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<!DOCTYPE html>
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<head>
<meta charset="UTF-8">
<title>GeoDa on Github</title>
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<section class="page-header">
<h1 class="project-name">GeoDa</h1>
<h2 class="project-tagline">An Introduction to Spatial Data Science</h2>
<a href="//geodacenter.github.io/download.html" class="btn">Download</a>
<a href="https://github.com/GeoDaCenter/geoda/" class="btn">View on GitHub</a>
<a href="https://geodacenter.github.io/data-and-lab/" target="_blank" class="btn">Data</a>
<a href="//geodacenter.github.io/cheatsheet.html" class="btn">Cheat Sheet</a>
<a href="documentation.html" class="btn">Documentation</a>
<a href="//geodacenter.github.io/support.html" class="btn">Support</a>
<a href="//geodacenter.github.io/index-cn.html" class="btn">中文</a>
</section>
<section class="main-content">
<h3>
<a id="welcome-to-github-pages" class="anchor" href="#welcome-to-github-pages" aria-hidden="true"><span
class="octicon octicon-link"></span></a>Introducing GeoDa 1.22
</h3>
<p>GeoDa is a free and open source software tool that serves as an introduction to spatial data science. It is
designed to facilitate new insights from data analysis by exploring and modeling spatial patterns.</p>
<div style="float: right; clear: right">
<iframe width="360" height="215" src="https://www.youtube.com/embed/--8vhhmpgdM" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture"
allowfullscreen></iframe>
</div>
<p>GeoDa was developed by <a href="https://en.wikipedia.org/wiki/Luc_Anselin">Dr. Luc Anselin</a> and his <a
href=" https://spatial.uchicago.edu/directories/full/team">team</a>. The program provides a user-friendly and
graphical interface to methods of exploratory spatial data analysis (ESDA), such as spatial autocorrelation
statistics for aggregate data (several thousand records), and basic spatial regression analysis for point and
polygon data (tens of thousands of records). To work with big data in GeoDa it should first be aggregated to areal
units.</p>
<p>Since its initial release in February 2003, GeoDa's user numbers have increased exponentially to over 520,000
(June 2022). This includes lab users at universities such as Harvard, MIT, and Cornell. The user community and
press embraced the program enthusiastically, calling it a "hugely important analytic tool," a "very fine piece of
software," and an "exciting development."</p>
<p>The latest version 1.22 contains multi-layer support, several new local cluster features, including <a
href="docs/LA_multivariateGeary1.pdf" target="_blank">univariate and multivariate local Geary cluster maps</a>,
redcap, skater, spectral clustering and max-p, and local join count maps for categorical data. It also implements
several classic non-spatial cluster techniques (principal component analysis, k-means, and hierarchical
clustering) implemented in <a href="https://bonsai.hgc.jp/~mdehoon/software/cluster/cluster.pdf"
target="_blank">Hoon et al.'s (2013) C Clustering Library</a>, as well as HDBScan.</p>
<p>A new workbook is under development. In the meantime, <a href="documentation.html">here are interim
resources</a>, including an overview of features in 1.22.</p>
<br />
<h3>
<a id="intro-toolbar" class="anchor" href="#intro-toolbar" aria-hidden="true"><span
class="octicon octicon-link"></span></a>GeoDa 1.22
</h3>
<p>
<div class="bss-slides num2" tabindex="2">
<figure>
<img src="images/intro1.png" class="shadowfilter intro1" width="100%" />
<figcaption>Mac OSX El Capitan</figcaption>
</figure>
<figure>
<img src="images/intro2.png" class="shadowfilter intro1" width="100%" />
<figcaption>Windows 7/8/10</figcaption>
</figure>
<figure>
<img src="images/intro3.png" class="shadowfilter intro1" width="100%" />
<figcaption>Ubuntu 14.04</figcaption>
</figure>
</div> <!-- // bss-slides -->
<script src="javascripts/better-simple-slideshow.min.js"></script>
<script>
var opts = {
auto: { speed: 5000, pauseOnHover: true },
fullScreen: false,
swipe: true
};
makeBSS('.num2', opts);
</script>
</p>
<p>GeoDa runs on Windows, MacOSX and Linux (Ubuntu)</p>
<br />
<h3>
<a id="intro-data-formats" class="anchor" href="#intro-data-formats" aria-hidden="true"><span
class="octicon octicon-link"></span></a>GeoDa Now Supports More Spatial Data Formats
</h3>
<p>GeoDa now supports a larger variety of vector data in different formats (click <a href="formats.html">here</a> to
see the details): You can work with shapefiles, geodatabases, GeoJSON, MapInfo, GML, KML, and other vector data
formats supported by the GDAL library. The program also converts coordinates in table format (.csv, .dbf, .xls,
.ods) to one of these spatial data formats and converts data between different file formats (such as .csv to .dbf
or shapefile to GeoJSON). Selecting a subset and exporting it as a new file is now also possible.</p>
<p><img src="images/dataformats.png" class="shadowfilter"></p>
<br />
<h3>
<a id="intro-data-formats" class="anchor" href="#intro-data-formats" aria-hidden="true"><span
class="octicon octicon-link"></span></a>Now With Multi-layer Support!
