Multidimensional data represent geophysical, environmental, climatological, or atmospheric phenomena that occur over space (two dimensional), time (another dimension), and/or height or depth (two more dimensions). Sometimes described as a data cube, image cube, or multidimensional raster, these datasets can also contain multiple variables such as precipitation, soil moisture, and temperature. Multidimensional raster data can be generated from satellite observations captured at different time intervals, or it can be generated from numerical models that aggregate, interpolate, or simulate data from other sources.
Because multidimensional data is so complex, it can be difficult to extract actionable information from it. ArcGIS offers powerful tools and user experiences to help you analyze multidimensional data.
A contextual tab in ArcGIS Pro provides an intuitive user experience to work with multiple variables and step through time and depth dimensions. Functions and tools for analyzing multidimensional data let you aggregate your data into by time, height, or depth (yearly averages, for example); generate multidimensional anomalies, where each slice shows the deviations from the global average; generate trend rasters and perform predictive modeling; or build a multidimensional transpose to optimize performance when accessing data along dimensions. You can also quickly chart multidimensional data using the temporal profile, including spatial aggregation and charting trends.
In addition to the user interface and geoprocessing tools for multidimensional raster analysis in ArcGIS Pro, ArcPy and the ArcGIS API for Python also support developer (and automated) workflows.
Explore the following resources to learn more about analyzing multidimensional data in ArcGIS. (Not sure where to start? Look for the star by Esri's most helpful resources.)
Note:In ArcGIS Pro, all multidimensional imagery analysis tools are available through ArcGIS Image Analyst, and a subset of these tools are available with ArcGIS Spatial Analyst.
Imagery Workflows resources
Community-supported tools and best practices for working with and automating imagery and remote sensing workflows:
- Follow a tutorial analyzing sea surface temperature using multidimensional analysis tools in ArcGIS Pro.*
Reference material for ArcGIS Pro, ArcGIS Online, and ArcGIS Enterprise:
ArcGIS blogs, articles, story maps, and white papers
Supplemental guidance about concepts, software functionality, and workflows:
Esri-produced videos that clarify and demonstrate concepts, software functionality, and workflows:
- Watch an overview of multidimensional analysis and visualization tools introduced in ArcGIS Pro 2.5 for tasks like data aggregation, anomaly detection, trend analysis and predictive analysis. (8 mins)*
- Watch examples of multidimensional data management and spatial and temporal analyses in ArcGIS, and how to extend them using Python. (1 hour)*
- Take a look at multidimensional analysis capabilities in ArcGIS Pro, including geoprocessing tools, ArcPy, and raster functions. (4 minutes)
Guided, hands-on lessons based on real-world problems:
- Use multidimensional data to predict coral bleaching events.
- Use multidimensional data to model weather conditions over time for oil leasing sites.
- Visualize social distancing across California as a multidimensional voxel layer.
Authoritative learning resources focusing on key ArcGIS skills:
- Take a web course to learn how to incorporate scientific data and models into common GIS workflows.
Resources and support for automating and customizing workflows:
- Read more about ArcPy functions for multidimensional analysis.
- Read more about ArcGIS REST API dependent raster analysis services, including Aggregate Multidimensional Raster, Analyze Change Using CCDC, Build Multidimensional Transpose, Generate Multidimensional Anomaly, Generate Trend Raster, and Subset Multidimensional Raster.
- Find information on using ArcGIS API for Python for multidimensional and raster analysis.
Online places for the Esri community to connect, collaborate, and share experiences:
- See what the imagery community is saying about analyzing multidimensional data.
* Esri's top picks