• Trajectory Simplification JS

Trajectory Simplification JS is a lightweight, modular, and customizable geospatial simplification library that focuses on providing a collection of efficient trajectory simplification algorithms for JavaScript and TypeScript projects. Each algorithm is implemented in a separate, minimalistic file, making it easy for users to copy, modify, and integrate them into their projects.

Key Advantages

1. Lightweight and modular : Unlike larger geospatial libraries, Trajectory Simplification JS focuses on providing lean, efficient algorithms without any unnecessary dependencies or bloat. This makes it suitable for projects where performance and simplicity are crucial.
2. Easy customization : Each algorithm is implemented in a standalone file, making it straightforward for users to copy and modify the code to suit their specific needs. This level of customization allows for more fine-grained control over the algorithm's behavior and performance.
3. Flexible integration : Trajectory Simplification JS's modular design enables users to import only the algorithms they need, resulting in a smaller code footprint and improved performance in their projects.
4. Comprehensive collection : Trajectory Simplification JS includes a wide range of trajectory simplification algorithms, such as Douglas-Peucker, Visvalingam-Whyatt, Radial Distance, and more, making it a versatile choice for diverse use cases and applications.
5. Well-documented code : The algorithms are accompanied by clear and detailed comments, making it easy for users to understand the inner workings of each algorithm and adapt them as needed.

Choose Trajectory Simplification JS for a lightweight, customizable, and efficient solution to your geospatial simplification needs.

Douglas-Peucker Algorithm

Advantages

• Applicable to various types of trajectory data.
• Able to handle large amounts of data.
• Able to preserve the shape of the original trajectory well.

Disadvantages

• Relatively high computational complexity, especially for large datasets.
• Difficult to find the best threshold that applies to all scenarios.

Applicable Scenarios

Suitable for various application scenarios, especially those that require high accuracy and shape preservation.

Visvalingam-Whyatt Algorithm

Advantages

• Low computational complexity.
• Applicable to various types of trajectory data.
• Able to balance the degree of simplification with the preservation of the original trajectory shape.

Disadvantages

• Requires a predetermined number of points as a simplification target, which may not be flexible enough.
• May not be suitable for trajectories with complex shapes.

Applicable Scenarios

Suitable for scenarios that require a balance between simplification and shape preservation.

Radial Distance Algorithm

Advantages

• Very low computational complexity.
• Applicable to various types of trajectory data.
• Simple implementation.

Disadvantages

• May not fully preserve the shape of the original trajectory, especially in curved sections.
• Sensitive to threshold selection.

Applicable Scenarios

Suitable for scenarios that require rapid trajectory simplification with low requirements for trajectory shape preservation.

Topological Preserving Simplification (TPS) Algorithm

Advantages

• Able to preserve the topological characteristics of the original trajectory.
• Applicable to various types of trajectory data.
• Able to handle trajectories with complex shapes.

Disadvantages

• Relatively high computational complexity.
• May not be suitable for scenarios that require extreme simplification.

Applicable Scenarios

Suitable for scenarios that require the preservation of topological characteristics, such as road networks and geographic information systems.

Sliding-Window Algorithm

Advantages

• Simple implementation, easy to understand and use.
• Provides control over simplification level through window size adjustment.

Disadvantages

• Limited capability in handling complex trajectory shapes.
• Choice of window size significantly affects simplification effect.
• May not be suitable for trajectories with uneven point densities.
• Performance may be inferior compared to more advanced algorithms like Douglas-Peucker.

Applicable Scenarios

Suitable for scenarios that require rapid trajectory simplification, especially when trajectory shapes are relatively simple and uniform, and data scale is not extremely large. For larger datasets or more complex trajectory shapes, considering more efficient algorithms like Douglas-Peucker might be advisable.

Priority-Queue Algorithm

Advantages

• Able to handle trajectories with varying densities of data points.
• Can control the level of simplification by adjusting the distance threshold.
• Preserves the shape of the original trajectory well.

Disadvantages

• May not be suitable for trajectories with complex shapes.
• Requires a relatively high computational complexity.

Applicable Scenarios

Suitable for scenarios that require the preservation of trajectory shape and density, such as GPS tracking and geographic information systems.

Imai-Iri Algorithm

Advantages

• Able to handle trajectories with complex shapes.
• Preserves the shape of the original trajectory well.
• Can be used to simplify both 2D and 3D trajectories.

Disadvantages

• Requires a relatively high computational complexity.
• May not be suitable for scenarios that require extreme simplification.

Applicable Scenarios

Suitable for scenarios that require the preservation of trajectory shape, such as GIS applications, and can also handle complex 2D and 3D trajectory data processing.

Opheim Algorithm

Advantages

• Able to handle trajectories with complex shapes.
• Preserves the topological characteristics of the original trajectory well.
• Can be used to simplify both 2D and 3D trajectories.

Disadvantages

• Requires a relatively high computational complexity.
• May not be suitable for scenarios that require extreme simplification.

Applicable Scenarios

Suitable for scenarios that require the preservation of topological characteristics, such as road networks and geographic information systems, as well as for 3D trajectory data processing.

Lang Algorithm

Advantages

• Simple and efficient, capable of handling trajectories with complex shapes.
• Allows control over the level of simplification by adjusting the distance threshold.

Disadvantages

• Preserves the shape of the original trajectory only to a moderate extent, depending on the chosen threshold.
• May demand a fine balance between computational efficiency and accuracy in preserving the trajectory.
• Might not be suitable for scenarios that require meticulous preservation of the original trajectory.

Applicable Scenarios

Ideal for applications where simplicity and computational efficiency are priorities, and where perfect preservation of the original trajectory shape is not critical. Examples might include basic GPS tracking and initial data processing in geographic information systems.

Note

These algorithms have their own advantages and disadvantages in different application scenarios. In practical applications, it is necessary to choose the appropriate trajectory simplification algorithm based on the characteristics and requirements of the data. In some cases, you may need to adjust or optimize the algorithm according to your own needs.

Contribution

If you would like to contribute to this project, feel free to submit a pull request with your suggestions, improvements or new algorithms.

License

This project is licensed under the MIT License - see the LICENSE.md file for details.