</h3>
<p>For the first time, you can now load additional layers into Geoda for visualization purposes. The analysis will
still be done on the layer you load first. In this example, the map shows transit access from housing blocks, with
the transit station locations as an additional layer.</p>
<p><img src="images/multi-layer.png" class="shadowfilter"></p>
<br />
<h3>
<a id="intro-esda" class="anchor" href="#intro-esda" aria-hidden="true"><span
class="octicon octicon-link"></span></a>Explore Statistical Results through Linked Maps and Charts
</h3>
<p>In contrast to programs that visualize raw data in maps, GeoDa focuses on exploring the results of statistical
tests and models through linked maps and charts.</p>
<p><img src="images/esda.png" class="shadowfilter"></p>
<br />
<h3>
<a id="intro-time" class="anchor" href="#intro-time" aria-hidden="true"><span
class="octicon octicon-link"></span></a>Analyze Spatial and Temporal Patterns Across Linked Views
</h3>
<p>You can now group the same variable across time periods in the new Time Editor to explore statistical patterns
across space and time. Then explore results as views change over time with the Time Player.</p>
<p><img src="images/time.png" class="shadowfilter"></p>
<h3>
<a id="intro-ui" class="anchor" href="#intro-ui" aria-hidden="true"><span
class="octicon octicon-link"></span></a>Ground-Truth Map Results with Basemaps
</h3>
<p>If your spatial data are projected (.prj file), you can now add a basemap to any map view, including cluster
maps, for better orientation and for ground-truthing results.</p>
<p>
<img src="images/intro4.png" class="shadowfilter fiximage" width=832>
</p>
<br />
<h3>
<a id="intro-avg" class="anchor" href="#intro-avg" aria-hidden="true"><span
class="octicon octicon-link"></span></a>Compare Averages Across Time and Space
</h3>
<p>A new Averages Chart compares values that are averaged over time and/or space and tests if the differences in
these means are significant. For instance, first select if you want to compare means of selected vs. unselected
observations in the same time period or compare all observations for different time periods. A basic
pre-post/impact-control test then indicates if your results changed over time and space (using an F-test and
difference-in-difference test).</p>
<p><img src="images/avgchart.png" class="shadowfilter"></p>
<br />
<h3>
<a id="intro-sp-matrix" class="anchor" href="#intro-sp-matrix" aria-hidden="true"><span
class="octicon octicon-link"></span></a>Detect Relationships in Multivariate Space
</h3>
<p>A scatter plot matrix allows you to explore multiple bivariate correlations at once. In this example, the
regression slopes for selected, unselected and all police precincts in San Francisco are shown to explore
relationships between four types of crime.</p>
<p><img src="images/scatter_matrix.png" class="shadowfilter"></p>
<br />
<h3>
<a id="intro-diff-mi" class="anchor" href="#intro-diff-mi" aria-hidden="true"><span
class="octicon octicon-link"></span></a>Find Statistically Significant Spatial Clusters
</h3>
<p>GeoDa has long supported uni-and bivariate local tests of spatial autocorrelation like local Moran. Now the
program also includes local G/G*, and a variety of local join count statistics for categorical data. In this
example, local Moran cluster maps identify higher % GOP votes in central US areas in both the 2012 and 2016
presidential elections (left). The colocation join count map (top right) shows which of the high-high cluster
values in both years overlapped in space while the differential local Moran map reveals clusters in % point
differences between 2016 and 2012 (bottom right) .</p>
<p><img src="images/Lisa.png" class="shadowfilter"></p>
<br />
<h3>
<a id="intro-diff-mi" class="anchor" href="#intro-diff-mi" aria-hidden="true"><span
