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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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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

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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 Analysis911 Calls in Portland

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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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Hot Spot Analysis

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Hot Spot Analysis

Distance Bands BetweenNeighbor Counts Illustration

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Hot Spot Analysis

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Hot Spots

2020

Hot Spots

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Weighting- Distance

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Hot Spots

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Spatial Regression

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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 RegressionUsually follows hot-spot analysis

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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 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 RegressionGeographically Weighted Regression

(GWR)

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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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OLS vs GWR

GLOBALGLOBALModelModel

LOCALLOCALModelModel

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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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Weight Matrix

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Weighting Scheme I

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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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Weighting Scheme II

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Spatial GWR Regression

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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.

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Soil & Imp. SfcHigh : 34357.96 Low : -220301.55

F

House AgeHigh : 929.44 Low : -1402.30

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Fire PlaceHigh : 74706.97 Low : -6722.29

C

Air ConditionerHigh : 55860.63 Low : -7098.88

B

±

0 10 205

Kilometers

Floor SizeHigh : 119.49 Low : 17.63

A

Num. of BathrmHigh : 39931.12 Low : -2044.24

D