Using GeoDA
Software for Geographic Data Analysis
and Exploration
Developed by Luc Anselin
Arizona State University
School of Geography and Planning
geodatacenter.asu.edu
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Software for Spatial Analysis and Statistics
• ArcGIS 9 The most common GIS Software, but $$$$!
– Spatial Statistics Tools for point and polygon analysis
– Spatial Analyst tools for density kernel
– GeoStatistical Analyst Tools for interpolation of continuous surface data
• OpenGeoDA, Geographic Data Analysis by Luc Anselin now at Arizona State
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Download from: http://geodacenter.asu.edu/
Runs on Vista and Windows 7 (also MAC and UNIX)
Earlier version called GeoDA runs only on XP (0.9.5i_6)
Easy to use and has good graphic capabilities
• CrimeStat III download from
http://www.icpsr.umich.edu/NACJD/crimestat.html
– Standalone package, free for government and education use
– Calculates values for spatial statistics but no GIS graphics
– Good documentation and explanation of measures and concepts
• R Open Source statistical package,
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originally on UNIX but now has MS Windows version
Has the most extensive set of spatial statistical analyses
Difficult to use
Need to learn it if you are going to do major work in this area
• S-Plus the only commercial statistical package with good support for spatial
statistics
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– www.insightful.com
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GeoDA Overview
• GeoDA is a package for exploratory analysis of geographic data.
• Primarily analyzes polygon data, but can also do some things with
point data
• Has major capabilities not easily available elsewhere including:
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--creates spatial weights matrices with multiple options
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--linking and brushing between maps, histograms, scatter plots
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--calculates and maps Local Indices of Spatial Association (LISA
or local Moran’s I).
• standard multiple regression full diagnostics for spatial effects
• spatial autoregressive model for both spatial lag and spatial error
models
• Free. ArcGIS not required, but it does require a shapefile for data
input.
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Obtaining GeoDA Software
• The GeoDA program is on my Web site at:
www.utdallas.edu/~briggs or go to
http://geodacenter.asu.edu/
You will have to create a new user account
• download, unzip, and click the file OpenGeoDA.exe to
start the software
– This version (OpenGeoDA) runs on Vista and Windows 7
– Earlier version (GeoDA095i) only runs on XP
• it does have some “bugs” so some things may not work
or it may crash!
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Help and Documentation for GeoDA
• For help using OpenGeoDA, go to
http://geodacenter.asu.edu/
Click on Support tab
• For printable manuals, go to
www.utdallas.edu/~briggs and download geoDAdoc.zip
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Geoda_quickstart : 25 page quick start guide to using geoda (read first)
Geoda_spauto a quick guide to spatial autocorreletion measures (read next)
Geoda93_manual is a 125 page manual which fully documents the software
Geoda 95i_updates is a 64 page manual which covers bug fixes and
enhancements in the latest release
– Note, all the above are written for the earlier version GeoDa9.3, not
OpenGeoDa but differences are small
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OpenGeoDa Interface: 1 of 2
Display and Create
• File—open a shape file: it should also contain the data to analyze
• Edit—copy maps, and open new maps to compare
• Tools—create spatial weights matrices (very good)
create shapefiles: Thiessen polygons, centroids, etc
create shapefiles from .dbf containing X,Y coordinates
• Table—Open a table (>Promotion), joins, variable manipulation,
joins, etc.
To access more options, right click on any open window
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OpenGeoDa Interface: 2 of 2
Analyze
• Map—create many types of choropleth maps
• Explore—creates various non-spatial graphs of data
• Space—calculating Spatial Autocorrelation measures
• Methods—standard and spatial simple and multiple regression
• Options—lists options for the currently active window.
