PPT-Key Spatial Analysis Concepts from Exercise 3

Contours and Hillshade to visualize topography Zonal Average of Raster over Subwatershed Subwatershed Precipitation by Thiessen Polygons Thiessen Polygons Feature to Raster Precip field Zonal Statistics Mean. China Data Center. University of Michigan. Spatial Intelligence for . China . and Global Studies. Information, Technology and . Applications. Demand for Spatial Analysis. Identify the spatial structure of economic and demographic forces . Neil Humpage, University of Leicester. The EO-Convoy Land study team. Introduction. ESA are undertaking three studies investigating possible . synergistic . satellite missions flying in formation with the operational . 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.. Abstract concepts become concrete when students can connect the words to real things and activities. . By . turning your classroom into a factory for a class period, you will help students understand and retain knowledge of these concepts. . Chapter One. Spatial Analysis. Patterns of spatially distributed points.. Correlation with environmental variables.. Interpolations and predictive models.. Spatial autocorrelation -> spatial patterns, aggregation. Based on distance to neighbors. Kaplan-Meier estimator, Moran’s I.. 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. . Ged Ridgway, London. With thanks to John Ashburner. a. nd the FIL Methods Group. Preprocessing overview. fMRI. time-series. Motion corrected. Mean functional. REALIGN. COREG. Anatomical MRI. SEGMENT. ”. ISPRS. 2013-SSG. Organized by:. ISPRS COMMISSION II . . - . Theory and Concepts of Spatial Information Science. ISPRS COMMISSION III . . - . Photogrammetric Computer Vision and Image An. alysis. Overview . of Spatial Big Data and . Analytics. (8:40-9:15am). James B. Pick. University of Redlands School of Business. James_pick@redlands.edu. . Pre-ICIS Workshop on Locational Analytics, Spatial . Robert Tanton. (CRICOS) #00212K. Outline. Description . of . spatial . microsimulation. Applications of . spatial . microsimulation. Future of . spatial microsimulation. Further reading. (CRICOS) #00212K. Jo Eidsvik. Jo.eidsvik@ntnu.no. My . background. :. Education. :. MSc. in Applied . Mathematics. , . Univ. . of. Oslo. PhD. in . S. tatistics. , NTNU. Work. . experience. :. Norwegian . Defense. jf@mit.edu. TA: . Rounaq Basu, . rounaq@mit.edu. Technical Instructor: Eric Huntley, . ehuntley@mit.edu. Tuesday, . Lab: . 4-7 PM in . Room . W31-301 . (Armory across street). . (. First meeting, . Lecture 13. 1. Spatial Models & Modeling. Ch. 13 Part 1. Lecture 13. 2. Introduction. A model is a description of reality.. A spatial model describes the basic properties or processes for a set of spatial features which helps us understand their form and behavior..