Gazetteers are dictionaries of geographic place-names that have important implications far beyond the worlds of geographers and cartographers. By containing ‘definitive’ lists of places, gazetteers have the ontological power to define what will and will not be geocoded and represented in databases, maps, search engines, and ultimately our spatial understandings of place. This paper focuses attention on GeoNames, which is the world's largest freely available and widely used gazetteer. We illustrate how content in GeoNames is characterised by highly uneven spatial distributions. There are dense clusters of place-names in some parts of the world and a relative absence of geographic content in others. These patterns are related not just to the wealth and population size of a country, but also to its policies on Internet access and open data. The paper then traces some of the specific implications of this information inequality: showing how biases in gazetteers are propagated in a variety of geographic meaning-making.
Ahlers D, 2013, “Assessment of the accuracy of GeoNames gazetteer data” Proceedings of the 7th Workshop on Geographic Information Retrieval (Association for Computing Machinery, New York) pp 74–81
2.
BatesD (1985) A Bibliography of Domesday Book, Woodbridge, Suffolk: Boydell
3.
BizerCHeathTBerners-LeeT (2009) “Linked data—the story so far.”. International Journal on Semantic Web and Information Systems5(3): 1–22
4.
BowkerG C (2013) “Data flakes: an afterword to ‘raw data’ is an oxymoron”. Raw Data is an Oxymoron, Cambridge, MA: MIT Press, pp. 167–171
5.
Brunner T, Purves R S, Weibel R, 2008, “Geographic information tetrieval: Identifikation der geographischen Lage von Zeitungsartikeln”, master's thesis, Geographisches Institut der Universität Zürich
6.
Budhathoki N R, Chhatkuli R R, 2004, “Building geographic information infrastructure at national level: Nepalese experience”, paper presented at GSDI 7th Conference, Bangalore, http://www.gsdidocs.org/gsdiconf/GSDI-7/papers/Nlnrb.pdf
7.
BuscaldiD (2011) “Approaches to disambiguating toponyms”. SIGSPATIAL Special3(2): 16–19
8.
ElwoodSGoodchildM FSuiD Z (2012) “Researching volunteered geographic information: spatial data, geographic research, and new social practice”. Annals of the Association of American Geographers102: 571–590
9.
GoodchildM F (2007) “Citizens as sensors: the world of volunteered geography”. GeoJournal69(4): 211–221
10.
GoodchildM FHillL L (2008) “Introduction to digital gazetteer research”. International Journal of Geographical Information Science22: 1039–1044
11.
GrahamM (2011a) “Cloud collaboration: peer-production and the engineering of the Internet”. In: BrunnS (eds) Engineering Earth, New York: Springer, pp. 67–83
12.
GrahamM (2011b) “Wiki space: palimpsests and the politics of exclusion”. In: LovinkGTkaczN (eds) Critical Point of View: A Wikipedia Reader, Amsterdam: Institute of Network Cultures, pp. 269–282
13.
GrahamM (2013) “The virtual dimension”. In: AcutoMSteeleW (eds) Global City Challenges: Debating a Concept, Improving the Practice, London: Palgrave, pp. 117–139
14.
GrahamM (2014) “Internet geographies: data shadows and digital divisions of labour”. In: GrahamMDuttonW H (eds) Society and the Internet: How Networks of Information and Communication are Changing our Lives, Oxford: Oxford University Press, pp. 99–116
15.
GrahamMSheltonT (2013) “Geography and the future of big data; big data and the future of geography”. Dialogues in Human Geography3: 255–261
16.
Graham M, Hale S, Stephens M, 2013, “Geographies of the world's knowledge”, Ed. C M Flick (Convoco! Edition, London)
17.
Graham M, Hogan B, Straumann R K, Medhat A, 2015, “Uneven geographies of user-generated information: patterns of increasing informational poverty” Annals of the Association of American Geographers (forthcoming)
18.
HaklayM (2010) “How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance Survey datasets”. Environment and Planning B: Planning and Design37: 682–703
19.
Hecht B, Gergle D, 2009, “Measuring self-focus bias in community-maintained knowledge repositories”, in Proceedings of the 2009 International Conference on Communities and Technologies (Association for Computing Machinery, New York) pp 11–19
20.
Kitchin R, 2014, The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences (Sage, London)
21.
LeidnerJ LLiebermanM D (2011) “Detecting geographical references in the form of place names and associated spatial natural language”. SIGSPATIAL Special3(2): 5–11
22.
Lemmens R, Keßler C, 2014, “Geo-information visualizations of linked data”, in Proceedings of the 17th AGILE Conference on Geographic Information Science (AGILE), http://carsten.io/Lemmens+Kessler-AGILE14.pdf
23.
Mayer-SchonbergerVCukierK (2013) Big Data: A Revolution That Will Transform How We Live, Work, and Think, New York: Eamon Dolan Books
24.
Ostermann F O, Spinsanti L, 2011, “A conceptual workflow for automatically assessing the quality of volunteered geographic information for crisis management.” Proceedings of the 10th AGILE Conference on Geographic Information Science (Association for Computing Machinery, New York)
25.
SchliederCHenrichA (2011) “Spatial grounding with vague place models”. SIGSPATIAL Special3(2): 20–23
26.
SEDAC, Center for International Earth Science Information Network Columbia University, Centro Internacional de Agricultura Tropical, 2014 Gridded Population of the World, Version 3 (GPWv3): Population Density Grid NASA Socioeconomic Data and Applications Center (SEDAC), Palisades, NY, https://dx-doi-org.web.bisu.edu.cn/10.7927/H4XK8CG2
27.
TarnW W (1923) “Alexander and the Ganges”. The Journal of Hellenic Studies43(2): 93–101
28.
WilsonP (2003) Sacred Signs: Hieroglyphs in Ancient Egypt, Oxford: Oxford University Press
29.
ZookMGrahamMSheltonTGormanS (2010) “Volunteered geographic information and crowdsourcing disaster relief: a case study of the Haitian earthquake”. World Medical and Health Policy2(2): 7–33
