PPT-Spatial Microsimulation and Policy Analysis

Robert Tanton CRICOS 00212K Outline Description of spatial microsimulation Applications of spatial microsimulation Future of spatial microsimulation Further reading CRICOS 00212K. 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.. 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.. Dr Kirk Harland. What is a Spatial . Microsimulation. ?. Static Spatial . Microsimulation. Deterministic Reweighting. Conditional Probabilities. Simulated Annealing. Dynamic . Microsimulation. This . Ben Phillips: Principal Research Fellow, NATSEM, 21 Feb 2015. 2. What are microsimulation models?. Tools to simulate government programs, demographic and economic change, current or alternative situation. Michael Noble and . Wanga. . Zembe. of SASPRI have found that the poorest municipalities are, without exception, in the old apartheid homelands, and all except one in either the Eastern Cape or KZN . USING CGE MODELS FOR POVERTY AND INEQUALITY ANALYSIS. Jeff Round. February 2012. Poverty impact analysis. Impact of economic shocks on poverty. Various manifestations of poverty. money-metric (income, expenditure, assets), health, . 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. methods for . Small Area Estimation. . Dr . Paul . Williamson. Centre for Spatial Demographics Research. Dept. . of Geography & Planning. (1) Direct survey estimation: a recap. (3) Conventional SAE: a recap. 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, . the case of SWITCH. Tim Callan. Economic and Social Research Institute. Overview. Context: the role of SWITCH. S. imulating . W. elfare and . I. ncome . T. ax . CH. anges. A tour of the SWITCH model.