1. Using GeoDA geodatacenter.asu.edu
Software for Geographic Data Analysis
and Exploration
Developed by Luc Anselin
Arizona State University
School of Geography and Planning
geodatacenter.asu.edu
Briggs Henan University 2010

2. 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
Download from:
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
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,
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
Briggs Henan University 2010

3. Briggs Henan University 2010
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:
--creates spatial weights matrices with multiple options
--linking and brushing between maps, histograms, scatter plots
--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.
Briggs Henan University 2010

4. Obtaining GeoDA Software
The GeoDA program is on my Web site at:
or go to
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!
Briggs Henan University 2010

5. Help and Documentation for GeoDA
For help using OpenGeoDA, go to
Click on Support tab
For printable manuals, go to
and download geoDAdoc.zip
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
Briggs Henan University 2010

6. 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
Briggs Henan University 2010

7. 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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8. Briggs Henan University 2010
Data for Demo
china.zip
geoDAdata.zip
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9. 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
Briggs Henan University 2010

10. 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
Briggs Henan University 2010

11. 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)

12. 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
standard deviation
categories based on
1,2, etc, SDs
above/below mean
Classes have different
numbers of observations -1 1 -2 2
34%
14%
.68
-.68
23%
25%
(Assumes a Normal distribution)
Standard Deviation
Equal area %s
Equal interval %s
Equal interval score
Equal area score
natural breaks
finds “natural groupings” by minimizing the
variance within each class using Jenks optimization.
Percentile
Similar to quantile with 4 cabut
Box map

13. 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

14. 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!)

15. 4. Box Plots and Frequency Distributions
Close all windows
Explore>Box Plot
repeat for illiteracy, urban pop %, NatGrow05
Explore>Histogram
The Box Plot:
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
Briggs Henan University 2010

16. 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
Briggs Henan University 2010

17. 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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18. Briggs Henan University 2010

19. Briggs Henan University 2010

20. Briggs Henan University 2010
6. Moran’s I and Lisa
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21. 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
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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22. Briggs Henan University 2010
Format of .gal File
.gal file is a .txt file:
open with Notepad
0 35 ChinaData POLY_ID
1 0
2 1 30
3 6
4 5
5 6
Hainan
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
Macau
Briggs Henan University 2010

23. Briggs Henan University 2010
6.2 Calculate Moran’s I
Calculate Moran’s I: Space>Univariate Moran
Variable: Illiteracy Click OK
Select Weight: ChinaData.gal Click OK
Moran Scatterplot opens
W_Illiteracy on vertical (Y) axis (neigbors)
Illiteracy on X axis
Moran’s I is .2047
Briggs Henan University 2010

24. 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: : Morans I
E(I) : 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
I=.2047
Briggs Henan University 2010

25. 6.4 Calculate Anselin’s LISA (Local Moran’s I)
Calculate LISA: Go to Space>Univariate LISA
Variable: Illiteracy Click OK
Weights: chinadata.gal Click OK
Place checks in top 2 boxes
We discussed these maps in
our last lecture
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26. 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 table and go to Save Shape File As....
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27. 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.
Briggs Henan University 2010

28. 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
An almost exact replica of Excel which is free
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29. Briggs Henan University 2010
Spatial data creation
geoDA also contains some capabilities for creating shapefiles: see Tools>shape
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30. 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
Briggs Henan University 2010