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Pre-conference Training MCH Epidemiology – CityMatCH Joint 2012 Annual Meeting

Pre-conference Training MCH Epidemiology – CityMatCH Joint 2012 Annual Meeting Intermediate/Advanced Spatial Analysis Techniques for the Analysis of MCH Data Tuesday, December 11, 2012. 1. Session Leaders. Russell S. Kirby, PhD, MS, FACE

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Pre-conference Training MCH Epidemiology – CityMatCH Joint 2012 Annual Meeting

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  1. Pre-conference Training MCH Epidemiology – CityMatCH Joint 2012 Annual Meeting Intermediate/Advanced Spatial Analysis Techniques for the Analysis of MCH Data Tuesday, December 11, 2012 1

  2. Session Leaders Russell S. Kirby, PhD, MS, FACE Department of Community and Family Health, College of Public Health, University of South Florida Marilyn O’Hara, PhD Director of GIS and Spatial Analysis Lab Department of Pathobiology University of Illinois

  3. Topics*slide needs updating Overview Point Pattern Analysis Hot Spots Surface of Hot Spots Applications Regression Analysis Ordinary Least Squares (OLS) Geographically Weighted Regression (GWR) Testing for Spatial Autocorrelation (Moran’s I) Applications Smoothing Rates: GeoDa

  4. Acknowledgement: This presentation based on a Powerpoint lecture by Professor Dante Verme, George Washington University

  5. Overview

  6. GIS • Integrates databases, graphics with digital maps. • Geographic display of information

  7. What is GIS?

  8. What is GIS?

  9. What is GIS?

  10. What is GIS?

  11. Hot Spot Analysis

  12. Hot Spot Analysis • Identify Statistical Significant Spatial clusters of high (hot) or low (cold) from a particular event (areas of high counts from an event). • It works with number of events summarized in a point. • Based on the Getis-Ord test statistic

  13. Hot Spot Analysis911 Calls in Portland

  14. Hot Spot Analysis • Hot Spot tool is located in the Mapping Clusters toolset in the Spatial Statistics tools.

  15. Hot Spot Analysis • To work properly it would require as input a feature class from a geodatabase. Populate its dialog.

  16. Hot Spot Analysis

  17. Hot Spot Analysis Distance Bands Between Neighbor Counts Illustration

  18. Hot Spot Analysis

  19. Hot Spots

  20. Hot Spots

  21. Weighting- Distance

  22. Hot Spots

  23. Spatial Regression

  24. Spatial Regression • Regression: Regression establishes a relationship among a dependent variable and a set of independent variable(s) • Purpose: better understand patterns of spatial relationships between attributes. • Objective: predictions

  25. Spatial Regression • Multiple Regression Model

  26. Spatial Regression

  27. Spatial Regression • Usually follows hot-spot analysis

  28. Spatial Regression • Spatially Join the 911 Calls in Portland to a census tract layer to determine how many calls were made from each tract. • Why? Demo and SES information is available.

  29. Spatial Regression • A spatial ordinary least square (OLS) regression model is going to determine if the number of 911 calls (dependent variable) from a Portland, OR, census track is a function of the population in each tract (independent variable).

  30. Spatial Regression

  31. Spatial Regression

  32. Spatial Regression

  33. Spatial (OLS) Regression

  34. Spatial (OLS) Regression

  35. Spatial (OLS) Regression

  36. Spatial (OLS) Regression

  37. Spatial Regression • Thematic Map of Residuals

  38. Spatial (OLS) Regression • Moran’s Test for Residual Spatial Autocorrelation • We would like the residuals to be randomly distributed over the study area

  39. Spatial Regression • What to do next? • Identify more predictors to be included in the model. Could be done graphically. • Generate a scatter plot matrix. Check next two slides.

  40. Spatial Regression

  41. Spatial Regression • What to do next? Identify more predictors to be included in the model. Generate a matrix scatterplot.

  42. Spatial RegressionGeographically Weighted Regression (GWR)

  43. Source: Yu and Wei, Geography Department UW Simpson’s paradox Spatially aggregated data Spatially disaggregated data House Price House density House density

  44. GWR • Associations vary spatially and are not fixed. • GWR constructs separate equations by including the dependent and explanatory variables of features that are within the bandwidth of each target feature. • Bandwiths are preferable chosen to be adaptive. • It generates a local regression model for each feature. It is truly a spatial analytical technique.

  45. OLS vs GWR GLOBAL Model LOCAL Model

  46. Source: Yu and Wei, Geography Department UW Fixed weighting scheme Weighting function Bandwidth

  47. Source: Yu and Wei, Geography Department UW Adaptive weighting schemes Weighting function Bandwidth

  48. Weight Matrix

  49. Weighting Scheme I

  50. Weighting Scheme II • dij= distance between two features i and j • hi= nearest neighbor distance from feature i

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