class="octicon octicon-link"></span></a>Compare a Suite of Spatially Constrained Cluster Techniques
</h3>
<p>GeoDa now has lots of new techniques to identify clusters with spatial constraints, including skater, redcap,
max-p, k-means, k-medians, k-medoids, and spectral clustering. Here are a few examples of how foreign-born white
residents, foreign-born Hispanic residents and median monthly rents in 2008-2009 in New York are clustered.</p>
<p><img src="images/sc_clusters.png" class="shadowfilter"></p>
<br />
<h3>
<a id="intro-diff-mi" class="anchor" href="#intro-diff-mi" aria-hidden="true"><span
class="octicon octicon-link"></span></a>Determine if Changes Over Time Are Spatially Clustered
</h3>
<p>Use a global or local Differential Moran's I test to find out if a variable's change over time in a given
location is statistically related to that of its neighbors. For instance, this local (LISA) cluster map shows
hotspots in New York with larger changes in the share of kids between 2002 and 2008 (and coldspots with smaller
changes).</p>
<p><img src="images/DiffMI.png" class="shadowfilter"></p>
<br />
<h3>
<a id="intro-diff-mi" class="anchor" href="#intro-diff-mi" aria-hidden="true"><span
class="octicon octicon-link"></span></a>Test if Multiple Variables Are Clustered in Space
</h3>
<p><a href="docs/LA_multivariateGeary1.pdf" target="_blank">Luc Anselin (2017)</a> recently extended Geary's c with
a new local indicator of spatial association. This is applied to the classic data set of "moral statistics" of
France (Guerry, 1833) to show significant high and low spatial concentrations of literacy (left map) and
significant associations of property crime and literacy (right map).</p>
<p><img src="images/localGeary.png" class="shadowfilter"></p>
<br />
<h3>
<a id="intro-diff-mi" class="anchor" href="#intro-diff-mi" aria-hidden="true"><span
class="octicon octicon-link"></span></a>Map Patterns of Non-Spatial Cluster Statistics
</h3>
<p>You can now map patterns of several classic non-spatial cluster techniques, including principal component
analysis (left maps), k-means (top right), and hierarchical clustering (bottom right) and multi-dimensional
scaling. Using the same data as in the example above, the maps below show local clusters of property crime,
literacy, and suicide.</p>
<p><img src="images/nonspatial_clusters.png" class="shadowfilter"></p>
<br />
<h3>
<a id="intro-corr" class="anchor" href="#intro-corr" aria-hidden="true"><span
class="octicon octicon-link"></span></a>Find the Threshold Where Spatial Correlation Ends
</h3>
<p>A nonparametric spatial autocorrelation test (correlogram) is now available to determine distance thresholds when
the values of neighboring pairs are no longer correlated.</p>
<p><img src="images/corr.png" class="shadowfilter"></p>
<br />
<h3>
<a id="intro-cat" class="anchor" href="#intro-cat" aria-hidden="true"><span
class="octicon octicon-link"></span></a>Explore the Impact of Flexible Data Categorization
</h3>
<p>With the new category editor, you can explore how sensitive your results are to changes in the thresholds that
categorize your data. In this example the thresholds in the conditional map (right) are based on the categories
that can be adjusted in the category editor (left).</p>
<p><img src="images/cat_editor.png" class="shadowfilter"></p>
<h3>
<a id="dependencies" class="anchor" href="#dependencies" aria-hidden="true"><span
class="octicon octicon-link"></span></a>Dependencies
</h3>
<p>GeoDa is released under a GPL license. It builds on several open source libraries and source-code files. Below is
the list of the key projects that we would like to acknowledge.</p>
<font style="font-size:small">
<p>
GDAL Libraries, version 1.10.
License: X/MIT style Open Source license.
Authors: Many.
