Exploratory Spatial Data Analysis Using GeoDA: : An Introduction

Exploratory Spatial Data Analysis Using GeoDA: : An Introduction Prepared by Professor Ravi K. Sharma, University of Pittsburgh Modified for NBDPN 2007 Conference Presentation by Professor Russell S. Kirby, University of Alabama at Birmingham

Objectives Using MACDP data on chromosomal abnormalities measured across census tracts, we will demonstrate the use of GeoDa to: Start a project, import data, use basic functions Perform Exploratory Spatial Data Analysis (ESDA) Calculate rates and weights Create spatial weight matrix Perform spatial autocorrelation The examples that follow are based on a dataset for county-level analysis of low birth weight for the state of Pennsylvania.

GeoDa GeoDa is a freely available software program for exploratory spatial data analysis (ESDA), developed by Professor Luc Anselin of the University of Illinois It can be downloaded from the following URL: https://www.geoda.uiuc.edu www.geoda.uiuc.edu/

Beginning a Project

Opening GeoDa To begin click on the GeoDa Icon

Starting a project

Open a map with shape file

The base map

Editing features

Menu toolbar features

Icon toolbar features

Creating Maps and Selecting Features

Create choropleth maps

Choropleth map steps

Creating choropleth (quantile)) maps

Creating quantile maps

Final choropleth map

Open a new copy of base map

Create a new choropleth map

Dynamic map selection option

Selecting map areas

Table features

Table sorting features

Specific table selection

Creating new variables

Creating shape files from map

Polygon to point shape file

Create centroids for point files

Exploratory Data Analysis (EDA)

EDA: plots

Variable selection for plots

Plots: Histogram

Linkage: selecting features

Linkage: selecting features (con( con t.)

Generate and interpret box plots

Calculating Rates

Create raw rates

Selecting variables for rates

Raw rates: by percent

Saving rates

Identifying outliers from box plots

Create excess risk rates

Excess risk map

Creating Empirical Bayes smoothing

Map generated by EB smoothing

Creating Weights: Examining spatial relationships

Creating weights

Loading weight files

Creating spatial rates

Spatially smoothed map

Create weights: Rook

Text file of Rook weights

Compare weights with map and table

View weight characteristics

Multiple views: weight histogram, map and table

Create weights: Queen

Compare multiple features

Creating weights: neighbors

Reviewing weights

Creating weights: nearest neighbors

Histogram of neighbor weights

Autocorrelation: Identifying clusters

Global Moran It is a measure of spatial autocorrelation (feature similarity) based not only on feature locations or attribute values alone but also on both feature locations and feature values simultaneously. Given a set of features and an associated attribute, it evaluates whether the pattern expressed is clustered, dispersed, or random. A Moran's Index value near +1.0 indicates clustering; an index value near -1.0 indicates dispersion

Global Moran

Autocorrelation: weight file required

Global Moran result

Randomization feature

Randomization: graph result

Randomization: Envelope Slopes

Local Moran (LISA) The local Moran test (Anselin( 1995), detects local spatial autocorrelation. It can be used to identify local clusters (regions where adjacent areas have similar values) or spatial outliers (areas distinct from their neighbors). The Local Moran statistic decomposes Moran's I (Moran 1950) ) into contributions for each location, Ii.. The sum of Ii for all observations is proportional to Moran's I, an indicator of global pattern. Thus, there can be two interpretations of Local Moran statistics, as indicators of local spatial clusters and as a diagnostic for outliers in global spatial patterns.

Local Moran

LISA: Significance map

LISA: Cluster map