A framework for classification of volunteered geographic data based on user’s need

References

• Ali AL, Schmid F, Al-Salman R, Kauppinen T. 2014. Ambiguity and plausibility: managing classification quality in volunteered geographic information. Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems; Dallas, TX: ACM. p. 143–152.
   Google Scholar
• Ather A. 2009. A quality analysis of openstreetmap data [ME thesis]. London (UK): University College London.
   Google Scholar
• Ballatore A, Zipf A. 2015. A conceptual quality framework for volunteered geographic information. In: Fabrikant S, Raubal M, Bertolotto M, Davies C, Freundschuh S, Bell S, editors. Spatial information theory. COSIT 2015. Lecture Notes in Computer Science, Vol. 9368. Cham: Springer.
   Google Scholar
• Barron C, Neis P, Zipf A. 2014. A comprehensive framework for intrinsic OpenStreetMap quality analysis. Trans GIS. 18(6):877–895.
   Web of Science ®Google Scholar
• Beale R, Jackson T. 1990. Neural computing-an introduction. Boca Raton, FL: CRC Press.
   Google Scholar
• Bégin D, Devillers R, Roche S. 2013. Assessing volunteered geographic information (VGI) quality based on contributors’ mapping behaviours. ISPRS Int Arch Photogramm Remote Sens Spatial Inf Sci. XL-2/W1:149–154.
   Google Scholar
• Belongie S, Malik J, Puzicha J. 2002. Shape matching and object recognition using shape contexts. IEEE Trans Pattern Anal Mach Intell. 24(4):509–522.
   Web of Science ®Google Scholar
• Bishr M, Mantelas L. 2008. A trust and reputation model for filtering and classifying knowledge about urban growth. GeoJournal. 72(3-4):229–237.
   Google Scholar
• Brown M, Hua G, Winder S. 2011. Discriminative learning of local image descriptors. IEEE Trans Pattern Anal Mach Intell. 33(1):43–57.
   PubMed Web of Science ®Google Scholar
• Budhathoki NR, Haythornthwaite C. 2013. Motivation for open collaboration: crowd and community models and the case of OpenStreetMap. Am Behav Sci. 57(5):548–575.
   Web of Science ®Google Scholar
• Çamdevýren H, Demýr N, Kanik A, Keskýn S. 2005. Use of principal component scores in multiple linear regression models for prediction of chlorophyll-a in reservoirs. Ecol Modell. 181(4):581–589.
   Web of Science ®Google Scholar
• Ciepłuch B, Jacob R, Mooney P, Winstanley AC. 2010. Comparison of the accuracy of OpenStreetMap for Ireland with Google Maps and Bing Maps. Proceedings of the Ninth International Symposium on Spatial Accuracy Assessment in Natural Resuorces and Enviromental Sciences; July 20–23; University of Leicester, England.
   Google Scholar
• Coleman DJ, Georgiadou Y, Labonte J. 2009. Volunteered geographic information: the nature and motivation of produsers. Int J Spat Data Infrastruct Res. 4(1):332–358.
   Google Scholar
• Dunteman GH. 1989. Principal components analysis. Newbury Park, CA: Sage; p. 69.
   Google Scholar
• Elwood S. 2008. Volunteered geographic information: key questions, concepts and methods to guide emerging research and practice. GeoJournal. 72(3-4):133–135.
   Google Scholar
• Fast V, Rinner C. 2018. Toward a participatory VGI methodology: crowdsourcing information on regional food assets. Int J Geogr Inf Sci. 32(11):2209–2224.
   Web of Science ®Google Scholar
• Fischer MM, Gopal S. 1994. Artificial neural networks: a new approach to modeling interregional telecommunication flows. J Reg Sci. 34(4):503–527.
   Web of Science ®Google Scholar
• Flanagin AJ, Metzger MJ. 2008. The credibility of volunteered geographic information. GeoJournal. 72(3-4):137–148.
   Google Scholar
• Fogliaroni P, D’Antonio F, Clementini E. 2018. Data trustworthiness and user reputation as indicators of VGI quality. Geo Spat Inf Sci. 21(3):213–233.
   Google Scholar
• Gahegan M. 2003. Is inductive machine learning just another wild goose (or might it lay the golden egg)? Int J Geogr Inf Sci. 17(1):69–92.
   Web of Science ®Google Scholar
• Girres JF, Touya G. 2010. Quality assessment of the French OpenStreetMap dataset. Trans GIS. 14(4):435–459.
   Web of Science ®Google Scholar
• Goodchild MF. 2007. Citizens as sensors: the world of volunteered geography. GeoJournal. 69(4):211–221.
   Google Scholar
• Goodchild MF, Hunter GJ. 1997. A simple positional accuracy measure for linear features. Int J Geogr Inf Sci. 11(3):299–306.
   Web of Science ®Google Scholar
• Goodchild MF, Li L. 2012. Assuring the quality of volunteered geographic information. Spat Stat. 1:110–120.
   Web of Science ®Google Scholar
• Haklay M. 2010. How good is volunteered geographical information? A comparative study of OpenStreetMap and Ordnance Survey datasets. Environ Plann B Plann Des. 37(4):682–703.
   Web of Science ®Google Scholar
• Haklay M, Weber P. 2008. Openstreetmap: user-generated street maps. IEEE Pervasive Comput. 7(4):12–18.
   Web of Science ®Google Scholar
• Haklay MM, 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.
