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
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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
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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
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Acknowledgement:
This presentation based on a
Powerpoint lecture by Professor
Dante Verme, George
Washington University
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Overview
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GIS
 Integrates databases, graphics with digital maps.
 Geographic display of information
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What is GIS?
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What is GIS?
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What is GIS?
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What is GIS?
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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 Analysis
911 Calls in Portland
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Hot Spot Analysis
 Hot Spot tool is located in the Mapping
Clusters toolset in the Spatial Statistics tools.
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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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Hot Spot Analysis
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Hot Spot Analysis
Distance Bands Between
Neighbor Counts Illustration
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Hot Spot Analysis
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Hot Spots
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Hot Spots
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Weighting- Distance
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Hot Spots
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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 Regression
 Multiple Regression Model
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Spatial Regression
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Spatial Regression
 Usually follows hot-spot analysis
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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.
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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 Regression
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Spatial Regression
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Spatial Regression
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Spatial (OLS) Regression
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Spatial (OLS) Regression
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Spatial (OLS) Regression
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Spatial (OLS) Regression
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Spatial Regression
 Thematic Map of Residuals
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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
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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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Spatial Regression
Geographically Weighted Regression
(GWR)
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 Source: Yu and Wei, Geography Department UW
Spatially aggregated data Spatially disaggregated data
House density House density 43

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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GLOBAL
Model
LOCAL
Model
OLS vs GWR
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 Source: Yu and Wei, Geography Department UW
Weighting function
Bandwidth
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 Source: Yu and Wei, Geography Department UW
Weighting function
Bandwidth
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Weight Matrix
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Weighting Scheme I
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Weighting Scheme II

 d ij
= distance between two features i and j h i
= nearest neighbor distance from feature i
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Weighting Scheme II
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Spatial GWR Regression
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GWR
 Are the regressions coefficients varying across the study area.
– Ftests based on the variability of the individual regression coefficients
 Surface map of the local regression coefficients over the study area.
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Floor Size
High : 119.49
Low : 17.63
A
Num. of Bathrm
High : 39931.12
Low : -2044.24
D
Air Conditioner
High : 55860.63
Low : -7098.88
B
House Age
High : 929.44
Low : -1402.30
E
0 5 10
Kilometers
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Fire Place
High : 74706.97
Low : -6722.29
C
Soil & Imp. Sfc
High : 34357.96
Low : -220301.55
F
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