1,204
Views
5
CrossRef citations to date
0
Altmetric
Research Articles

Enhancing VGI application semantics by accounting for spatial bias

Pages 255-268 | Received 26 May 2019, Accepted 15 Jul 2019, Published online: 31 Jul 2019

References

  • Amatulli, G., Domisch, S., Tuanmu, M.-N., Parmentier, B., Ranipeta, A., Malczyk, J., & Jetz, W. (2018). A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Scientific Data, 5, 180040.
  • Anderson, D. R., Laake, J. L., Crain, B. R., & Burnham, K. P. (1979). Guidelines for line transect sampling of biological populations. The Journal of Wildlife Management, 43(1), JSTOR: 70–78.
  • Arsanjani, J., Jamal, M. H., Bakillah, M., Hagenauer, J., & Zipf, A. (2013). Toward mapping land-use patterns from volunteered geographic information. International Journal of Geographical Information Science, 27(12), 2264–2278.
  • Beck, J., Böller, M., Erhardt, A., & Schwanghart, W. (2014). Spatial bias in the GBIF database and its effect on modeling species geographic distributions. Ecological Informatics, 19, 10–15.
  • Boria, R. A., Olson, L. E., Goodman, S. M., & Anderson, R. P. (2014). Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecological Modelling, 275, 73–77.
  • Brunsdon, C. (1995). Estimating probability surfaces for geographical point data: An adaptive kernel algorithm. Computers & Geosciences, 21(7), 877–894.
  • Columbia University, Center for International Earth Science Information Network - CIESIN. (2018). Gridded population of the world, Version 4 (GPWv4): Population density, revision 11. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC).
  • Dewey, T., & Middleton, C. (2002). Turdus Migratorius. Animal Diversity Web. Retrieved from https://animaldiversity.org/accounts/Turdus_migratorius/
  • Egenhofer, M. J. (2002). Toward the semantic geospatial web. Proceedings of the 10th ACM International Symposium on Advances in Geographic Information Systems (pp. 1–4). McLean, Virginia: ACM.
  • Elith, J., Graham, C. H., Anderson, R. P., Dudík, M., Ferrier, S., Guisan, A., … Zimmermann, N. (2006). Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29(2), 129–151.
  • Fick, S., & Hijmans, R. (2017). WorldClim 2: New 1 Km Spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302–4315.
  • Fonte, C. C., Bastin, L., See, L., Foody, G., & Lupia, F. (2015). Usability of VGI for validation of land cover maps. International Journal of Geographical Information Science, 29(7), 1269–1291.
  • Franklin, J., & Miller, J. A. (2009). Mapping species distributions: Spatial inference and prediction (Vol. 338). Cambridge: Cambridge University Press.
  • Goodchild, M. F. (2007). Citizens as sensors: The world of volunteered geography. Geojournal, 69(4), 211–221.
  • Goodchild, M. F., & Li, L. (2012). Assuring the quality of volunteered geographic information. Spatial Statistics, 1, 110–120.
  • Haklay, M., & Weber, P. (2008). OpenStreetMap: User-generated street maps. Pervasive Computing, IEEE, 7(4), 12–18.
  • Huang, Q., & Wong, D. W. S. (2015). Modeling and visualizing regular human mobility patterns with uncertainty: An example using twitter data. Annals of the Association of American Geographers, 105(6), 1179–1197.
  • Janowicz, K., Schade, S., Bröring, A., Keßler, C., Maué, P., & Stasch, C. (2010). Semantic enablement for spatial data infrastructures. Transactions in GIS, 14(2), 111–129.
  • Janowicz, K., Scheider, S., Pehle, T., & Hart, G. (2012). Geospatial semantics and linked spatiotemporal data-past, present, and future. Semantic Web, 3(4), 321–332.
  • Jensen, R. R., & Shumway, J. M. (2010). Sampling our world. In B. Gomez & J. Paul Jones III (Eds.), Research methods in geography: A critical introduction (pp. 77–90). John Wiley & Sons.
  • Kadmon, R., Farber, O., & Danin, A. (2004). Effect of roadside bias on the accuracy of predictive maps produced by bioclimatic models. Ecological Applications, 14(2), 401–413.
  • Kelling, S., Hochachka, W. M., Fink, D., Riedewald, M., Caruana, R., Ballard, G., & Hooker, G. (2009). Data-intensive science: A new paradigm for biodiversity studies. Bioscience, 59(7), 613–620.
  • Kuhn, W. (2005). Geospatial semantics: Why, of What, and How? Journal on Data Semantics, III, 1–24. Springer.
  • Li, W., Goodchild, M. F., & Raskin, R. (2014). Towards geospatial semantic search: exploiting latent semantic relations in geospatial data. International Journal of Digital Earth, 7(1), 17–37.
  • Miller, H. J., & Goodchild, M. F. (2014). Data-driven geography. GeoJournal, 80(4), 449–461.
  • Munson, A. M., Webb, K., Sheldon, D., Fink, D., Hochachka, W. M., Iliff, M., … Kelling, S. (2012). The ebird reference dataset, version 4.0 (pp. 1–11). Ithaca, NY: Cornell Lab of Ornithology and National Audubon Society.
