11 Pre-conference Training MCH Epidemiology – CityMatCH Joint 2012 Annual Meeting...
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Transcript of 11 Pre-conference Training MCH Epidemiology – CityMatCH Joint 2012 Annual Meeting...
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Pre-conference TrainingMCH Epidemiology – CityMatCH
Joint 2012 Annual Meeting
Intermediate/Advanced Spatial Analysis Techniques for the
Analysis of MCH Data
Tuesday, December 11, 2012
Session LeadersSession Leaders
Russell S. Kirby, PhD, MS, FACERussell S. Kirby, PhD, MS, FACEDepartment of Community and Family Department of Community and Family Health, College of Public Health, Health, College of Public Health, University of South FloridaUniversity of South Florida
Marilyn O’Hara, PhDMarilyn O’Hara, PhDDirector of GIS and Spatial Analysis LabDirector of GIS and Spatial Analysis LabDepartment of PathobiologyDepartment of PathobiologyUniversity of IllinoisUniversity of Illinois
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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
Acknowledgement: Acknowledgement:
This presentation based on a This presentation based on a Powerpoint lecture by Professor Powerpoint lecture by Professor Dante Verme, George Dante Verme, George Washington UniversityWashington University
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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
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Hot Spot tool is located in the Mapping Clusters toolset in the Spatial Statistics tools.
Hot Spot Analysis
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Hot Spot Analysis To work properly it would require as input a feature class from a geodatabase. Populate its dialog.
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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
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Spatial RegressionSpatially Join the 911 Calls in Portland to acensus tract layer to determine how many calls were made from each tract. Why? Demo and SES information is available.
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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).
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Spatial (OLS) Regression
Moran’s Test for Residual Spatial
Autocorrelation We would like the residuals to be
randomly distributed over the study area
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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.
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Spatial Regression What to do next? Identify more
predictors to be included in the model. Generate a matrix scatterplot.
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Simpson’s paradox
House density
Hou
se P
rice
Spatially aggregated data Spatially disaggregated data
House density
Source: Yu and Wei, Geography Department UWSource: Yu and Wei, Geography Department UW
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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.
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Fixed weighting scheme
Bandwidth
Weighting function
Source: Yu and Wei, Geography Department UWSource: Yu and Wei, Geography Department UW
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Adaptive weighting schemes
Bandwidth
Weighting function
Source: Yu and Wei, Geography Department UWSource: Yu and Wei, Geography Department UW
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Weighting Scheme II
ddijij= distance between two features i and j= distance between two features i and j hhii= nearest neighbor distance from feature i= nearest neighbor distance from feature i
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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.