![]() For instance, the option to visualize Hot and cold spot results is available if you have run Emerging Hot Spot Analysis, and the option to visualize Cluster and outlier results is available if you have run Local Outlier Analysis on that particular variable. You also have many other options depending on the analysis tools you have run on the space-time cube. The Value option allows you to see the raw numbers associated with aggregation or creation of the cube. ![]() Choose a Display Theme option for the selected cube variable.After Create Space Time Cube By Aggregating Points, Create Space Time Cube From Defined Locations, or Create Space Time Cube From Multidimensional Raster Layer is run, variables stored in the cube include count as well as any summary fields or variables that you chose to aggregate when creating the cube. Open the Visualize Space Time Cube in 3D tool.Note:The next time a new scene is added, the default surfaces populate again. To do this, turn off the default elevation surfaces by clicking off any Ground layers that appear in the Elevation Surfaces group in the Contents pane. Because time is used as the vertical axis in visualizing the space-time cube, it is important for accurate interpretation that all locations on the ground be at the same elevation so all time-step intervals start at the same base. To open a scene so the results of the tool can be rendered in 3D, click the Insert tab, click New Map, and choose a new global or local scene. To visualize the space-time cube in 3D, complete the following steps: Additionally, visualizing the value of the summary fields, variables, or other display themes can help you understand how confident you can be in subsequent analyses by displaying the spatial pattern of empty bins that had to be estimated or temporal outliers in your analysis.It can also offer insights into the results of Emerging Hot Spot Analysis, Local Outlier Analysis, and the other Space Time Pattern Mining analysis tools, providing evidence that can help you understand the results. ![]() It can help you understand the structure of the space-time cube and how the process of aggregation into the cube works.Visualization of the cube is useful for several reasons: The space-time cube can be visualized and explored in three dimensions in a 3D scene. The data and variables stored in the space-time netCDF cube can be visualized in either two or three dimensions using the Visualize Space Time Cube in 2D or Visualize Space Time Cube in 3D tools located in the Utilities toolset. The netCDF cube is updated with the results of these analyses in the form of several new variables. There are a variety of forecast methods in the Time Series Forecasting toolset that allow you to forecast and estimate future values and identify temporal outliers as well as a tool to help you evaluate and compare the different forecast models created. The Time Series Clustering tool identifies the locations in the space-time cube that are most similar and partitions them into distinct clusters in which members of each cluster have similar time series characteristics. The Local Outlier Analysis tool also takes the netCDF cube as input and runs an interpretation of the Local Moran's I statistic to identify statistically significant clusters and outliers in the context of both space and time. The Emerging Hot Spot Analysis tool takes the netCDF cube as input runs a space-time hot spot analysis ( Getis-Ord Gi* statistic) and identifies trends in the aggregated count data, summary fields, or variables, such as new, intensifying, diminishing, and sporadic hot and cold spots. There are a variety of analysis tools in the Space Time Pattern Mining toolbox that you can run against the cube after it is created. Additionally, the cube may contain one or more summary fields or variables with statistics for the attribute fields specified in each bin. ![]() Each bin in the cube contains a value, or count, of the number of events that occurred at the bin location for the time-step interval specified. The Create Space Time Cube By Aggregating Points, Create Space Time Cube From Defined Locations, and Create Space Time Cube From Multidimensional Raster Layer tools structure and summarize datasets into a netCDF data format by creating space-time bins that form a cube-like structure. Explore 3D results using the time slider.
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