To access options, right click on an open window
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Data for Demo
www.utdallas.edu/~briggs
china.zip
geoDAdata.zip
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1. Use GeoDA to find the Centroids of the
Provinces of China
(Need ArcInfo to do this in ArcGIS, which is expensive. GeoDA
is free. )
--Input the provinces shapefile: File>Open Shape File China.shp
--Open the data table: Table>Promotion to see what is there
--Create centroids for each province:
Options> Add Centroids to Table
Place check mark in X coordinates and Y coordinates box, click OK
--X and Y centroid coordinates are added to the table
--to keep them permanently you need to save as new shapefile
Table> Save to Shapefile as China_Centroids.shp
--to close these files and start something new: File>Close All
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2. Create Thiessen Polygons for
Provinces of China
--use point file of province centroids created
--Start the tool: Tools>Shape>Points to Polygons
Input File: China_Centroids.shp
Output file: China_Thiessen.shp
Bounding Box: leave blank (establishes outer edges)
--click Create, then Close
--Display the Thiessen polygons
File>Open Shapefile> China_Thiessen.shp
If a map window is already open, use:
Edit>New map layer> China_Thiessen.shp
Result not good because of outer boundary problem
--to close these files and start something new: File>Close All
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3. Explore data with different maps
Illiteracy for Provinces of China
-- Input the provinces shapefile, with data:
File>Open Shape File ChinaData.shp
Map window opens showing China provinces
--To see the data: Table>Promotion
(variables are defined in the file: chinaProvinceData.xls)
--To map the data, right click on the map window and select
Map > Quantile
Select variable to map: 1st variable: Illiteracy (% illiterate)
(note: default variable via Edit>Select variable does not work)
--Multiple different choropleth maps available:
Quantile, percentile, box map, std dev, equal interval, natural break
choropleth map: color polygons based on variable value
--Draw a second map:
Edit>Duplicate map (to use the same data set)
Edit>New map layer (to use a different data set)
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Different Choropleth Map Types:
Always examine different map types and number of classes!
• quantile (note the frequency counts in the map legend!)
– classes have equal numbers (quantities) of observations
(equal areas under the frequency distribution)
– If use 4 categories called quartile (quarter) map
– Each has 25% of data
• equal interval (note the frequency counts!)
– classes are equal width on variable
– will have different numbers of
observations
(Assumes a Normal distribution)
25%
25%
23%
Equal area %s
23%
• standard deviation
– categories based on
– 1,2, etc, SDs
above/below mean
– Classes have different
numbers of observations
• natural breaks
14%
34%
34%
14%
Equal interval %s
Standard Deviation
-2
-1
0
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Equal interval score
-.68
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.68
– finds “natural groupings” by minimizing the
variance within each class using Jenks optimization.
Equal area score
Different Choropleth Map Types:
Identifying the extremes: Box Map
We are often interested in outliers: observations with very large or very
small (extreme) values
Box map examines extreme data values
Possibly no observations in the extreme categories
Map<Box with “hinge” = 1.5 :
• Similar to quantile map with 4 categories
• adds “extreme” categories for data with values which are 1.5 (or 3)
times the interquartile range
(difference between 25% and 75% percentiles)
• Extremes here are based on the data value itself.
– Maybe no observations in the extreme categories
– always look at the frequency counts in the legend
Different Choropleth Map Types:
Identifying the extremes: Percentile Map
We are often interested in outliers: observations with very large or
very small (extreme) values
Percentile map examines extreme percentages of data
Always have observations in the extreme categories
Map>Percentile with “hinge” = 1.5 (or 3):
• Similar to quantile map with 4 categories except
• Uses percentiles to identify extremes: top & bottom 1% & 10%.
– Extremes are the tails of the distribution.
• Extremes here are based on the data value itself.
– Always* have observations in these categories, but they may
not be extreme (*in theory, but sometimes not!)
4. Box Plots and Frequency Distributions
• Close all windows
• Explore>Box Plot
repeat for illiteracy, urban pop %, NatGrow05
• Explore>Histogram
repeat for illiteracy, urban pop %, NatGrow05
The Box Plot:
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all observations are positioned based on their value on the variable
the green asterisk is the median observation
The blue line is the mean
the colored center section shows the 25-75% percentile
the red T line in the upper part shows the location of upper “hinge”
(value which is 1.5 times the interquartile range above the mean)
the red  in the lower part shows the location of lower “hinge”
(value which is 1.5 times the interquartile range below the mean)
--sometimes both Ts are at the top & bottom of box (as in crime data), so no
observations are beyond the hinge
--sometimes no Ts show at all—if they are within the interquartile range
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5. Linking between maps and plots
• Edit>Duplicate Map to create map layer
• Right click, and select Map>Percentile
repeat for illiteracy, urban pop %, NatGrow05
(ignore warnings)
widen the legend box so that you can see frequency count
arrange boxes as illustrated
note that <1% has 0 observations for Urban pop, NatGrow
--the reason for warnings
Linking
• click a province on the map :
– it’s highlighted on other maps and plots!
