The Space Time Pattern Mining toolbox contains statistical tools for analyzing data distributions and patterns in the context of both space and time. The toolbox contains toolsets for clustering analysis, forecasting, and tools that are helpful for visualizing the data stored in the space-time netCDF cube in both 2D and 3D. It also includes options for estimating and filling missing values in the data prior to cube creation.
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.
Using the tools in the Space Time Cube Creation toolset, you can summarize your data into a netCDF data structure that can then be used as input to tools in the Space Time Pattern Analysis and Time Series Forecasting toolsets. The data aggregated and summarized into the space-time cube must have time stamps but can come from many different formats such as a set of points, panel data, related tables, or multidimensional raster layers. When the space-time cube is created, initial summary statistics and trend are calculated.
Using the tools in the Space Time Cube Visualization toolset, you can visualize the variables stored in the space-time cube in 2D and 3D. These tools can be used to understand the structure of the cube and how the cube aggregation process works, as well as to visualize patterns over time at specific locations of interest.
Using the analytical and statistical tools in the Space Time Pattern Analysis toolset, you can identify patterns and interrogate the data in a space-time cube.
The tools in the Time Series Forecasting toolset allow you to forecast and estimate future values at locations in a space-time cube as well as evaluate and compare forecast models for each location. Various time series forecasting models are available, including simple curve fitting, exponential smoothing, and a forest-based method.
The Utilities toolset contains tools that allow perform a variety of data conversion tasks including tools that complete and estimate missing values in a dataset prior to the creation of a space-time cube and smooth time series data. The Fill Missing Values tool minimizes the impact of missing data (null values) on subsequent analyses. The Time Series Smoothing tool can help smooth out irregularities in order to better see patterns and trends in your data. These tools are designed to be used in conjunction with the other tools in the Space Time Pattern Mining toolbox.
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
- An overview of the Space Time Cube Creation toolset
- An overview of the Space Time Pattern Analysis toolset
- An overview of the Space Time Cube Visualization toolset
- An overview of the Time Series Forecasting toolset
- An overview of the Utilities toolset
- How Create Space Time Cube works
- How Emerging Hot Spot Analysis works
- How Local Outlier Analysis works
- How Time Series Clustering works
- Visualize the space-time cube
- Space Time Pattern Mining toolbox history