Links: https://www.gdal.org/
</p>
<p>Boost Libraries, version 1.53.
License: Boost Software License - Version 1.0.
Authors: Many.
Links: https://www.boost.org/
<p>wxWidgets Cross-Platform GUI Library, version 2.9.4.
License: The wxWindows Library Licence.
Authors: Julian Smart, Robert Roebling, and others.
Links: <a href="https://www.wxwidgets.org/">https://www.wxwidgets.org/</a>
<p>CLAPACK Linear Algebra Libraries, version 3.2.1.
Authors: Many.
License: Custom by University of Tennessee.
Links: <a href="https://www.netlib.org/clapack/">https://www.netlib.org/clapack/</a>
<p>Approximate Nearest Neighbor Library, version 0.1.
Note: Full source of 0.1 release included in kNN directory.
Authors: Sunil Arya and David Mount.
License: See kNN/AHH.h in included source files.
Links: <a href="https://www.cs.umd.edu/%7Emount/ANN/">https://www.cs.umd.edu/~mount/ANN/</a></p>
<p>FastArea.c++ source code.
Note: The source for functions findArea and
ComputeArea2D are in the file GenGeomAlgs.h from FastArea.c++
in Journal of Graphics Tools, 7(2):9-13, 2002
Author: Daniel Sunday.
License: Unknown.
Links: <a
href="https://jgt.akpeters.com/papers/Sunday02/FastArea.html">https://jgt.akpeters.com/papers/Sunday02/FastArea.html</a>
</p>
<p>logger.h source code.
Author: Seweryn Habdank-Wojewodzki.
Note: We have copied the source for logger.h and modified it slightly
to work with wxString.
License: Boost Software License - Version 1.0.
Links: <a href="https://accu.org/index.php/journals/1304">https://accu.org/index.php/journals/1304</a></p>
<p>nullstream.h source code.
Author: Maciej Sobczak.
License: See logger.h in included source files.
Links: <a href="https://www.msobczak.com/">https://www.msobczak.com/</a></p>
<p>The C Clustering Library.
Authors: Hoon, Michiel de, Seiya Imoto, Satoru Miyano. (2013). The University of Tokyo, Institute of Medical
Science, Human Genome Center.
License: Python License.
Links: <a href="https://bonsai.hgc.jp/~mdehoon/software/cluster/cluster.pdf" target="_blank">The C Clustering
Library.</a></p>
</font>
<br />
<h3>
<a id="intro-ackn" class="anchor" href="#intro-ackn" aria-hidden="true"><span
class="octicon octicon-link"></span></a>Acknowledgments
</h3>
<p>The development of GeoDa has most recently been supported by the National Science Foundation, the National
Institutes of Health, the National Institute of Justice, and the Agency for Healthcare Research and Quality.</p>
<h3>
<a id="intro-support" class="anchor" href="#intro-support" aria-hidden="true"><span
class="octicon octicon-link"></span></a>Support
</h3>
<p>We are currently updating the <a href="documentation.html">documentation</a> to reflect the new features in GeoDa
1.22.
The <a href="support.html">Openspace listserv</a> supports technical questions about GeoDa.</p>
<h3>
<a id="intro-license" class="anchor" href="#intro-license" aria-hidden="true"><span
class="octicon octicon-link"></span></a>License
</h3>
<p>GeoDa uses a <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GPL License (General Public License)</a>.</p>
<h3>
<a id="intro-contact" class="anchor" href="#intro-contact" aria-hidden="true"><span
class="octicon octicon-link"></span></a>Contact
</h3>
<p>Questions? Contact <a href="mailto:[email protected]">us</a>.</p>
<h3>
<a id="intro-donate" class="anchor" href="#intro-donate" aria-hidden="true"><span
class="octicon octicon-link"></span></a>Donate
</h3>
<p>Help us keep GeoDa free by contributing <a
href="https://giving.uchicago.edu/site/Donation2?1838.donation=form1&df_id=1838&mfc_pref=T&set.Designee=1901">here</a><br />
Thank you for supporting free and open-source spatial software! <br /><a
href="https://giving.uchicago.edu/site/Donation2?1838.donation=form1&df_id=1838&mfc_pref=T&set.Designee=1901"><img
src="images/donate.png"></a></p>
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