12,708
Views
123
CrossRef citations to date
0
Altmetric
Articles

Spatial prediction based on Third Law of Geography

ORCID Icon, , , ORCID Icon &
Pages 225-240 | Received 01 Oct 2018, Accepted 08 Oct 2018, Published online: 19 Oct 2018

References

  • Anselin, L. 2013. Spatial Econometrics: Methods and Models (Vol. 4). New York, NY: Springer Science & Business Media.
  • Brunsdon, C., A. S. Fotheringham, and M. E. Charlton. 1996. “Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity.” Geographical Analysis 28: 281–298. doi:10.1111/j.1538-4632.1996.tb00936.x.
  • Cressie, N. 1991. Statistics for Spatial Data, 900 pp. New York, NY: Wiley.
  • Cruden, D. 2017. Landslide Risk Assessment. London: Routledge.
  • Dokuchaev, V. V. 1883/1948/1967. “Russian Chernozem.” In Selected Works of V. V. Dokuchaev, Moscow, 1948, 1, 14–419, (for USDA-NSF), Publ. by S. Monson, 1967. (Transl. into English by N. Kaner). Jerusalem: Israel Program for Scientific Translations.
  • Eldawy, A., and M. F. Mokbel, 2015. The Era of Big Spatial Data. In: Data Engineering Workshops (ICDEW), The 31st IEEE International Conference, 42–49. Doi:10.1177/1753193414554357
  • Fotheringham, A. S., and C. Brunsdon. 1999. “Local Forms of Spatial Analysis.” Geographical Analysis 31: 340–358. doi:10.1111/j.1538-4632.1999.tb00989.x.
  • Fotheringham, A. S., C. Brunsdon, and M. Charlton. 2002. Geographically Weighted Regression - the Analysis of Spatially Varying Relationships. Chichester: Wiley.
  • Goodchild, M. F. 2004a. “GIScience: Geography, Form, and Process.” Annals of the Association of American Geographers 94: 709–714.
  • Goodchild, M. F. 2004b. “The Validity and Usefulness of Laws in Geographic Information Science and Geography.” Annals of the Association of American Geographers 94: 300–303. doi:10.1111/j.1467-8306.2004.09402008.x.
  • Goodchild, M. F. 2007. “Citizens as Sensors: The World of Volunteered Geography.” GeoJournal 69 (4): 211–221. doi:10.1007/s10708-007-9111-y.
  • Goodchild, M. F., and A. J. Glennon. 2010. “Crowdsourcing Geographic Information for Disaster Response: A Research Frontier.” International Journal of Digital Earth 3 (3): 231–241. doi:10.1080/17538941003759255.
  • Goodchild, M. F., B. O. Parks, and L. T. Steyaert, eds. 1993. Environmental Modeling with GIS. New York, USA: Oxford University Press.
  • Goodchild, M. F., L. T. Steyeart, and B. O. Parks. 1996. GIS and Environmental Modeling: Progress and Research Issues. GIS World:Fort Collins, USA.
  • Goovaerts, P. 1999. “Geostatistics in Soil Science: State-Of-The-Art and Perspectives.” Geoderma 89 (1–2): 1–45. doi:10.1016/S0016-7061(98)00078-0.
  • Haklay, M. 2013. “Citizen Science and Volunteered Geographic Information: Overview and Typology of Participation.” In Crowdsourcing Geographic Knowledge, edited by D. Sui, S. Elwood, and M. Goodchild. Dordrecht: Springer.
  • Hartshorne, R. 1939. “The Nature of Geography: A Critical Survey of Current Thought in the Light of the Past.” Annals of the Association of American Geographers 29 (3): 173–412. doi:10.2307/2561063.
  • Hartshorne, R. 1959. Perspective on the Nature of Geography. Chicago: Rand McNally.
  • Harvey, D. 1996. Justice, Nature and the Geography of Difference. Oxford: Basil Blackwell.
