Cartography

7.

7.1.

The Need for Generalization

7.1.1.

Scale Constraints

7.1.2.

Readability and Clarity

7.1.3.

Cognitive Limitations

7.1.4.

Technical Constraints

7.2.

Principles of Cartographic Generalization

7.2.1.

Retaining Essential Information

7.2.2.

Eliminating Redundancy

7.2.3.

Maintaining Spatial Relationships

7.2.4.

Preserving Map Purpose

7.3.

Generalization Operators

7.3.1.

7.3.1.1.

Feature Elimination

7.3.1.2.

Importance Ranking

7.3.2.

7.3.2.1.

Line Simplification

7.3.2.2.

Douglas-Peucker Algorithm

7.3.3.

7.3.3.1.

Curve Smoothing

7.3.3.2.

Noise Reduction

7.3.4.

7.3.4.1.

Feature Grouping

7.3.4.2.

Statistical Aggregation

7.3.5.

Amalgamation (Merging)

7.3.5.1.

Boundary Dissolution

7.3.5.2.

Feature Combination

7.3.6.

7.3.6.1.

Dimensional Reduction

7.3.6.2.

Point to Line Collapse

7.3.7.

7.3.7.1.

Detail Enhancement

7.3.7.2.

Feature Subdivision

7.3.8.

7.3.8.1.

Size Enhancement

7.3.8.2.

Minimum Size Requirements

7.3.9.

7.3.9.1.

Conflict Resolution

7.3.9.2.

Spatial Adjustment

7.3.10.

7.3.10.1.

Visual Emphasis

7.3.10.2.

Contrast Improvement

7.4.

Automated Generalization Techniques

7.4.1.

Rule-based Systems

7.4.2.

Machine Learning Approaches

7.4.3.

Multi-scale Databases

7.5.

Quality Assessment in Generalization

7.5.1.

Geometric Accuracy

7.5.2.

Semantic Accuracy

7.5.3.

Previous

6. Map Scale

Go to top

Next

8. Data for Cartography