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 . 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 11 Specifications are subject to change without notice HOLOEYE Photonics AG AlbertEinsteinStr 14 12489 Berlin Germany Phone 49 030 6392 3660 Fax 49 030 6392 3662 contactholoeyecom wwwholoeyecom HOLOEYE Corporation 1620 Fifth Avenue Suite 550 San Di Chris Jochem. Geog. 5161 – Spring 2011. When you know ‘where’, you can start to . ask . ‘why’. John Snow’s map of cholera deaths in London, 1854.. Water pump locations. Need to move beyond simply mapping events and beyond general point pattern analysis.. 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. 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 . Reflectors. By Alexander B. Snyder. The New Madrid Seismic Zone. Responsible for a huge series of earthquakes from 1811-1812. Contains layers and layers of sediment that needs to be studies in more depth. 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. 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. 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 . 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. . 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. Oral Presentation at The 143. rd. APHA Annual Meeting and Exposition(October . 31 – November 4, 2015. ), Chicago. . George Siaway, PhD, Christine A. Clarke, MS, Fern Johnson-Clarke, PhD and Rowena Samala, MS.