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processor) is a loadable extension of Tcl which provides a powerful and efficient
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facility for processing data in the form of n-dimensional arrays. It has been
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designed to provide an array-processing facility with much of the functionality
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of languages such as <ahref="http://www.acm.org/sigapl/" >APL</a>, Fortran-90, <ahref="#IDL" >IDL</a>, <ahref="http://www.jsoftware.com/" >J</a>, <ahref="http://www.mathworks.com" >matlab</a>, and <ahref="http://www.octave.org/" >octave</a>.
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of languages such as <ahref="http://www.acm.org/sigapl/" >APL</a>, Fortran-90, <ahref="#IDL" >IDL</a>, <ahref="http://www.jsoftware.com/" >J</a>, <ahref="http://www.mathworks.com" >MATLAB</a>, and <ahref="http://www.octave.org/" >octave</a>.
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</p>
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<p>
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Support is provided for data based on n-dimensional grids, where the dimensions
In addition, this version contains a capability to geo-reference some data
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and to read ASCII data in tabular format. Also new is the ability to output
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data in numerical form (e.g. NetCDF) and a context sensitive, integrated help
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data in numerical form (e.g. netCDF) and a context sensitive, integrated help
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system.
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</p>
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<p>
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As with earlier versions, data in several different formats, including NetCDF,
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As with earlier versions, data in several different formats, including netCDF,
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can be read in easily from your local machine or from the Web. In addition,
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most data can be subset or subsampled on load, making it possible to visualize
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very large multidimensional and/or multispectral datasets. The package includes
@@ -3047,29 +3047,44 @@ <h1 id="commercial">Commercial or Licensed Packages</h1>
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<h2><aid="ArcGIS" name="ArcGIS">ArcGIS Pro - Space Time Pattern Mining Toolbox</a></h2>
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<p>
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The <ahref="https://pro.arcgis.com/en/pro-app/tool-reference/space-time-pattern-mining/an-overview-of-the-space-time-pattern-mining-toolbox.htm">Space Time Pattern Mining toolbox</a> contains statistical tools for analyzing data distributions and patterns in the context of both space and time. It includes a toolset for visualizing the data stored in the space-time netCDF cube in both 2D and 3D.
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The <ahref="https://pro.arcgis.com/en/pro-app/tool-reference/space-time-pattern-mining/an-overview-of-the-space-time-pattern-mining-toolbox.htm">Space Time Pattern Mining toolbox</a>
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contains statistical tools for analyzing data distributions and patterns in the context of both space and time. It includes a toolset for visualizing the data stored in the space-time netCDF cube in both 2D and 3D.
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Create Space Time Cube takes point datasets and builds a multidimensional cube data structure (netCDF) for analysis. Emerging Hot Spot Analysis then takes the cube as input and identifies statistically significant hot and cold spot trends over time. You might use the Emerging Hot Spot Analysis tool to analyze crime or disease outbreak data in order to locate new, intensifying, persistent, or sporadic hot spot patterns at different time-step intervals. The Local Outlier Analysis tool takes the cube as input to identify statistically significant clusters of high or low values as well as outliers that have values that are statistically different than their neighbors in space and time. The Utilities toolset enables you to visualize the data and analysis results stored in the space-time cube in two and three dimensions. These visualization tools can be used to understand the structure of the cube, how the cube aggregation process works, and to visualize the analytical results added to the cube by other Space Time Pattern Mining tools. See Visualizing the Space Time Cube for strategies to allow you to look at cube contents.
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Create Space Time Cube takes point datasets and builds a multidimensional cube data structure (netCDF) for analysis.
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Emerging Hot Spot Analysis then takes the cube as input and identifies statistically significant hot and cold spot
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trends over time. You might use the Emerging Hot Spot Analysis tool to analyze crime or disease outbreak data in
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order to locate new, intensifying, persistent, or sporadic hot spot patterns at different time-step intervals.
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</p>
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<p>
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The Local Outlier Analysis tool takes the cube as input to identify statistically significant clusters of high or
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low values as well as outliers that have values that are statistically different than their neighbors in space and time.
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</p>
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<p>
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The Utilities toolset enables you to visualize the data and analysis results stored in the space-time cube in two and
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three dimensions.These visualization tools can be used to understand the structure of the cube, how the cube aggregation
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process works, and to visualize the analytical results added to the cube by other Space Time Pattern Mining tools.
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See Visualizing the Space Time Cube for strategies to allow you to look at cube contents.
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