Using ArcGIS Time-Series Analysis to Understand Past and Predict Future Change
Evaluating how landscapes evolve over time has become central to addressing major global challenges. From climate variability and urban expansion to forest loss and environmental degradation, decision-makers rely on spatial analysis to interpret past conditions and anticipate future outcomes. Leveraging time-series imagery within the ArcGIS framework enables organizations to move beyond static snapshots and examine change as a dynamic process.
This webinar demonstrates how multidimensional analysis tools within ArcGIS can be applied to historical datasets to detect patterns, quantify shifts, and support predictive modeling.
Understanding Multidimensional Analysis
Multidimensional analysis refers to the ability to work with datasets that vary across dimensions such as time, depth, or other measured variables. In the context of geospatial imagery, this typically involves time-series collections that capture repeated observations of the same geographic area.
ArcGIS supports the storage, visualization, and analysis of multidimensional raster data, allowing analysts to explore trends across temporal sequences. Instead of comparing isolated images manually, users can evaluate entire datasets as structured, queryable collections.
Practical Use Case for Multidimensional Workflows
Time-aware spatial analysis is essential for studying phenomena that evolve incrementally or seasonally. Applications include tracking climate indicators, measuring urban growth patterns, monitoring vegetation health, assessing water resource variability, and identifying long-term land cover transformations.
By applying multidimensional tools, analysts can isolate trends, detect anomalies, and evaluate cyclical patterns that might otherwise remain hidden in single-date imagery. This structured approach improves analytical rigor and supports evidence-based planning.
From Historical Trends to Predictive Insight
ArcGIS enables users to visualize change over time, calculate statistical summaries across temporal layers, and model potential future conditions based on observed trajectories. Through time-series data processing, organizations can identify accelerating patterns of deforestation, quantify the pace of urban sprawl, or examine environmental recovery following policy interventions.
Predictive workflows rely on robust historical datasets and carefully constructed models. By combining imagery analysis with geospatial analytics, users gain the ability not only to interpret what has occurred but also to estimate likely future scenarios.
About Esri
Esri is a global leader in geographic information system software, location intelligence, and mapping technology. The company provides a comprehensive geospatial cloud platform that enables organizations to transform data into operational insight and strategic value.
Founded in 1969, Esri technology is used by more than 350,000 organizations worldwide, including the vast majority of Fortune 100 companies, all U.S. state governments, numerous county administrations, leading universities, and executive departments of the U.S. Government. Esri continues to advance geospatial innovation in areas such as digital transformation, Internet of Things (IoT) integration, and advanced analytics.
Through multidimensional analysis and time-series imagery, ArcGIS equips professionals with the tools needed to understand environmental change and support informed, forward-looking decisions.
