Using Latent Semantic Analysis to Identify Research Trends in OpenStreetMap

Abstract

OpenStreetMap (OSM), based on collaborative mapping, has become a subject of great interest to the academic community, resulting in a considerable body of literature produced by many researchers. In this paper, we use Latent Semantic Analysis (LSA) to help identify the emerging research trends in OSM. An extensive corpus of 485 academic abstracts of papers published during the period 2007–2016 was used. Five core research areas and fifty research trends were identified in this study. In addition, potential future research directions have been provided to aid geospatial information scientists, technologists and researchers in undertaking future OSM research.

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• https://www.mdpi.com/2220-9964/6/7/195/pdf under the license https://creativecommons.org/licenses/by

• http://www.mdpi.com/2220-9964/6/7/195/pdf,

http://dx.doi.org/10.3390/ijgi6070195 under the license cc-by

• https://www.mdpi.com/2220-9964/6/7/195,

https://doaj.org/toc/2220-9964 under the license https://creativecommons.org/licenses/by/4.0/

• https://www.mdpi.com/2220-9964/6/7/195,

https://www.mdpi.com/2220-9964/6/7/195/pdf,

https://dblp.uni-trier.de/db/journals/ijgi/ijgi6.html#SehraSR17,

https://doi.org/10.3390/ijgi6070195,

http://www.mdpi.com/2220-9964/6/7/195,

https://core.ac.uk/display/88461607,

https://academic.microsoft.com/#/detail/2733759049