online: https://en.wikipedia.org/wiki/Likertscale (accessed on 12 August 2016).
- Harpe, S.E. How to analyze Likert and other rating scale data. Curr. Pharm. Teach. Learn. 2015, 7, 836–850. [Google Scholar] [CrossRef]
- OSM Map Features. The OSM Map Features Wiki Page Leisure=Pitch, 2016. Available online: http://wiki.openstreetmap.org/wiki/Tag:leisure=pitch (accessed on 29 August 2016).
- OSM Wiki Page for Key Oneway. Available online: http://wiki.openstreetmap.org/wiki/Key:oneway (accessed on 1 December 2016).
- OpenStreetMapWiki. The Path Controversy. Available online: http://wiki.openstreetmap.org/wiki/Pathcontroversy (accessed on 29 August 2016).
- Corcoran, P.; Mooney, P.; Bertolotto, M. Analysing the growth of OpenStreetMap networks. Spat. Stat. 2013, 3, 21–32. [Google Scholar] [CrossRef]
- Mooney, P.; Minghini, M.; Stanley-Jones, F. Observations on an OpenStreetMap mapping party organised as a social event during an open source GIS conference. Int. J. Spat. Data Infrastruct. Res. 2015, 10, 138–150. [Google Scholar]
- Hecht, R.; Kunze, C.; Hahmann, S. Measuring completeness of building footprints in OpenStreetMap over space and time. ISPRS Int. J. Geo-Inf. 2013, 2, 1066–1091. [Google Scholar] [CrossRef]
- Kunze, C.; Hecht, R. Semantic enrichment of building data with volunteered geographic information to improve mappings of dwelling units and population. Comput. Environ. Urban Syst. 2015, 53, 4–18. [Google Scholar] [CrossRef]
- Zielstra, D.; Hochmair, H.H.; Neis, P.; Tonini, F. Areal delineation of home regions from contribution and editing patterns in OpenStreetMap. ISPRS Int. J. Geo-Inf. 2014, 3, 1211–1233. [Google Scholar] [CrossRef]
- Davidovic, N.; Mooney, P. Patterns of tagging in OpenStreetMap data in urban areas. In Proceedings of GISRUK 2016; Warboys, M., Wood, Z., Eds.; University of Greenwich: London, UK, 2016; pp. 30–35. Available online: http://www.cs.nuim.ie/~pmooney/websitePapers/GISRUK2016-patterns-tagging-openstreetmap.pdf (accessed on 29 August 2016).
- Jonietz, D.; Zipf, A. Defining fitness-for-use for crowdsourced points of interest (POI). ISPRS Int. J. Geo-Inf. 2016. [Google Scholar] [CrossRef]
- Gröchenig, S.; Brunauer, R.; Rehrl, K. Digging into the history of VGI data-sets: Results from a worldwide study on OpenStreetMap mapping activity. J. Locat. Based Serv. 2014, 8, 198–210. [Google Scholar] [CrossRef]
- Mooney, P.; Corcoran, P. Analysis of interaction and co-editing patterns amongst OpenStreetMap contributors. Trans. GIS 2014, 18, 633–659. [Google Scholar] [CrossRef]
- Wu, D.; He, D.; Qiu, J.; Lin, R.; Liu, Y. Comparing social tags with subject headings on annotating books: A study comparing the information science domain in English and Chinese. J. Inform. Sci. 2013, 39, 169–187. [Google Scholar] [CrossRef]
- Joorabchi, A.; English, M.; Mahdi, A.E. Automatic mapping of user tags to Wikipedia concepts: The case of a Q&A website—StackOverflow. J. Inform. Sci. 2015, 41, 570–583. [Google Scholar]
- Carman, M.J.; Baillie, M.; Gwadera, R.; Crestani, F. A Statistical Comparison of Tag and Query Logs. In Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’09), Boston, MA, USA, 19–23 July 2009; ACM: New York, NY, USA, 2009; pp. 123–130. [Google Scholar]
- Harvey, M.; Carman, M.; Elsweiler, D. Comparing tweets and tags for URLs. In Advances in Information Retrieval; Baeza-Yates, R., Vries, A.P.D., Zaragoza, H., Cambazoglu, B.B., Murdock, V., Lempel, R., Silvestri, F., Eds.; Springer: Berlin, Germany, 2012; pp. 73–84. [Google Scholar]
- Muller, M.J. Comparing tagging vocabularies among four enterprise tag-based services. In Proceedings of the 2007 International ACM Conference on Supporting Group Work (GROUP ’07), Sanibel Island, FL, USA, 4–7 November 2007.
- Graham, M.; Shelton, T. Geography and the future of big data, big data and the future of geography. Dialogues Hum. Geogr. 2013, 3, 255–261. [Google Scholar] [CrossRef]
- Stonebraker, M.; Çetintemel, U.; Zdonik, S. The 8 requirements of real-time stream processing. SIGMOD Rec. 2005, 34, 42–47. [Google Scholar] [CrossRef]
|Target Tag||TagInfo Ranking||Number of Objects|
|Report for Tag: leisure=pitch|
|Total number of objects: 470|
|Total number of different tags used: 26|
|Report for Tag: railway=rail|
|Total number of objects: 2922|
|Total number of different tags used: 30|
|City||Co-Occurring Tag Keys||Percentage of Objects|
|Vienna||denotation, species, species:de, taxon, tree:ref|
|Dusseldorf||denotation, genus, genus:de, object:street|
|London||leaf-cycle, leaf-type, note, species|
© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).