When analyzing datasets that evolve across time and geography—disease outbreaks, environmental changes, demographic shifts—most researchers face a fundamental limitation: their tools force them to choose between temporal analysis or spatial analysis, but not both simultaneously.
This creates a blind spot. You might detect that disease rates are increasing over time. You might identify hotspots where rates are highest. But can you pinpoint exactly when and where the trend fundamentally changed direction? That’s where traditional GIS falls short.
The Problem with Conventional GIS Approaches
Standard geospatial data analysis challenges include:
- Temporal OR spatial, never integrated. Most platforms excel at one dimension but struggle to reveal insights that only emerge when both are analyzed together.
- Missed inflection points. Without space-time analysis capabilities, you can’t detect the critical moments when a trend breaks—when intervention effectiveness suddenly improves in certain regions, when environmental degradation accelerates in specific areas, or when disease patterns shift trajectory.
- Inappropriate methods for compositional data. When your data represents parts of a whole (market shares, demographic percentages, land use compositions), traditional statistical approaches produce misleading results.
The Solution: True Space-Time Integration With Vesta
Space-time joinpoint regression solves these challenges by detecting trend breakpoints across both dimensions simultaneously. Instead of asking “when did this change?” or “where did this change?”, you can now answer “when and where did this change?”—revealing patterns that would otherwise remain hidden.
This integrated approach is particularly powerful for:
- Epidemiologists tracking how disease patterns evolve differently across regions
- Environmental scientists identifying when and where pollution levels cross critical thresholds
- Policy analysts measuring intervention impacts that vary by location and timing
- Any researcher working with geospatial time-series data
See Space-Time Analysis in Action
Want to understand how space-time analysis works in practice? Watch our recorded webinar featuring BioMedware experts Geoffrey Jacquez and Pierre Goovaerts as they demonstrate real-world applications using Vesta—the only platform designed specifically for true space-time integration.
In this 50-minute session, you’ll learn:
- When and how to apply space-time joinpoint regression to your data
- Proper techniques for analyzing compositional geospatial data
- Live platform demonstration with actual datasets
- Practical applications across multiple research domains
Whether you’re analyzing health outcomes, environmental trends, or policy effectiveness, this webinar will show you how to uncover the insights your current tools are missing. Ready to try out Vesa? Get a free 30-day trial here