  • Pardieck, K. L., Ziolkowski, D. J., Jr, Hudson, M.-A. R., & Campbell, K. (2016). North American Breeding Bird Survey Dataset 1966–2015, Version 2015.1. doi:10.5066/F7C53HZN
  • Pardieck, K. L., Ziolkowski, D. J., Jr, Lutmerding, M., & Hudson, M.-A. R. (2018). North American breeding bird survey dataset 1966–2017, version 2017.0. U.S. Geological Survey, Patuxent Wildlife Research Center. doi:10.5066/F76972V8
  • Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190(3–4), 231–259.
  • Phillips, S. J., & Dudík, M. (2008). Modeling of species distributions with maxent: New extensions and a comprehensive evaluation. Ecography, 31(2), 161–175.
  • Ripley, B. D. (1976). The second-order analysis of stationary point processes. Journal of Applied Probability, 13(2), 255–266.
  • Robbins, C. S., Bystrak, D., & Geissler, P. H. (1986). The breeding bird survey: Its first fifteen Years, 1965–1979 (No. FWS-PUB-157). Patuxent wildlife research center (Vol. 89). Laurel, MD: DTIC Document. doi:10.2307/1368666
  • Sauer, J. R., Hines, J. E., Fallon, J. E., Link, W. A., Fallon, J. E., Pardieck, K. L., & Ziolkowski, D. J. (2013). The north american breeding bird survey 1966–2011: Summary analysis and species accounts. North American Fauna, 79(79), 1–32.
  • See, L., Mooney, P., Foody, G., Bastin, L., Comber, A., Estima, J., … Rutzinger, M. (2016). Crowdsourcing, citizen science or volunteered geographic information? The current state of crowdsourced geographic information. ISPRS International Journal of Geo-Information, 5(5), 55.
  • Sheth, A. (1997). Panel: Data semantics: What, where and how? Database Applications Semantics: Proceedings of the IFIP WG 2.6 Working Conference on Database Applications Semantics (DS-6) Stone Mountain (pp. 601–610), Atlanta, Georgia, U.S.A., May 30 - June 2, 1995: Springer.
  • Sui, D., Elwood, S., & Goodchild, M. (eds). (2013). Crowdsourcing geographic knowledge: Volunteered geographic information (VGI) in theory and practice. Springer Science & Business Media.
  • Sullivan, B. L., Aycrigg, J. L., Barry, J. H., Bonney, R. E., Bruns, N., Cooper, C. B., … Kelling, S. (2014). The EBird enterprise: An integrated approach to development and application of citizen science. Biological Conservation, 169, 31–40.
  • Sullivan, B. L., Wood, C. L., Iliff, M. J., Bonney, R. E., Fink, D., & Kelling, S. (2009). EBird: A citizen-based bird observation network in the biological sciences. Biological Conservation, 142(10), 2282–2292.
  • Wikipedia. (2016). Breeding Bird Survey. Author. Retrieved from https://en.wikipedia.org/wiki/Breeding_bird_survey
  • Wood, J. (1985). What’s in a Link? Foundations for semantic networks. In R. J. Brachman & H. J. Levesque (Eds.), Readings in knowledge representation (pp. 35–82). Los Altos, California: Morgan Kaufmann Publishers, Inc.
  • Yue, P., Liping, D., Yang, W., Genong, Y., & Zhao, P. (2007). Semantics-based automatic composition of geospatial web service chains. Computers & Geosciences, 33(5), 649–665.
  • Zhang, C., Li, W., & Zhao, T. (2007). Geospatial data sharing based on geospatial semantic web technologies. Journal of Spatial Science, 52(2), 35–49.
  • Zhang, G., Huang, Q., Zhu, A.-X., & Keel, J. (2016). Enabling point pattern analysis on spatial big data using cloud computing: optimizing and accelerating Ripley’s K function. International Journal of Geographical Information Science, 30(11), 2230–2252.
  • Zhang, G., & Zhu, A.-X. (2018). The representativeness and spatial bias of volunteered geographic information: A review. Annals of GIS, 24(3), 151–162.
  • Zhang, G., & Zhu, A.-X. (2019). A representativeness directed approach to spatial bias mitigation in VGI for predictive mapping. International Journal of Geographical Information Science, 33(9), 1873–1893.
  • Zhang, G., Zhu, A.-X., & Huang, Q. (2017). A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data. International Journal of Geographical Information Science, 31(10), 2068–2097.
  • Zhang, G., Zhu, A.-X., Huang, Z.-P., Ren, G., Qin, C.-Z., & Xiao, W. (2018). Validity of historical volunteered geographic information: evaluating citizen data for mapping historical geographic phenomena. Transactions in GIS, 22(1), 149–164.
  • Zhu, A.-X., Zhang, G., Wang, W., Xiao, W., Huang, Z.-P., Dunzhu, G.-S., … Yang, S. (2015). A citizen data-based approach to predictive mapping of spatial variation of natural phenomena. International Journal of Geographical Information Science, 29(10), 1864–1886.
  • Zook, M., Graham, M., Shelton, T., & Gorman, S. (2010). Volunteered geographic information and crowdsourcing disaster relief: A case study of the haitian earthquake. World Medical & Health Policy, 2(2), 6–32.