PPT-Spatial Autocorrelation Basics

NR 245 Austin Troy University of Vermont SA basics Lack of independence for nearby obs Negative vs positive vs random Induced vs inherent spatial autocorrelation First order gradient vs second order patchiness. Autocorrelation is also sometimes called ODJJHG57347FRUUHODWLRQ or 57523VHULDO57347FRUUHODWLRQ which refers to the correlation between members of a series of numbers arranged in time Positive autocorrelation might be considered a specific form of pe data . Edward Park. SAC in MATLAB. Digital Globe inc.. Introduction. 1.1 Objective. Objective: . To do the . accuracy assessment. of various classification of raster pixels. . Why?. The . ultimate goal of Geographic Information System (GIS) is to model our world. However, the modeling process is too complicated and requires elaborateness that we should not rely entirely on computer. . Nr245. Austin Troy. Based on . Spatial Analysis. by Fortin and Dale, Chapter 5. Autcorrelation types. None: independence. Spatial independence, functional dependence. True autocorrelation>> inherent autoregressive. important. ?. The fundamental issue. "The problem of pattern and scale is the central problem in ecology, unifying population biology and ecosystems science, and marrying basic and applied ecology. Applied challenges ... require the interfacing of phenomena that occur on very different scales of space, time, and ecological organization. Furthermore, there is . Spatial Regression. Elisabeth Root. Department . of Geography. A few . good books…. Bivand. , R., E.J. . Pebesma. and V. Gomez-Rubio. . Applied Spatial Data Analysis with R. . New York: Springer.. RADIOMETRY. A. W. (Tony) . England, Hamid . Nejati. , and Amanda Mims. University of Michigan, Ann Arbor, Michigan, U.S.. A. IGARSS 2011. . Outline. Intro to global snowpack sensing. Limitations of current snowpack sensing technologies. data . Edward Park. SAC in MATLAB. Digital Globe inc.. Introduction. 1.1 Objective. Objective: . To do the . accuracy assessment. of various classification of raster pixels. . Why?. The . ultimate goal of Geographic Information System (GIS) is to model our world. However, the modeling process is too complicated and requires elaborateness that we should not rely entirely on computer. . important. ?. The fundamental issue. "The problem of pattern and scale is the central problem in ecology, unifying population biology and ecosystems science, and marrying basic and applied ecology. Applied challenges ... require the interfacing of phenomena that occur on very different scales of space, time, and ecological organization. Furthermore, there is . “…the problem of relating phenomena across scales is the central problem in biology and in all of science”. . Simon . Levin . 1992.. Why be concerned about scale?. Scale greatly influences our understanding of ecological causality. . studies. Michał . Żmihorski. Department. of . Ecology. SLU, Uppsala, . Sweden. Institute. of Nature . Conservation. PAS, Kraków, Poland. Ornithological. . studies. Aim. : to . propose. . bird-friendly. THE GEOGRAPHIC QUESTIONS. The Why of Where!. How are places described?. What makes each place unique?. What connects places?. What . is . Geography. ?. “description of the earth”. a study of spatial variation. William Greene. Department of Economics. University of South Florida. Econometric Analysis of Panel Data. 17. Spatial Autoregression . and Spatial Autocorelation. Nonlinear Models with Spatial Data. William Greene. Department of Economics. University of South Florida. Econometric Analysis of Panel Data. 17. Spatial Autoregression . and Spatial Autocorelation. Nonlinear Models with Spatial Data. Computational Earth Science. Bill Menke, Instructor. Emily Glazer, Teaching Assistant. TR 2:40 – 3:55. Today. Use of the Fast Fourier Transform in Modeling. “random textures”. of natural phenomenon.