  • Hengl, T., G. B. M. Heuvelink, and A. Stein. 2004. “A Generic Framework for Spatial Prediction of Soil Variables Based on Regression-Kriging.” Geoderma 120: 75–93. doi:10.1016/j.geoderma.2003.08.018.
  • Hengl, T., G. B. M. Heuvelink, and D. G. Rossiter. 2007. “About Regression-Kriging: From Equations to Case Studies.” Computers & Geosciences 33 (10): 1301–1315. doi:10.1016/j.cageo.2007.05.001.
  • Hunsaker, C. T., M. F. Goodchild, M. A. Friedl, and T. J. Case, Eds.. 2013. Spatial Uncertainty in Ecology: Implications for Remote Sensing and GIS Applications. Springer Science & Business Media, New York.
  • Isaaks, E. H., and R. M. Srivastava. 1989. An Introduction to Applied Geostatistics. New York, NY: Oxford University Press.
  • Jenny, H. 1994. Factors of Soil Formation: A System of Quantitative Pedology. North Chelmsford, MA: Courier Corporation.
  • Kanevski, M., V. Timonin, and A. Pozdnukhov. 2009. Machine Learning for Spatial Environmental Data: Theory, Applications, and Software. Lausanne: EPFL Press.
  • Kitanidis, P. K., and K. F. Shen. 1996. “Geostatistical Interpolation of Chemical Concentration.” Advances in Water Resources 19 (6): 369–378. doi:10.1016/0309-1708(96)00016-4.
  • Krige, D. G. 1951. “A Statistical Approach to Some Basic Mine Valuation Problems on the Witwatersrand.” Journal of the Chemical, Metallurgical and Mining Society of South Africa 52 (6): 119–139.
  • Li, J., A. D. Heap, A. Potter, and J. J. Daniell. 2011. “Application of Machine Learning Methods to Spatial Interpolation of Environmental Variables.” Environmental Modelling & Software 26 (12): 1647–1659. doi:10.1016/j.envsoft.2011.07.004.
  • Li, Y., A. X. Zhu, Z. Shi, J. Liu, and F. Du. 2016. “Supplemental Sampling for Digital Soil Mapping Based on Prediction Uncertainty from Both the Feature Domain and the Spatial Domain.” Geoderma 284: 73–84. doi:10.1016/j.geoderma.2016.08.013.
  • Ließ, M., B. Glaser, and B. Huwe. 2012. “Uncertainty in the Spatial Prediction of Soil Texture: Comparison of Regression Tree and Random Forest Models.” Geoderma 170: 70–79. doi:10.1016/j.geoderma.2011.10.010.
  • Liu, J., 2017. Integration of Samples from Multiple Sources for Predictive Mapping over Large Areas, PhD thesis, University of Wisconsin – Madison, Madison, WI, USA.
  • Liu, J., A. X. Zhu, D. Rossiter, F. Du, and J. Burt. 2018. “Reliability Estimation of Individual Sample Points in Individual Predictive Soil Mapping (Manuscript under Review).”
  • Matheron, G. 1963. “Principles of Geostatistics.” Economic Geology 58: 1246–1266. doi:10.2113/gsecongeo.58.8.1246.
  • Miller, D. A., and R. A. White. 1998. “A Conterminous United States Multilayer Soil Characteristics Dataset for Regional Climate and Hydrology Modeling.” Earth Interactions 2 (2): 1–26. doi:10.1175/1087-3562(1998)002<0001:ACUSMS>2.3.CO;2.
  • Odeh, I. O., A. B. McBratney, and D. J. Chittleborough. 1995. “Further Results on Prediction of Soil Properties from Terrain Attributes: Heterotopic Cokriging and Regression-Kriging.” Geoderma 67 (3–4): 215–226. doi:10.1016/0016-7061(95)00007-B.
  • Phillips, J. D. 2003. “Sources of Nonlinearity and Complexity in Geomorphic Systems.” Progress in Physical Geography 27: 1–23. doi:10.1191/0309133303pp340ra.
