Advanced search
Publication Cover
Geo-spatial Information Science Volume 19, 2016 - Issue 2: Big Data in Geo-spatial Information Science
Submit an article Journal homepage
2,706
Views
10
CrossRef citations to date
0
Altmetric
Articles

Spatially simplified scatterplots for large raster datasets

Bin LiDepartment of Geography, College of Science and Engineering, Central Michigan University, Mount Pleasant, MI, USACorrespondence[email protected]
,
Daniel A. GriffithSchool of Economic, Political and Policy Sciences, University of Texas at Dallas, Richardson, TX, USA
&
Brian BeckerDepartment of Geography, College of Science and Engineering, Central Michigan University, Mount Pleasant, MI, USA
Pages 81-93 | Received 10 Feb 2016, Accepted 13 Apr 2016, Published online: 24 May 2016

References

  • Bivand, R., J. Hauke, and T. Kossowski. 2013. “Computing the Jacobian in Gaussian Spatial Autoregressive Models: An Illustrated Comparison of Available Methods.” Geographical Analysis 45 (2): 150–179.10.1111/gean.2013.45.issue-2
  • Buttenfield, B. P., and R. B. McMaster. 1991. Map Generalization: Making Rules for Knowledge Representation. New York: Longman Scientific & Technical.
  • Carr, D. B., R. J. Littlefield, W. L. Nicholson, and J. S. Littlefield. 1987. “Scatterplot Matrix Techniques for Large N.” Journal of the American Statistical Association 82 (398): 424–436.
  • Chun, Y., and D. A. Griffith. 2011. “Modeling Network Autocorrelation in Space-Time Migration Flow Data: An Eigenvector Spatial Filtering Approach.” Annals of the Association of American Geographers 101 (3): 523–536.10.1080/00045608.2011.561070
  • Chun, Y., and D. A. Griffith. 2013. Spatial Statistics and Geostatistics: Theory and Applications for Geographic Information Science and Technology. London: Sage.
  • Clifford, P., S. Richardson, and D. Hémon. 1989. “Assessing the Significance of the Correlation between Two Spatial Processes.” Biometrics 45 (1): 123–134.10.2307/2532039
  • Cressie, N., A. Olsen, and D. Cook. 1997. “Massive Data Sets: Problems and Possibilities, with Application to Environmental Monitoring.” In Massive Data Sets: Proceedings of a Workshop, 115–119. Washington, DC: National Academies.
  • Dinero, T. E. 1996. “Seven Reasons Why You Should Not Categorize Continuous Data.” Journal of Health & Social Policy 8 (1): 63–72.
  • ERDAS, Inc. 1999. ERDAS Field Guide 5th ed. Atlanta, GA: ERDAS.
  • Gelfand, A. E., P. Diggle, P. Guttorp, and M. Fuentes. 2010. Handbook of Spatial Statistics. London: CRC Press.10.1201/CHHANMODSTA
  • Griffith, D. A. 2000. “Eigenfunction Properties and Approximations of Selected Incidence Matrices Employed in Spatial Analyses.” Linear Algebra and Its Applications 321 (1–3): 95–112.10.1016/S0024-3795(00)00031-8
  • Griffith, D. A. 2003. Spatial Autocorrelation and Spatial Filtering: Gaining Understanding through Theory and Scientific Visualization. Berlin: Springer-Verlag.10.1007/978-3-540-24806-4
  • Griffith, D. A. 2004a. “Extreme Eigenfunctions of Adjacency Matrices for Planar Graphs Employed in Spatial Analyses.” Linear Algebra and Its Applications 388: 201–219.10.1016/S0024-3795(03)00368-9
  • Griffith, D. A. 2004b. “Faster Maximum Likelihood Estimation of Very Large Spatial Autoregressive Models: An Extension of the Smirnov–Anselin Result.” Journal of Statistical Computation and Simulation 74 (12): 855–866.10.1080/00949650410001650126
  • Griffith, D. A. 2005. “Effective Geographic Sample Size in the Presence of Spatial Autocorrelation.” Annals of the Association of American Geographers 95 (4): 740–760.10.1111/j.1467-8306.2005.00484.x
  • Griffith, D. A. 2015. “Approximation of Gaussian Spatial Autoregressive Models for Massive Regular Square Tessellation Data.” International Journal of Geographical Information Science 29 (12): 2143–2173.10.1080/13658816.2015.1068318
  • Hastie, T. J., and R. J. Tibshirani. 1990. Generalized Additive Models. vol. 43. London: CRC Press.
  • MacCallum, R. C., Z. Shaobo, K. J. Preacher, and D. D. Rucker. 2002. “On the Practice of Dichotomization of Quantitative Variables.” Psychological Methods 7 (1): 19–40.10.1037/1082-989X.7.1.19
  • Mackaness, W. A., A. Ruas, and L. T. Sarjakoski. 2011. Generalisation of Geographic Information: Cartographic Modelling and Applications. Amsterdam: Elsevier.
  • McMaster, R. B., and K. S. Shea. 1992. Generalization in Digital Cartography. Washington, DC: Association of American Geographers.
  • Monmonier, M. S. 1983. “Raster-Mode Area Generalization for Land Use and Land Cover Maps” Cartographica: The International Journal for Geographic Information and Geovisualization 20 (4): 65–91.10.3138/X572-0327-4670-1573
  • Pan, V. Y., and Z. Q. Chen. 1999. “The Complexity of the Matrix Eigenproblem.” In Proceedings of the Thirty-First Annual ACM Symposium on Theory of Computing, 507–516. New York: ACM.
  • Unwin, A., M. Theus, and H. Hofmann. 2007. Graphics of Large Datasets: Visualizing a Million. Berlin: Springer Science & Business Media.
  • Wand, M. P., and M. C. Jones. 1994. Kernel Smoothing. London: CRC Press.
  • Wang, J. F., A. Stein, B. B. Gao, and Y. Ge. 2012. “A Review of Spatial Sampling.” Spatial Statistics 2 (1): 1–14.10.1016/j.spasta.2012.08.001
  • Wood, S. 2006. Generalized Additive Models: An Introduction with R. London: CRC Press.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.