## The Problem in GIS
Dimensionality derives from the high number of attributes associated with geographic objects: - Demographic data (age, income, education) - Environmental data (temperature, precipitation, air quality) - Infrastructure data (transportation, energy networks) - Economic and social data (commercial activities, crime, public services) - Spatialized time series across multiple periods
When combining multiple *thematic layers* with many attributes, the analytical space rapidly expands, incurring the curse of dimensionality.
## Practical Effects
*Performance Issues*: High computation times during spatial analyses (overlay, buffer, spatial join) and slower spatial search operations (nearest neighbor, interpolations).
*Interpretative Difficulties*: Overloaded dashboards make it difficult to identify significant patterns and spatial correlations.
*Reduced Spatial Distance Significance*: When considering many variables simultaneously, objects in multidimensional space appear equally distant from each other. This causes methods like IDW interpolation or distance-based clustering to produce misleading results.
## Key Strategies
*Selection of Relevant Attributes*: Choose only truly relevant attributes and use correlation analysis to eliminate redundancy.
*Dimensionality Reduction*: Apply *Principal Component Analysis (PCA)* to synthesize data into spatially interpretable indicators, or use *Spatially Constrained Clustering*.
*Spatial Scale Management*: Work with appropriate aggregations (hexagonal cells, H3 grids) and choose geographic detail levels matching your analytical question.
*Efficient Databases*: Use optimized spatial databases like *PostGIS* with advanced indexing (R-Tree, GIST) and modern formats like *vector tiles (MVT)* for web visualization.
## Dashboard Design Best Practices
- *Selective layers*: Let users activate only needed thematic layers, organizing them in categories for targeted combinations
- *Simplified queries*: Provide pre-configured searches instead of complex multi-attribute filters
- *Synthetic indicators*: Create composite indices (e.g., "Environmental Quality Index") instead of showing numerous separate variables
- *Multi-level interfaces*: Design different views for policy makers (simplified) vs. technical analysts (advanced)
## The Blessing Side
High dimensionality becomes an advantage when properly managed:
- *Robust predictive models* for complex phenomena (hydrogeological risk, disease spread)
- *Advanced pattern detection* revealing hidden territorial relationships
- *Personalized analyses* for different stakeholders
## Conclusion
Conscious dimensionality management improves GIS work quality and efficiency while providing clearer, more interpretable insights. The key is applying the right techniques at the right time.