   Web of Science ®Google Scholar
• Haworth BT. 2018. Implications of volunteered geographic information for disaster management and GIScience: a more complex world of volunteered geography. Ann Am Assoc Geogr. 108(1):226–240.
   Web of Science ®Google Scholar
• Jayalakshmi T, Santhakumaran A. 2011. Statistical normalization and back propagation for classification. Int J Comput Theory Eng. 3(1):1793–8201.
   Google Scholar
• Jiang B, Liu X. 2012. Scaling of geographic space from the perspective of city and field blocks and using volunteered geographic information. Int J Geogr Inf Sci. 26(2):215–229.
   Web of Science ®Google Scholar
• Keßler C, De Groot RTA. 2013. Trust as a proxy measure for the quality of volunteered geographic information in the case of OpenStreetMap. In: Vandenbroucke D, Bucher B, Crompvoets J, editors. Geographic information science at the heart of Europe. Lecture Notes in Geoinformation and Cartography. Cham: Springer; p. 21–37.
   Google Scholar
• Kusumo A, Reckien D, Verplanke J. 2017. Utilising volunteered geographic information to assess resident’s flood evacuation shelters. Case study: Jakarta. Appl Geogr. 88:174–185.
   Web of Science ®Google Scholar
• Lowe DG. 2004. Distinctive image features from scale-invariant keypoints. Int J Comput Vis. 60(2):91–110.
   Web of Science ®Google Scholar
• Lu W, Wang W, Wang X, Xu Z, Leung A. 2003. Using improved neural network model to analyze RSP, NOx and NO2 levels in urban air in Mong Kok, Hong Kong. Environ Monit Assess. 87(3):235–254.
   PubMed Web of Science ®Google Scholar
• Maué P, Schade S. 2008. Quality of geographic information patchworks. Proceedings of 11th AGILE International Conference on Geographic Information Science, University of Girona, Spain.
   Google Scholar
• Mohammadi N, Malek M. 2015a. Artificial intelligence-based solution to estimate the spatial accuracy of volunteered geographic data. J Spat Sci. 60(1):119–135.
   Web of Science ®Google Scholar
• Mohammadi N, Malek M. 2015b. VGI and reference data correspondence based on location‐orientation rotary descriptor and segment matching. Trans GIS. 19(4):619–639.
   Web of Science ®Google Scholar
• Mooney P, Corcoran P. 2012. Characteristics of heavily edited objects in OpenStreetMap. Future Internet. 4(1):285–305.
   Google Scholar
• Moreri KK, Fairbairn D, James P. 2018. 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.
   Web of Science ®Google Scholar
• Mummidi LN, Krumm J. 2008. Discovering points of interest from users’ map annotations. GeoJournal. 72(3-4):215–227.
   Google Scholar
• Muttaqien BI, Ostermann FO, Lemmens RL. 2018. Modeling aggregated expertise of user contributions to assess the credibility of OpenStreetMap features. Trans GIS. 22(3):823–841.
   Web of Science ®Google Scholar
• Napolitano M, Mooney P. 2012. MVP OSM: a tool to identify areas of high quality contributor activity in OpenStreetMap. Bull Soc Cartogr. 45(1):10–18.
   Google Scholar
• Over M, Schilling A, Neubauer S, Zipf A. 2010. Generating web-based 3D city models from OpenStreetMap: the current situation in Germany. Comput Environ Urban Syst. 34(6):496–507.
   Web of Science ®Google Scholar
• Picton P. 1994. Introduction to neural networks. London: Palgrave.
   Google Scholar
• Quinn SD, MacEachren AM. 2018. A geovisual analytics exploration of the OpenStreetMap crowd. Cartogr Geogr Inf Sci. 45(2):140–155.
   Web of Science ®Google Scholar
• Sabone B. 2010. Assessing alternative technologies for use of volunteered geographic information in authoritative databases. Canada: Department of Geodesy and Geomatics Engineering, University of New Brunswick.
   Google Scholar
• Seasholtz MB, Kowalski B. 1993. The parsimony principle applied to multivariate calibration. Anal Chim Acta. 277(2):165–177.
   Web of Science ®Google Scholar
• Severinsen J, de Roiste M, Reitsma F, Hartato E. 2019. VGTrust: measuring trust for volunteered geographic information. Int J Geogr Inf Sci. 33(8):1683–1701.
   Web of Science ®Google Scholar
• Smith TR. 1984. Artificial intelligence and its applicability to geographical problem solving. Prof Geogr. 36(2):147–158.
   Web of Science ®Google Scholar
• Su S, Lei C, Li A, Pi J, Cai Z. 2017. Coverage inequality and quality of volunteered geographic features in Chinese cities: analyzing the associated local characteristics using geographically weighted regression. Appl Geogr. 78:78–93.
   Web of Science ®Google Scholar
• Yang A, Fan H, Jing N. 2016. Amateur or professional: assessing the expertise of major contributors in OpenStreetMap based on contributing behaviors. ISPRS Int J Geo Inf. 5(2):21.
   Web of Science ®Google Scholar
• Zhang YX. 2007. Artificial neural networks based on principal component analysis input selection for clinical pattern recognition analysis. Talanta. 73(1):68–75.
   PubMed Web of Science ®Google Scholar
• Zielstra D, Zipf A. 2010. Quantitative studies on the data quality of OpenStreetMap in Germany. Proceedings of the Sixth International Conference on Geographic Information Science; Zurich (Switzerland): GIScience.
   Google Scholar