1. 11 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

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 2

3. 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 4

5. 5 Overview

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

7. 7 What is GIS?

8. 8

9. 9

10. 10 What is GIS?

11. 11 Hot Spot Analysis

12. 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. 13 Hot Spot Analysis 911 Calls in Portland

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

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

16. 16 Hot Spot Analysis

17. 17 Hot Spot Analysis Distance Bands Between Neighbor Counts Illustration

18. 18 Hot Spot Analysis

19. 19 Hot Spots

20. 20 Hot Spots

21. 21 Weighting- Distance

22. 22 Hot Spots

23. 23 Spatial Regression

24. 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. 25 Spatial Regression   Multiple Regression Model

26. 26 Spatial Regression

27. 27 Spatial Regression   Usually follows hot-spot analysis

28. 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. 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. 30 Spatial Regression

31. 31 Spatial Regression

32. 32 Spatial Regression

33. 33 Spatial (OLS) Regression

34. 34 Spatial (OLS) Regression

35. 35 Spatial (OLS) Regression

36. 36 Spatial (OLS) Regression

37. 37 Spatial Regression   Thematic Map of Residuals

38. 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. 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. 40 Spatial Regression

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

42. 42 Spatial Regression Geographically Weighted Regression (GWR)

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

44. 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.dependentexplanatory variables   Bandwiths are preferable chosen to be adaptive.   It generates a local regression model for each feature. It is truly a spatial analytical technique.

45. 45 OLS vs GWR GLOBALModel LOCALModel

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

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

48. 48 Weight Matrix

49. 49 Weighting Scheme I

50. 50 Weighting Scheme II d ij = distance between two features i and j d ij = distance between two features i and j h i = nearest neighbor distance from feature i h i = nearest neighbor distance from feature i

51. 51 Weighting Scheme II

52. 52 Spatial GWR Regression

53. 53 GWR   Are the regressions coefficients varying across the study area. – –F-tests based on the variability of the individual regression coefficients   Surface map of the local regression coefficients over the study area.

54. 54