refactor: 重组项目目录结构
以讲义内容为骨架迁移到标准目录格式： - officefile/ 主内容（12章 + 附录 + CC4SI补充） - dofile/ 代码示例（11个Python脚本） - data/ 图片资源 - output/ 生成输出（忽略） - Archive/ 归档旧目录（忽略） - .claude/skills/ 保留markdown-to-docx工具链 - .pandoc/ 保留CSL和本地化配置 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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"""
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空间表征示例 (Spatial Representation Example)
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============================================
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本示例展示空间智能系统中的空间表征方法。
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空间表征是对地理空间现象的抽象和建模，是空间推理的基础。
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核心概念:
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1. 空间对象模型 - 点、线、面等几何对象
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2. 空间关系模型 - 拓扑、距离、方向关系
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3. 空间场模型 - 连续表面的表示
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4. 空间索引结构 - 加速空间查询
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5. 多尺度表征 - 不同详细程度的表示
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应用场景:
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- 地理信息系统 (GIS)
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- 空间数据库
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- 空间推理引擎
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- 地理可视化
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作者: CC4SI 项目组
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"""
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import math
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import json
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from typing import List, Dict, Tuple, Optional, Any, Set
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from dataclasses import dataclass, field
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from enum import Enum
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from abc import ABC, abstractmethod
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import random
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# ============================================================================
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# 几何类型与空间对象
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# ============================================================================
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class GeometryType(Enum):
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"""几何类型枚举"""
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POINT = "Point"
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LINESTRING = "LineString"
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POLYGON = "Polygon"
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MULTIPOINT = "MultiPoint"
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MULTILINESTRING = "MultiLineString"
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MULTIPOLYGON = "MultiPolygon"
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GEOMETRYCOLLECTION = "GeometryCollection"
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class SpatialReference(Enum):
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"""空间参考系统"""
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WGS84 = "EPSG:4326" # 经纬度
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WEB_MERCATOR = "EPSG:3857" # Web墨卡托
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CGCS2000 = "EPSG:4490" # 中国大地坐标系统
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@dataclass
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class Point:
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"""点几何对象"""
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x: float
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y: float
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srid: int = 4326 # 空间参考ID
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def __iter__(self):
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"""支持解包"""
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return iter((self.x, self.y))
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def __iter__(self):
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return iter((self.x, self.y))
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def to_tuple(self) -> Tuple[float, float]:
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"""转换为元组"""
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return (self.x, self.y)
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def to_geojson(self) -> Dict:
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"""转换为GeoJSON"""
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return {
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"type": "Point",
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"coordinates": [self.x, self.y]
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}
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def distance_to(self, other: 'Point') -> float:
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"""计算到另一个点的欧氏距离"""
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return math.sqrt((self.x - other.x)**2 + (self.y - other.y)**2)
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def __repr__(self) -> str:
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return f"Point({self.x:.4f}, {self.y:.4f})"
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@dataclass
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class LineString:
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"""线几何对象"""
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coordinates: List[Point]
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srid: int = 4326
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@property
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def length(self) -> float:
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"""计算线的长度"""
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if len(self.coordinates) < 2:
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return 0.0
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total = 0.0
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for i in range(len(self.coordinates) - 1):
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total += self.coordinates[i].distance_to(self.coordinates[i + 1])
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return total
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def to_geojson(self) -> Dict:
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"""转换为GeoJSON"""
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return {
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"type": "LineString",
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"coordinates": [[p.x, p.y] for p in self.coordinates]
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}
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def get_point_at(self, ratio: float) -> Point:
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"""
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获取线上指定比例位置的点
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Args:
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ratio: 0到1之间的比例值
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Returns:
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该位置的点
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"""
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if ratio <= 0:
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return self.coordinates[0]
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if ratio >= 1:
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return self.coordinates[-1]
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target_length = self.length * ratio
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accumulated = 0.0
