Turf-MCP

使用 K-means 算法进行点聚类。

此功能使用 K-means 聚类算法对点进行聚类，将点划分为指定数量的簇。

Args: points: 点特征集合 - 类型: str (JSON 字符串格式的 GeoJSON FeatureCollection) - 格式: FeatureCollection with Point features - 示例: '{"type": "FeatureCollection", "features": [{"type": "Feature", "geometry": {"type": "Point", "coordinates": [-75.343, 39.984]}}, ...]}'

number_of_clusters: 聚类数量
    - 类型: int
    - 描述: 要创建的聚类数量
    - 示例: 5

options: 可选参数配置
    - 类型: str (JSON 字符串) 或 None
    - 可选字段:
        - numberOfClusters: 聚类数量（与 number_of_clusters 参数相同）
        - mutate: 是否修改原始特征 (默认: false)
    - 示例: '{"mutate": true}'

Returns: str: JSON 字符串格式的 GeoJSON FeatureCollection - 类型: GeoJSON FeatureCollection with Point features - 格式: {"type": "FeatureCollection", "features": [{"type": "Feature", "geometry": {"type": "Point", "coordinates": [lng, lat]}, "properties": {"cluster": 聚类编号, ...}}, ...]} - 示例: '{"type": "FeatureCollection", "features": [{"type": "Feature", "geometry": {"type": "Point", "coordinates": [-75.343, 39.984]}, "properties": {"cluster": 1}}, ...]}'

Raises: Exception: 当 JavaScript 执行失败、超时或输入数据格式错误时抛出异常

Example: >>> import asyncio >>> points = '{"type": "FeatureCollection", "features": [{"type": "Feature", "geometry": {"type": "Point", "coordinates": [-75.343, 39.984]}}]}' >>> result = asyncio.run(clustersKmeans(points, 5)) >>> print(result) '{"type": "FeatureCollection", "features": [{"type": "Feature", "geometry": {"type": "Point", "coordinates": [-75.343, 39.984]}, "properties": {"cluster": 1}}, ...]}'

Notes: - 输入参数 points 和 options 必须是有效的 JSON 字符串 - 坐标顺序为 [经度, 纬度] (WGS84 坐标系) - K-means 算法需要预先指定聚类数量 - 聚类编号从 0 开始 - 算法使用随机初始中心点，每次运行结果可能不同 - 依赖于 Turf.js 库和 Node.js 环境