Presentation on theme: "11 Pre-conference Training MCH Epidemiology – CityMatCH Joint 2012 Annual Meeting Intermediate/Advanced Spatial Analysis Techniques for the Analysis of."— Presentation transcript:
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
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
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
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
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).
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 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.
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
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.