PPT-Intro to Spatial Analysis with

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 . 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 . 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.. GIS. Post Midterm Review . March. 25. th. , 2013. Vector vs. . Raster. Characteristic. Vector. Raster. Data Structure. Usually complex. Usually simple. Storage Requirements. Small for most datasets. 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.. 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. 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. Analysis. . of . Social Media Data . Shaowen Wang. CyberInfrastructure and Geospatial Information Laboratory (CIGI). Department of Geography and Geographic Information Science. Department of Computer Science. 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..