GeoTier 1.1

Assigns features to hierarchical tiers based on score and spatial separation.

GeoTier: Intelligent Map Decluttering

GeoTier takes a list of scored geographic features and assigns them to hierarchical tiers based on score and spatial separation. Items are assigned to tiers based on the percentile rank of their score. This is further refined by enforcing that every item in a tier meets a specified separation distance for that tier.

The output is a dataset ready for multi-scale map rendering, with two distinct assignments for each feature:

• score_tier: The tier a feature qualifies for based its scores percentile rank. This is ideal for styling, allowing features to be styled by their importance tier.
• resolved_tier: The tier a feature is placed in after enforcing separation. This is ideal for visibility filtering at different zoom levels. Using the resolved_tier for zoom filtering ensures that each zoom level has features with the specified separation distance.

Example: Tiering Mountain Peaks

Imagine you have a list of mountain peaks, and your configuration defines two tiers: Tier 10 for globally significant peaks, and Tier 9 for regionally significant ones. (Note for the example: Lhotse is the 4th tallest peak in the world but close to Everest)

Input Data:

Name	Score	Lon	Lat
Mount Everest	100	...	...
Lhotse	99	...	...

Configuration:

• Tier 4: Requires a score in the top 1% and a 1000km separation.
• Tier 5: Requires a score in the top 5% and a 200km separation.

GeoTier Output:

Name	Score	score_tier	resolved_tier	Comment
Mount Everest	100	4	4	Highest score, placed in Tier 4.
Lhotse	99	4	6	Downgraded. Qualifies for Tier 4 by score, but is too close to Everest.
				Placed in the next tier where it meets separation.

The score tier can be used to style based on importance, while the resolved_tier can be used to filter to prevent clutter. As the example shows, GeoTier ensures your most important features are always preserved while intelligently managing the visual density of your map. At a low zoom, Lhotse could be filtered out and then shown as the zoom increases. However, in both cases it could be displayed with the same bold labelling for the top mountains in the world.

Key Benefits :

• Automated Decluttering: Eliminates the cumbersome process of selecting which labels to show at different map scales.
• Data-Driven Generalization: Uses a quantitative, repeatable process based on an importance score, leading to more objective and consistent maps.
• Guaranteed Separation: Enforces a minimum separation distance for each resolved_tier, ensuring labels meet the distance you specify.
• Dual-Tier Output: By separating the score_tier (for styling) from the resolved_tier (for visibility), you can correctly style a downgraded item with the prominence it deserves.
• Hierarchy-Aware: The algorithm is designed to be "hierarchy-aware." If two points are in conflict, the point with the higher importance score always wins, ensuring your most significant features are always preserved.
• Configuration-Driven: All tiering and separation rules are defined in a simple, human-readable YAML file, making your workflow transparent, versionable, and easy to modify.

Any points that do not meet the criteria for any defined tier will be assigned the default_tier_id. Note that you choose the tier names. You could name them to match the expected zoom level for the tier.

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Distance Measurement and Performance

Input Data: The input CSV file must contain point coordinates in WGS84 (EPSG:4326).

To ensure both accuracy and speed, GeoTier uses a two-phase process for spatial calculations:

1. Coordinate Projection: All geographic coordinates are projected once into a meter-based system (EPSG:3395) for fast Euclidean distance checks.
2. Conflict Detection: An R-tree spatial index is used to find nearby points efficiently. The engine supports two modes, controlled by the approximate_distance config setting:
   • Accurate (Default): A fast bounding box search finds potential neighbors, then a precise radial distance check is performed on that small subset.
   • Approximate (Faster): Skips the second precise check and uses only the initial bounding box search. This should only be needed for extremely large datasets.

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Configuration Example

All tiering rules are defined in a single YAML file (e.g., config/geysers_classification.yml).

# Defines the input and output column names
id_column: id
score_column: score
output_resolved_tier_column: tier      # For zoom filtering
output_score_tier_column: style_tier # For styling
default_tier_id: 0  # For items not meeting any tier criteria

# Any item with a score below this will be ignored by percentile calculations
# and automatically placed in the default tier.
ignore_scores_below: 0.01
approximate_distance: false   # If true then a bounding box is used (faster) otherwise true distance is used

# Defines the tiering rules, processed from most restrictive to least.
# `separation` is in meters.
tiers:
  - tier_id: 9
    method: percentile
    min_pctl: 98
    separation: 4000

  - tier_id: 7
    method: percentile
    min_pctl: 95
    separation: 1000

  - tier_id: 1
    method: score
    min_score: 10
    separation: 50

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Usage with Multiple Datasets (Cross-Category Ranking)

A key challenge in cartography is creating a single, coherent map from multiple feature types (e.g., peaks, waterfalls, points of interest, etc). GeoTier is designed to support this through a deliberate, two-stage workflow that relies on its companion tool, ImportanceScore.

This approach is called Thematic Scaling, which allows you, the domain expert, to define the relative importance between different categories.

The Recommended Workflow

Stage 1: Generate Thematically Scaled Scores with ImportanceScore

Before using GeoTier, process each category independently using ImportanceScore. ImportanceScore uses Machine Learning to create a score based on the item's attributes.

The key is to use ImportanceScore's scaling configuration block to assign each category its own "importance ceiling."

• Example: You decide that the most important mountain peaks are globally significant, while even the most important man-made points of interest are only regionally significant.

  • config/peaks_model.yml:

    scaling:
      min: 0
      max: 100 # Peaks can achieve the maximum possible score.
    

  • config/poi_model.yml:

    scaling:
      min: 0
      max: 70 # POIs are capped; they can never be more important than a 70.
    

1. Run Importance score on peaks and then on POI's.

Stage 2: Merge and Classify with GeoTier

1. Merge Data: Combine all the scored output files from Stage 1 into a single CSV file. The score column in this file now reflects your expert-defined cross-category hierarchy.
2. Run GeoTier: Run GeoTier once on this single, merged file.

Why this works: GeoTier's percentile ranking will now operate on the pre-weighted distribution. The top tiers will naturally be filled by peaks (as only they can have scores above 70), and the spatial separation will be correctly resolved between all feature types simultaneously. This ensures a globally consistent and cartographically sound map.