Abstract
Data quality measures commonly used to assess the credibility and correctness of spatial data are generally lacking in volunteered geographic information (VGI) environments particularly in developing countries, prevalent with an absence of reference data. This challenge is further exacerbated by the dynamic nature of VGI which makes traditional approaches to data quality inappropriate to establish the quality of VGI. As a result, these have motivated researchers in the geographic community to investigate alternative measures of determining the quality of contributed datasets in VGI environments. These innovative, community-based collaborative measures use trust matrices, reputation, and data provenance to establish the quality of contributed datasets in VGI platforms. This study proposes the use of human computation (HC) methods, through data provenance and aggregation algorithms to establish the semantic accuracy of VGI from multiple contributors. Thus, semantic accuracy determination here is concerned with the aggregation of multiple records of the same entity from different contributors to define its final label and improve semantic rigor. The methodology was implemented successfully in a case study in Botswana to demonstrate its effectiveness in establishing the semantic accuracy of VGI.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Antoniou V, Skopeliti A (2015) Measures and indicators of vGI quality: an overview. ISPRS Annals of the Photogrammetry, Remotre Sensing and Spatial Information Sciences 2(3):345–351
Baba, Y. (2018, July, 2018). Statistical Quality Control for Human Computation and Crowdsourcing. Paper presented at the Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, (IJCAI-18), Stockholm, Sweden.
Ballatore A, Bertolotto M, Wilson D (2013) Computing the Semantic similarity of geographic terms using volunteered lexical definitions. Int J Geogr Inf Sci 27(10):2099–2118
Barzilay, R. (2003). Information fusion for multidocument summarization. (PhD), Columbia University.
Celino I (2013) Human computation VGI provenance: semantic web-based representation and publishing. IEEE Trans Geosci Remote Sens 51(11):5137–5143
Comber A, See L, Fritz S, Van der Velde M, Perger C, Foody GM (2013) Using control data to determine the reliability of volunteered geographic information about land cover. Int J Appl Earth Obs Geoinf 23(1):37–48
DiFranzo, D., & Hendler, J. (2013). The semantic web and the next generation of human computation. In M. P. (Ed.), Handbook of Human Computation. New York, NY: Springer.
Durbha S, King R, Shah V, Younan N (2009) A framework for semantic reconciliation of disparate earth observation thematic data. Comput Geosci 35(4):761–773
Fairbairn D, Al-Bakri M (2013) Using geometric properties to evaluate possible integration of authoritative and volunteered geographic information. Int J Geogr Inf Sci 2:349–370
Feng, D., Besana, S., Boydston, K., & Christian, G. (2010, October 4). Towards high-quality data extraction via crowdsourcing. Paper presented at the CrowdConf 2010, San Francisco, CA.
FGDC. (1998). Content standard for digitial geospatial metadata. In F. G. D. C. S. Metadata Ad Hoc Working Group (Ed.), (Vol. FGDC-STD-001–1998, pp. 1–90). 590 National Center, Reston, Virginia 20192.
Foody GM, See LSF, van der Velde M, Perger C, Schill C, Comber A (2015) Accurate attribute mapping from volunteered geographic information: issues of volunteer quantity and quality. The Cartographic Journal 52(4):336–344
Haklay M, Basiouka S, Antoniou V, Ather A (2010) How Many Volunteers Does It Take To Map An Area Well? The validity of Linus’ law to Volunteered Geographic Information. Cartogr J 47(4):315–322
Haklay M, Singleto A, Parker C (2008) Web Mapping 2.0: The neogeography of the geoweb. Geogr Compass 3:2011–2039
Heipke C (2010) Crowdsourcing geospatial data. ISPRS J Photogramm Remote Sens 65(6):550–557
LaGro JA (2005) LAND-USE CLASSIFICATION. In: Hillel D (ed) Encyclopedia of Soils in the Environment. Elsevier, Oxford, pp 321–328
Law, E., & von Ahn, L. (2011a). Human Computation: Morgan and Claypool Publishers.
Law, E., & von Ahn, L. (2011b). Human Computation (Synthesis Lectures on Artificial Intelligence and Machine Learning). San Rafael, CA, USA: Morgan & Claypool.
Manning CD, Schutze H (1999) Foundations of statistical natural language processing. Cambridge, Mass, London
Moreri KK (2020) Using Kappa methodology to consider volunteered geographic information in official land administration systems in developing countries. Spat Inf Res 28(3):299–311. https://doi.org/10.1007/s41324-019-00289-7
Moreri KK, Fairbairn D, James P (2018a) Volunteered geographic information quality assessment using trust and reputation modelling in land administration systems in developing countries. Int J Geogr Inf Sci 32(5):931–959
Moreri KK, Fairbairn D, James P (2018b) Issues in developing a fit for purpose system for incorporating VGI in land administration in Botswana. Land Use Policy 77:402–411
Osterman, F. O., & Spinsanti, L. (2011). A conceptual workflow for automatically assessing the quality of volunteered geographic information for crisis management. Paper presented at the Proceedings of the AGILE 2011, Utrecht, Netherlands.
Rahmatizadeh S, Rajabifard A, Kalantari M (2016) A conceptual framework for utilising VGI in land administration. Land Use Policy 56:81–89
Ramos, J., Vandecasteele, A., & Devillers, R. (2013). Semantic integration of authoritative and volunteered geographic information (VGI) using ontologies. Paper presented at the 16th AGILE Conference on Geographic Information Science, Leuven, Belgium.
Ronzhin, S. (2015). Semantic enrichment of volunteered geographic information using linked data: a use case scenario for disaster management. (Unpublished MSc Thesis), University of Twente, Enschede, Netherlands.
Sabou M, Aroyo L, Bontcheva K, Bozzon A, Qarout R (2018) Semantic web and human computation: the status of an emerging field. Semantic Web 9:291–302. https://doi.org/10.3233/SW-180292
Schall, D., Truong, H., & Dustdar, S. (2008, July 21–14, 2008). The human provided services framework. Paper presented at the 10th IEEE International Conference on E-Commerce Technology (CEC 2008), Washington, DC, USA.
Shih-Wen, H., & Wai-Tat, F. (2013). Enhancing reliability using peer consistency evaluation in human computation. Paper presented at the Conference on Computer Supported Collaborative Work, San Antonio, Texas, USA.
Snow, R., O’Connor, B., Jurafsky, D., & Ng, A. Y. (2008). Cheap and fast – but is it good? Evaluating non-expert annotations for natural language tasks. Paper presented at the Conference on Empirical Methods in Natural language Processing, Hawaii, USA
Stasch, C., Schade, S., Llaves, A., Janowicz, K., & Bröring, A. (2011, 23 October). Aggregating linked sensor data. Paper presented at the 4th International Workshop on Semantics Sensor Networks, Bonn, Germany.
Tanasescu V, Gugliotta A, Domingue J, Villarias L, Davies R, Rowlatt M, Stincic S (2007) Geospatial data integration with semantic web services: the emerges approach. In: Scharl A, Tochtermann K (eds) The Geospatial Web. Springer, London, pp 247–256
Von Ahn L (2006) Games with a purpose. Computer 39(6):92–94
Yuen, M., Chen, L., & King, I. (2009, August 29–31). A survey of human computation systems. Paper presented at the CSE'09. International Conference on Computational Science and Engineering, Vancouver, Canada.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The author declares no conflict of interest.
Rights and permissions
About this article
Cite this article
Moreri, K.K. Semantic accuracy determination of VGI using human computation: Botswana case study. Appl Geomat 13, 877–884 (2021). https://doi.org/10.1007/s12518-021-00399-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12518-021-00399-8