  • Qin, C.-Z., A.-X. Zhu, T. Pei, B.-L. Li, T. Scholten, T. Behrens, and C.-H. Zhou. 2011. “An Approach to Computing Topographic Wetness Index Based on Maximum Downslope Gradient.” Precision Agriculture 12 (1): 32–43. doi:10.1007/s11119-009-9152-y.
  • Shi, W. 2008. “Modeling Uncertainty in Geographic Information and Analysis.” Science in China Series E: Technological Sciences 51 (1): 38–47. doi:10.1007/s11431-008-5019-0.
  • Skidmore, A., eds.. 2003. Environmental Modelling with GIS and Remote Sensing. Boca Raton, FL: CRC Press.
  • Stein, A., and L. Corsten. 1991. “Universal Kriging and Cokriging as a Regression Procedure.” Biometrics 47 (2): 575–587. doi:10.2307/2532147.
  • Sui, D., S. Elwood, and M. Goodchild, eds.. 2012. Crowdsourcing Geographic Knowledge: Volunteered Geographic Information (VGI) in Theory and Practice. New York, NY: Springer Science & Business Media.
  • Tobler, W. R. 1970. “A Computer Movie Simulating Urban Growth in the Detroit Region.” Economic Geography 46 (sup1): 234–240. doi:10.2307/143141.
  • Tomlinson, R. F., H. W. Calkins, and D. Marble. 1976. Computer Handling of Geographic Data: An Examination of Selected Geographic Information Systems. Paris,: UNES.
  • Van Westen, C. J., E. Castellanos, and S. L. Kuriakose. 2008. “Spatial Data for Landslide Susceptibility, Hazard, and Vulnerability Assessment: An Overview.” Engineering Geology 102 (3–4): 112–131. doi:10.1016/j.enggeo.2008.03.010.
  • Wingle, W. L., and E. P. Poeter. 1998. “Directional Semivariograms: Kriging Anisotropy without Anisotropy Factors.” Advances in Geostatistics 2: 1–4.
  • Wortley, R., and M. Townsley, eds.. 2016. Environmental Criminology and Crime Analysis. Vol. 18. London: Taylor & Francis.
  • Yang, L., A. X. Zhu, B. L. Li, C. Z. Qin, T. Pei, B. Y. Liu, R. K. Li, and Q. G. Cai. 2007. “Extraction of Knowledge about Soil-Environment Relationship for Soil Mapping Using Fuzzy C-Means (FCM) (In Chinese).” Acta Pedologica Sinica 44 (5): 784–791.
  • Zhang, S. J., A. X. Zhu, J. Liu, C.-Z. Qin, and Y.-M. An. 2016. “An Heuristic Uncertainty Directed Field Sampling Design for Digital Soil Mapping.” Geoderma 267: 123–136. doi:10.1016/j.geoderma.2015.12.009.
  • Zhu, A. X., L. Yang, B. Li, C. Qin, E. English, J. E. Burt, and C. H. Zhou. 2008. “Purposefully Sampling for Digital Soil Mapping.” In Digital Soil Mapping with Limited Data, eds A. E. Hartemink, A. B. McBratney, and M. L. Mendonca Santos, 233–245. New York: Springer-Verlag.
  • Zhu, A. X., G. M. Zhang, W. Wang, W. Xiao, Z. P. Huang, D. Z. Ge-Sang, G. P. Ren, et al. 2015b. “A Citizen Data-Based Approach to Predictive Mapping of Spatial Variation of Natural Phenomena.” International Journal of Geographical Information Science 29 (10): 1864–1886. doi:10.1080/13658816.2015.1058387.
  • Zhu, A. X., J. Liu, F. Du, S. J. Zhang, C. Z. Qin, J. Burt, and T. Scholten. 2015a. “Predictive Soil Mapping with Limited Sample Data.” European Journal of Soil Science 66 (3): 535–547. doi:10.1111/ejss.12244.