Using the analytical and statistical tools in the Space Time Pattern Analysis toolset, you can identify patterns and interrogate the data in your space-time cube.
After the creation of the space-time cube, these analysis tools can provide a deeper understanding of the data aggregated into the cube. The Emerging Hot Spot Analysis tool uses a cube as input and identifies statistically significant hot and cold spot trends over time. You can use the this tool to analyze crime or disease outbreak data to locate new, intensifying, persistent, or sporadic hot spot patterns at different time-step intervals. The Local Outlier Analysis tool uses 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 Time Series Clustering tool partitions the locations in a space-time cube into distinct clusters in which members of each cluster have similar time series characteristics.
See Visualize the space-time cube for strategies that allow you to view cube contents.
The Space Time Cube Explorer add-in available on the Spatial Statistics Resources page can also be used to visualize space-time cube contents and analysis results in 2D and 3D by automatically setting up time and range sliders and providing a variety of display theme options.
Identifies trends in the clustering of point densities (counts) or values in a space-time cube created using either the Create Space Time Cube By Aggregating Points, Create Space Time Cube From Defined Locations or Create Space Time Cube from Multidimensional Raster Layer tool. Categories include new, consecutive, intensifying, persistent, diminishing, sporadic, oscillating, and historical hot and cold spots.
Identifies statistically significant clusters and outliers in the context of both space and time. This tool is a space-time implementation of the Anselin Local Moran's I statistic.
Partitions a collection of time series, stored in a space-time cube, based on the similarity of time series characteristics. Time series can be clustered based on three criteria: having similar values across time, tending to increase and decrease at the same time, and having similar repeating patterns. The output of this tool is a 2D map displaying each location in the cube symbolized by cluster membership and messages. The output also includes charts containing information about the representative time series signature for each cluster.
The Spatial Statistics Resources page at https://www.esriurl.com/spatialstats contains a variety of resources to help you use the Spatial Statistics and Space Time Pattern Mining tools, including the following:
- Hands-on tutorials and Learn lessons
- Workshop videos and presentations
- Training and web seminars
- Links to books, articles, and technical papers
- Sample scripts and case studies