Exploratory Spatial Data Exploratory Spatial Data

Analysis Using Analysis Using GeoDAGeoDA: An : An

IntroductionIntroduction

Prepared by Professor Ravi K. Sharma, University of PittsburghModified for NBDPN 2007 Conference Presentation byProfessor Russell S. Kirby, University of Alabama at Birmingham

ObjectivesObjectives

Using MACDP data on chromosomal Using MACDP data on chromosomal

abnormalities measured across census abnormalities measured across census

tracts, we will demonstrate the use of tracts, we will demonstrate the use of

GeoDaGeoDa to:to:

�� Start a project, import data, use basic functionsStart a project, import data, use basic functions

�� Perform Exploratory Spatial Data Analysis (ESDA) Perform Exploratory Spatial Data Analysis (ESDA)

�� Calculate rates and weightsCalculate rates and weights

�� Create spatial weight matrixCreate spatial weight matrix

�� Perform spatial autocorrelationPerform spatial autocorrelation

The examples that follow are based on a dataset The examples that follow are based on a dataset

for countyfor county--level analysis of low birth weight for level analysis of low birth weight for

the state of Pennsylvania.the state of Pennsylvania.

GeoDaGeoDa

�� GeoDaGeoDa is a freely available software is a freely available software

program for exploratory spatial data program for exploratory spatial data

analysis (ESDA), developed by analysis (ESDA), developed by

Professor Luc Professor Luc AnselinAnselin of the of the

University of IllinoisUniversity of Illinois

�� It can be downloaded from the It can be downloaded from the

following URL:following URL:

•• https://https://www.geoda.uiuc.eduwww.geoda.uiuc.edu//

Beginning a ProjectBeginning a Project

Opening Opening GeoDaGeoDa

�� To begin click on the To begin click on the GeoDaGeoDa IconIcon

Starting a projectStarting a project

Open a map with shape fileOpen a map with shape file

The base mapThe base map

Editing featuresEditing features

Menu toolbar featuresMenu toolbar features

Icon toolbar featuresIcon toolbar features

Creating Maps and Creating Maps and

Selecting FeaturesSelecting Features

Create Create choroplethchoropleth mapsmaps

ChoroplethChoropleth map stepsmap steps

Creating Creating choroplethchoropleth ((quantilequantile) maps) maps

Creating Creating quantilequantile mapsmaps

Final Final choroplethchoropleth mapmap

Open a new copy of base mapOpen a new copy of base map

Create a new Create a new choroplethchoropleth mapmap

Dynamic map selection optionDynamic map selection option

Selecting map areasSelecting map areas

Table featuresTable features

Table sorting featuresTable sorting features

Specific table selectionSpecific table selection

Creating new variablesCreating new variables

Creating shape files from mapCreating shape files from map

Polygon to point shape filePolygon to point shape file

Create Create centroidscentroids for point filesfor point files

Exploratory Data Exploratory Data

Analysis (EDA)Analysis (EDA)

EDA: plotsEDA: plots

Variable selection for plotsVariable selection for plots

Plots: HistogramPlots: Histogram

Linkage: selecting featuresLinkage: selecting features

Linkage: selecting features (Linkage: selecting features (concon’’tt.).)

Generate and interpret box plotsGenerate and interpret box plots

Calculating RatesCalculating Rates

Create raw ratesCreate raw rates

Selecting variables for ratesSelecting variables for rates

Raw rates: by percentRaw rates: by percent

Saving ratesSaving rates

Identifying outliers from box plotsIdentifying outliers from box plots

Create excess risk ratesCreate excess risk rates

Excess risk mapExcess risk map

Creating Empirical Creating Empirical BayesBayes smoothingsmoothing

Map generated by EB smoothingMap generated by EB smoothing

Creating Weights: Creating Weights:

Examining spatial Examining spatial

relationshipsrelationships

Creating weightsCreating weights

Loading weight filesLoading weight files

Creating spatial ratesCreating spatial rates

Spatially smoothed mapSpatially smoothed map

Create weights: RookCreate weights: Rook

Text file of Rook weightsText file of Rook weights

Compare weights with map and tableCompare weights with map and table

View weight characteristicsView weight characteristics

Multiple views: weight histogram, Multiple views: weight histogram,

map and tablemap and table

Create weights: QueenCreate weights: Queen

Compare multiple featuresCompare multiple features

Creating weights: neighborsCreating weights: neighbors

Reviewing weightsReviewing weights

Creating weights: nearest neighborsCreating weights: nearest neighbors

Histogram of neighbor weightsHistogram of neighbor weights

Autocorrelation: Autocorrelation:

Identifying clustersIdentifying clusters

Global Moran Global Moran

�� It is a measure of spatial autocorrelation It is a measure of spatial autocorrelation (feature similarity) based not only on (feature similarity) based not only on feature locations or attribute values alone feature locations or attribute values alone but also on both feature locations and but also on both feature locations and feature values simultaneously. feature values simultaneously.

�� Given a set of features and an associated Given a set of features and an associated attribute, it evaluates whether the pattern attribute, it evaluates whether the pattern expressed is clustered, dispersed, or expressed is clustered, dispersed, or random. random.

�� A Moran's Index value near +1.0 indicates A Moran's Index value near +1.0 indicates clustering; an index value near clustering; an index value near --1.0 1.0 indicates dispersionindicates dispersion

Global MoranGlobal Moran

Autocorrelation: weight file requiredAutocorrelation: weight file required

Global Moran resultGlobal Moran result

Randomization featureRandomization feature

Randomization: graph resultRandomization: graph result

Randomization: Envelope SlopesRandomization: Envelope Slopes

Local Moran (LISA)Local Moran (LISA)

�� The local Moran test (The local Moran test (AnselinAnselin 19951995), detects local ), detects local spatial autocorrelation. It can be used to identify spatial autocorrelation. It can be used to identify local clusters (regions where adjacent areas have local clusters (regions where adjacent areas have similar values) or spatial outliers (areas distinct similar values) or spatial outliers (areas distinct from their neighbors).from their neighbors).

�� The Local Moran statistic decomposes Moran's I The Local Moran statistic decomposes Moran's I ((Moran 1950Moran 1950) into contributions for each location, ) into contributions for each location, IiIi. The sum of . The sum of Ii Ii for all observations is for all observations is proportional to Moran's I, an indicator of global proportional to Moran's I, an indicator of global pattern. pattern.

�� Thus, there can be two interpretations of Local Thus, there can be two interpretations of Local Moran statistics, as indicators of local spatial Moran statistics, as indicators of local spatial clusters and as a diagnostic for outliers in global clusters and as a diagnostic for outliers in global spatial patterns.spatial patterns.

Local MoranLocal Moran

LISA: Significance mapLISA: Significance map

LISA: Cluster mapLISA: Cluster map