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for i in range(len(self.coordinates) - 1):
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segment_length = self.coordinates[i].distance_to(self.coordinates[i + 1])
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if accumulated + segment_length >= target_length:
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# 在此段上
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segment_ratio = (target_length - accumulated) / segment_length
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p1 = self.coordinates[i]
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p2 = self.coordinates[i + 1]
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return Point(
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p1.x + (p2.x - p1.x) * segment_ratio,
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p1.y + (p2.y - p1.y) * segment_ratio
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)
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accumulated += segment_length
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return self.coordinates[-1]
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def __repr__(self) -> str:
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return f"LineString({len(self.coordinates)} points)"
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@dataclass
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class Polygon:
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"""面几何对象"""
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exterior: List[Point] # 外环
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interiors: List[List[Point]] = field(default_factory=list) # 内环（空洞）
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srid: int = 4326
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@property
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def area(self) -> float:
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"""使用鞋带公式计算多边形面积"""
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return self._ring_area(self.exterior) - sum(
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self._ring_area(interior) for interior in self.interiors
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)
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def _ring_area(self, ring: List[Point]) -> float:
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"""计算环的面积（绝对值）"""
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if len(ring) < 3:
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return 0.0
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area = 0.0
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for i in range(len(ring)):
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j = (i + 1) % len(ring)
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area += ring[i].x * ring[j].y
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area -= ring[j].x * ring[i].y
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return abs(area) / 2
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@property
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def centroid(self) -> Point:
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"""计算多边形质心"""
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if len(self.exterior) < 3:
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return self.exterior[0] if self.exterior else Point(0, 0)
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# 简化：使用外环的顶点平均值
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avg_x = sum(p.x for p in self.exterior) / len(self.exterior)
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avg_y = sum(p.y for p in self.exterior) / len(self.exterior)
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return Point(avg_x, avg_y)
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def contains_point(self, point: Point) -> bool:
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"""
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判断点是否在多边形内（射线法）
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Args:
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point: 待判断的点
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Returns:
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是否在多边形内
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"""
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return self._point_in_ring(point, self.exterior) and \
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all(not self._point_in_ring(point, interior)
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for interior in self.interiors)
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def _point_in_ring(self, point: Point, ring: List[Point]) -> bool:
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"""射线法判断点是否在环内"""
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if len(ring) < 3:
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return False
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x, y = point.x, point.y
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inside = False
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for i in range(len(ring)):
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j = (i + 1) % len(ring)
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xi, yi = ring[i].x, ring[i].y
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xj, yj = ring[j].x, ring[j].y
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# 检查射线与边的交点
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if ((yi > y) != (yj > y)) and \
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(x < (xj - xi) * (y - yi) / (yj - yi + 1e-10) + xi):
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inside = not inside
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return inside
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def to_geojson(self) -> Dict:
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"""转换为GeoJSON"""
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coords = [[[p.x, p.y] for p in self.exterior]]
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coords.extend([[[p.x, p.y] for p in interior] for interior in self.interiors])
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return {
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"type": "Polygon",
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"coordinates": coords
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}
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def __repr__(self) -> str:
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holes = len(self.interiors)
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return f"Polygon({len(self.exterior)} vertices{f', {holes} holes' if holes > 0 else ''})"
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@dataclass
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class Envelope:
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"""包围盒"""
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min_x: float
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max_x: float
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min_y: float
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max_y: float
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@property
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def width(self) -> float:
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return self.max_x - self.min_x
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@property
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def height(self) -> float:
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return self.max_y - self.min_y
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@property
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def area(self) -> float:
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return self.width * self.height
