PPT-Application of spatial autocorrelation analysis in determin

data Edward Park SAC in MATLAB Digital Globe inc Introduction 11 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 . MatLab. Lecture 19:. Smoothing, Correlation and Spectra. . Lecture 01. . Using . MatLab. Lecture 02 Looking At Data. Lecture 03. . Probability and Measurement Error. . Lecture 04 Multivariate Distributions. MatLab. Lecture 17:. Covariance and Autocorrelation. . Lecture 01. . Using . MatLab. Lecture 02 Looking At Data. Lecture 03. . Probability and Measurement Error. . Lecture 04 Multivariate Distributions. Lecture. 8. Ergodicty. 1. Random process. 2. 3. Agenda (. Lec. . . 8. ). Ergodicity. Central equations. Biomedical engineering example:. Analysis of heart sound murmurs. 4. Ergodicity. A random process . What does it mean?. The variance of the error term is not constant. What are its consequences. ?. . Heteroscedasticity. does not destroy the . unbiasedness. and consistency properties of OLS estimators. 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). What can we do with GIS?. SPATIAL STATISTICS. What can we do with GIS?. SPATIAL STATISTICS. We utilize map data! . What can we do with GIS?. Designed for use with maps. Uses either . RASTER. or . 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 . Robert Tanton. (CRICOS) #00212K. Outline. Description . of . spatial . microsimulation. Applications of . spatial . microsimulation. Future of . spatial microsimulation. Further reading. (CRICOS) #00212K. “…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. Greg Reese, . Ph.D. Research Computing Support Group. Academic Technology Services. Miami University. . October 2013. MATLAB Signal Processing Toolbox. © 2013 Greg Reese. All rights reserved. 2. Toolbox. MatLab. Lecture 19:. Smoothing, Correlation and Spectra. . Lecture 01. . Using . MatLab. Lecture 02 Looking At Data. Lecture 03. . Probability and Measurement Error. . Lecture 04 Multivariate Distributions. Greg Reese, . Ph.D. Research Computing Support Group. Academic Technology Services. Miami University. . October 2013. MATLAB Signal Processing Toolbox. © 2013 Greg Reese. All rights reserved. 2. Toolbox. 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.