– click a data point in a plot, it shows on the map
• If not, maybe it’s too small to see (e.g. Hong Kong): use zoom
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Warning about Missing Data
• Often, value for some observations on some variables are
missing
– e.g. for Macau, or the Taiwan islands near the Fujian coast
• Can cause big problems with results of analyses and with
plots (such as the box plot)
– Software often assumes value is zero
– Big mistake
• Observation should be:
– Omitted
– Insert average for the variable
– Use an estimate (provided you have evidence)
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6. Moran’s I and Lisa
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6.1 Create Spatial Weights Matrix
• Create File: Go to Tools>weights>create
Input file: chinadata.shp
Queen contiguity
Click Add ID Variable (using existing variable does not work)
Enter new variable name: Poly_ID
Click Save to DBF
Click—Yes, its safe
Click Create and name the file: ChinaData.gal
A new file ChinaData.gal is saved in the folder with
• Check File: Go to Tools>Weights>Properties
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Enter name of weights file
Histogram (frequency distribution) showing number of neighbors
Polygons with zero neighbors are potential problems (4 in this case)
Click on zero column and they are highlighted on map (Linking)
Open table (Table>Promote) and they are highlighted in table
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Format of .gal File
• .gal file is a .txt file:
– open with Notepad
Hainan
Macau
0 35 ChinaData POLY_ID
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21
30
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25 14 11 6 5 4
45
23 9 6 5 3
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30 14 13 9 4 3
First line: 4 items: 0, Number of observations,
filename, IDvariable
All subsequent lines are in sets of two:
ID, number of neighbors
List of neighbor IDs
ID, number of neighbors
List of neighbor IDs
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6.2 Calculate Moran’s I
Calculate Moran’s I: Space>Univariate Moran
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Variable: Illiteracy
Click OK
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Select Weight: ChinaData.gal Click OK
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Moran Scatterplot opens
– W_Illiteracy on vertical (Y) axis (neigbors)
– Illiteracy on X axis
• Moran’s I is .2047
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6.3 Statistical Significance via Simulation
Check Statistical Significance via Simulation:
• Right Click on scatterplot and select Options>Randomization
Select 999 permutations
Click Run for additional simulations and to check sensitivity of results
• If p-value < .05 then statistically significant
Note numbers at bottom:
– I: 0.2047: Morans I
– E(I) -0.294: Expected value
for Moran’s I if random (no SA)
• same for every simulation
– Mean: of the sampling distribution
– Sd: Standard Deviation of Sampling
Distribution (Standard Error)
– Change each simulation
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I=.2047
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6.4 Calculate Anselin’s LISA (Local Moran’s I)
• Calculate LISA: Go to Space>Univariate LISA
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Variable: Illiteracy
Click OK
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Weights: chinadata.gal
Click OK
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Place checks in top 2 boxes
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We discussed these maps in
our last lecture
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6.5 Saving Results of LISA Analysis
Save spatial lag and standardized (z scores) for variable analyzed
• Right-click Moran scatterplot and go to Save Results.
• Check the boxes you want
• Optionally, change the default
variable names
Save LISA scores, relationship type*, and probability level
• Right-click significance or cluster map and go to Save Results.
• Check the boxes you want
• Optionally, change the default
variable names
*1: high-high, 2: low-low, 3: low-high, 4: high-low
To permanently add the new variables to the table, right-click on the
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table and go to Save Shape File As....
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6.6 Recomputing Moran’s I for selected observations
The Moran's I slope and value can be recomputed for all observations
excluding the ones selected:
• Right-click on Moran scatterplot and choose Exclude Selected
Exclude selected observations
• Click an individual observation or drag a box and Moran’s I is
recomputed excluding selected observation(s)
– New value shown in red on top right
– Exclude observations also highlighted on maps
Exclude groups of observation by brushing
• Hold Ctrl key and draw a rectangle; release mouse, then release
Ctrl key; rectangle flashes;
• Use mouse to move rectangle across screen
• Moran’s I recalculated excluding observations within rectangle
Note: not a true Moran's I since lag-X not adjusted for excluded
observations.
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Hints on getting your data into geoda
• Data (variables) must be in a shapefile, or in a .dbf which
you join to the shapefile using Table>Join tables
• a shapefile also stores data in a .dbf file which you can
edit to add variables
How do I edit a .dbf file to add data?
• Use Excel 2003 or earlier
– You can save files from Excel in .dbf format
– Excel 2007 or later will read but not write .dbf files
• Use OpenOffice from Sun/Oracle
www.openoffice.org
An almost exact replica of Excel which is free
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Spatial data creation
• geoDA also contains some capabilities for
creating shapefiles: see Tools>shape
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What have we learned today?
How to use geoDA for
• general exploration of spatial data
• analysis of spatial autocorrelation
Next time
• spatial regression
• Then, using geoDA for spatial regression
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