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@property
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def center(self) -> Point:
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return Point(
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(self.min_x + self.max_x) / 2,
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(self.min_y + self.max_y) / 2
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)
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def contains(self, point: Point) -> bool:
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"""判断点是否在包围盒内"""
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return (self.min_x <= point.x <= self.max_x and
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self.min_y <= point.y <= self.max_y)
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def intersects(self, other: 'Envelope') -> bool:
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"""判断是否与另一个包围盒相交"""
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return not (self.max_x < other.min_x or self.min_x > other.max_x or
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self.max_y < other.min_y or self.min_y > other.max_y)
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def union(self, other: 'Envelope') -> 'Envelope':
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"""计算与另一个包围盒的并集"""
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return Envelope(
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min_x=min(self.min_x, other.min_x),
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max_x=max(self.max_x, other.max_x),
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min_y=min(self.min_y, other.min_y),
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max_y=max(self.max_y, other.max_y)
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)
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def __repr__(self) -> str:
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return f"Envelope([{self.min_x:.2f}, {self.min_y:.2f}] -> [{self.max_x:.2f}, {self.max_y:.2f}])"
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# ============================================================================
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# 空间特征对象
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# ============================================================================
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@dataclass
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class SpatialFeature:
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"""
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空间特征
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包含几何和属性信息的完整空间对象。
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"""
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id: str
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geometry: Any # Point, LineString, Polygon等
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properties: Dict[str, Any] = field(default_factory=dict)
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def to_geojson(self) -> Dict:
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"""转换为GeoJSON Feature"""
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geom_data = None
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if isinstance(self.geometry, Point):
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geom_data = self.geometry.to_geojson()
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elif isinstance(self.geometry, LineString):
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geom_data = self.geometry.to_geojson()
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elif isinstance(self.geometry, Polygon):
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geom_data = self.geometry.to_geojson()
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return {
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"type": "Feature",
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"id": self.id,
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"geometry": geom_data,
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"properties": self.properties
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}
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def to_geojson_collection(self) -> Dict:
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"""转换为GeoJSON FeatureCollection"""
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return {
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"type": "FeatureCollection",
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"features": [self.to_geojson()]
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}
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def envelope(self) -> Envelope:
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"""计算包围盒"""
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if isinstance(self.geometry, Point):
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return Envelope(
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self.geometry.x, self.geometry.x,
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self.geometry.y, self.geometry.y
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)
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elif isinstance(self.geometry, (LineString, list)):
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coords = self.geometry.coordinates if isinstance(self.geometry, LineString) else self.geometry
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xs = [p.x for p in coords]
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ys = [p.y for p in coords]
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return Envelope(min(xs), max(xs), min(ys), max(ys))
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elif isinstance(self.geometry, Polygon):
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xs = [p.x for p in self.geometry.exterior]
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ys = [p.y for p in self.geometry.exterior]
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return Envelope(min(xs), max(xs), min(ys), max(ys))
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else:
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raise ValueError(f"Unsupported geometry type: {type(self.geometry)}")
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def __repr__(self) -> str:
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return f"SpatialFeature(id={self.id}, geom={type(self.geometry).__name__})"
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# ============================================================================
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# 空间关系
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# ============================================================================
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class SpatialRelation(Enum):
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"""空间关系类型"""
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EQUALS = "equals"
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DISJOINT = "disjoint"
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INTERSECTS = "intersects"
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TOUCHES = "touches"
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CROSSES = "crosses"
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WITHIN = "within"
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CONTAINS = "contains"
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OVERLAPS = "overlaps"
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@dataclass
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class TopologyRelation:
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"""
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拓扑关系描述
