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I am searching for spatial statistics software which has many powerful features and supports a GIS interface. Can anyone give me some advice?

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What are you going to use it for? –  R.K. May 17 '12 at 15:59
    
Good question, @R.K. If a specific application is not sought, then there is no basis to select a best answer and the thread should be made CW. –  whuber May 23 '12 at 13:22
    
I notice that you have not accepted any of the answers offered. An alternative site within SE that you may want to consider when asking for software recommendations is Software Recommendations (beta). –  PolyGeo Jun 29 at 3:54
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7 Answers 7

In a nutshell: Start with QGIS.

There are several Free and Open Source tools for geospatial statistics.

QGIS (Quantum GIS)

There are several spatial statistics plugins in Quantum GIS, such as

  • fTools: Tools for vector data analysis and management
  • Zonal Statistics: Extended zonal statistics and report generation
  • manageR: Interface to the R statistical programming language
  • Landscape Ecology Statistics: Contains several analytical functions for land cover analysis
  • Live Statistics: display simple statistics about vector data in small toolbars that provide real-time feedback.
  • Statist: Calculate and show statistics for a field

Linfinity has provided a brief tutorial covering QGIS Spatial Stastics.

Anita Graser has written a tutorial on the QGIS Group Statistics plugin.

GGIS Processing (model builder)

QGIS 2.0 Processing is a graphical modelling environment that integrates with several prominent projects, including SAGA and GRASS, some of which contain various statistical algorithms.

System for Automated Geoscientific Analysis

SAGA has over 400 modules for geoprocessing and statistical analysis.

GRASS

GRASS contains several zonal statistics functions.

R Language

The R Language has several spatial extensions.

Spatstat

The Spatstat module for R provides a host of spatial statistics functions.

R Studio

R Studio is a very nice IDE for the R Language, and will help you to easily locate and install the R spatial libraries.

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Concerning the "GIS interface" R offers (covering the first three options here), please see the comment thread to the earlier reply about R. –  whuber May 17 '12 at 18:03
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GeoDa is free, cross-platform software designed for dynamic visualization, exploratory spatial data analysis, and spatial statistics. It has been around for almost 15 years (starting as an ArcView 3.x extension, it was recoded to be independent of ArcView after ESRI abandoned the old AV architecture). It is associated with an illustrious group of GIS educators and researchers.

Screenshot

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Very interesting, strange that it stayed outside my radar for such a long time. Pity they don't have a license that is compatible with open-source projects. –  johanvdw May 18 '12 at 7:31
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It's open source now. code.google.com/p/opengeoda –  R.K. Sep 9 '12 at 18:01
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See also GRASS and R integration:

http://grass.osgeo.org/wiki/R_statistics

For example boxplots: enter image description here enter image description here

... or decision trees (rpart example): enter image description here

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ArcGIS has Spatial Statistics Toolbox for statical information. it analyze spatial distributions, patterns, processes, and relationships.

Spatial statistics allow you to:

Summarize the key characteristics of a distribution. 
Identify statistically significant spatial clusters (hot spots/cold spots) 
and spatial outliers. 
Assess overall patterns of clustering or dispersion. 
Model spatial relationships.

you can get information here.

spatial1

spatial2

spatial3

spatial4

i hope it helps you...

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A new software that is now also available is insensa GIS at www.insensa.org, "free and open source software for statistical computing and display of GIS data." You can also e.g. calculate correlations, view scatterplots ... In case the function you want to use is not available, you may also write your own plugin.

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Hi Lisa, welcome to our community! Thank you for sharing your information. –  whuber Jul 16 '12 at 12:49
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There are few interesting packages from the Spatial Data Mining and Visual Analytics Lab, especially:

  • EntroMap: Detecting Spatially Varying Multivariate Relationships
  • VIS-STAMP: A Visualization System for Space-Time and Multivariate Patterns
  • SOMVIS: Multivariate Mapping and Visualization

S4 research group also has few useful tools, especially:


CrimeStat is also worth having a look. Although focused on crime analysis, the functionality can be adapted to other purposes.


SANET is an ArcGIS toolset for analysis based on networks.


GeoSOM is a package for creating spatial Self-Organizing Maps.


Although lacking any mapping capabilities SaTScan is an essential tool for exploration of spatial and spatio-temporal clusters. Fast Spatial Scan from The Auton Lab can cope with larger datasets.

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+1 Interesting collection with some powerful specialized capabilities. Thanks for assembling these links. –  whuber May 25 '12 at 20:39
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Pls. refer this,

http://cran.r-project.org/web/views/Spatial.html

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We're looking for long answers that provide some explanation and context. Don't just give a one-line answer; explain why your answer is right, ideally with citations. Answers that don't include explanations may be removed.

    
This indeed has powerful features--but no "GIS interface" AFAIK. –  whuber May 17 '12 at 15:38
    
@whuber - Does "GIS interface" necessarily mean "Graphical User Interface for GIS"? While the human-application interface is command line, R can (and does) graphically present the data to us. –  Simbamangu May 17 '12 at 16:50
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@Simba I doubt most people would take a text command line to be a "GIS interface." It's really an on-the-fly interpreted programming interface. R doesn't easily allow the kind of direct interaction with mapped data one expects of a GIS. Indeed, GeoDa represents perhaps the sharpest possible contrast: not only does it allow direct interaction with graphical representations of data (to select subsets, for instance), it also dynamically transmits those effects to all related graphics in the window. This powerful exploratory statistical interface is very hard to achieve in R (if at all). –  whuber May 17 '12 at 18:01
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