An Exploration of Wikipedia Data as a Measure of Regional Knowledge Distribution

  • Fabian StephanyEmail author
  • Fabian Braesemann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10540)


In today’s economies, knowledge is the key ingredient for prosperity. However, it is hard to measure this intangible asset appropriately. Standard economic models mostly rely on common measures such as enrollment rates and international test scores. However, these proxies focus rather on the quality of education of pupils than on the distribution of knowledge among the whole population, which is increasingly defined by alternative sources of education such as online learning platforms. As a consequence, the economically relevant stock of knowledge in a region is only roughly approximated. Furthermore, they are abstract in content, and both capital-, and time-consuming in census. This paper proposes to explore Wikipedia data as an alternative source of capturing the knowledge distribution on a narrow geographical scale. Wikipedia is by far the largest digital encyclopedia worldwide and provides data on usage and editing publicly. We compare Wikipedia usage worldwide and edits in the U.S. to existing measures of the acquisition and stock of knowledge. The results indicate that there is a significant correlation between Wikipedia interactions and knowledge approximations on different geographical scales. Considering these results, it seems promising to further explore Wikipedia data to develop a reliable, inexpensive, and real-time proxy of knowledge distribution around the world.


Mining of big social data Wikipedia Knowledge geographies 

JEL Classification

C 55 C 82 I 21 



The authors are thankful for the feedback this work received on the brown-bag seminar at the Oxford Internet Institute and the SIS Statistics and Data Science conference in Florence, both taken place in June 2017. Particularly helpful comments have been made by Scott Hale, Otto Kässi, and Taha Yasseri.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Vienna University of Economics and BusinessViennaAustria
  2. 2.Oxford Internet InstituteUniversity of OxfordOxfordUK

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