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基于DE-9IM (Dimensionally Extended nine-Intersection Model)模型。
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"""
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relation: SpatialRelation
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confidence: float = 1.0
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def __repr__(self) -> str:
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return f"TopologyRelation({self.relation.value}, conf={self.confidence:.2f})"
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def calculate_relation(geom1: Any, geom2: Any) -> TopologyRelation:
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"""
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计算两个几何对象的空间关系
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Args:
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geom1: 第一个几何对象
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geom2: 第二个几何对象
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Returns:
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拓扑关系
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"""
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# 点-点关系
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if isinstance(geom1, Point) and isinstance(geom2, Point):
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if geom1.x == geom2.x and geom1.y == geom2.y:
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return TopologyRelation(SpatialRelation.EQUALS)
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return TopologyRelation(SpatialRelation.DISJOINT)
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# 点-多边形关系
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if isinstance(geom1, Point) and isinstance(geom2, Polygon):
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if geom2.contains_point(geom1):
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return TopologyRelation(SpatialRelation.WITHIN)
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# 检查是否在边界上
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for i in range(len(geom2.exterior)):
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p1, p2 = geom2.exterior[i], geom2.exterior[(i + 1) % len(geom2.exterior)]
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if point_on_segment(geom1, p1, p2):
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return TopologyRelation(SpatialRelation.TOUCHES)
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return TopologyRelation(SpatialRelation.DISJOINT)
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# 多边形-点关系
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if isinstance(geom1, Polygon) and isinstance(geom2, Point):
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relation = calculate_relation(geom2, geom1)
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if relation.relation == SpatialRelation.WITHIN:
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return TopologyRelation(SpatialRelation.CONTAINS)
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return relation
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# 多边形-多边形关系（简化版：只检查相交）
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if isinstance(geom1, Polygon) and isinstance(geom2, Polygon):
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env1 = envelope_of_polygon(geom1)
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env2 = envelope_of_polygon(geom2)
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if not env1.intersects(env2):
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return TopologyRelation(SpatialRelation.DISJOINT)
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# 简化：检查是否有交点
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if geom1.contains_point(geom2.exterior[0]):
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return TopologyRelation(SpatialRelation.CONTAINS)
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if geom2.contains_point(geom1.exterior[0]):
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return TopologyRelation(SpatialRelation.WITHIN)
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return TopologyRelation(SpatialRelation.INTERSECTS)
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return TopologyRelation(SpatialRelation.DISJOINT)
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def point_on_segment(point: Point, seg_start: Point, seg_end: Point,
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tolerance: float = 1e-6) -> bool:
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"""判断点是否在线段上"""
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# 检查点是否在线段的包围盒内
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if not (min(seg_start.x, seg_end.x) - tolerance <= point.x <=
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max(seg_start.x, seg_end.x) + tolerance and
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min(seg_start.y, seg_end.y) - tolerance <= point.y <=
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max(seg_start.y, seg_end.y) + tolerance):
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return False
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# 检查三点共线
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cross = (seg_end.x - seg_start.x) * (point.y - seg_start.y) - \
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(seg_end.y - seg_start.y) * (point.x - seg_start.x)
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return abs(cross) < tolerance
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def envelope_of_polygon(polygon: Polygon) -> Envelope:
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"""计算多边形的包围盒"""
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xs = [p.x for p in polygon.exterior]
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||||
ys = [p.y for p in polygon.exterior]
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return Envelope(min(xs), max(xs), min(ys), max(ys))
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# ============================================================================
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||||
# 空间索引
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||||
# ============================================================================
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||||
|
||||
class RTreeNode:
|
||||
"""R树节点"""
|
||||
def __init__(self, is_leaf: bool = False):
|
||||
self.is_leaf = is_leaf
|
||||
self.envelope: Optional[Envelope] = None
|
||||
self.children: List['RTreeNode'] = []
|
||||
self.features: List[SpatialFeature] = []
|
||||
|
||||
def update_envelope(self):
|
||||
"""更新节点的包围盒"""
|
||||
if self.is_leaf and self.features:
|
||||
envelopes = [f.envelope() for f in self.features]
|
||||
self.envelope = envelopes[0]
|
||||
for env in envelopes[1:]:
|
||||
self.envelope = self.envelope.union(env)
|
||||
elif not self.is_leaf and self.children:
|
||||
self.envelope = self.children[0].envelope
|
||||
for child in self.children[1:]:
|
||||
if child.envelope:
|
||||
self.envelope = self.envelope.union(child.envelope)
|
||||
|
||||
|
||||
class RTree:
|
||||
"""
|
||||
R树空间索引
|
||||
|
||||
用于加速空间查询的树状索引结构。
|
||||
"""
|
||||
|
||||
def __init__(self, max_children: int = 4):
|
||||
"""
|
||||
初始化R树
|
||||
|
||||
Args:
|
||||
max_children: 每个节点的最大子节点数
|
||||
"""
|
||||
self.max_children = max_children
|
||||
self.root = RTreeNode(is_leaf=True)
|
||||
self.size = 0
|
||||
|
||||
def insert(self, feature: SpatialFeature) -> None:
|
||||
"""插入空间特征"""
|
||||
self._insert(self.root, feature)
|
||||
self.size += 1
|
||||
|
||||
def _insert(self, node: RTreeNode, feature: SpatialFeature) -> None:
|
||||
"""递归插入"""
|
||||
feature_env = feature.envelope()
|
||||
|
||||
if node.is_leaf:
|
||||
node.features.append(feature)
|
||||
node.update_envelope()
|
||||
|
||||
# 如果超过容量，分裂节点
|
||||
if len(node.features) > self.max_children:
|
||||
self._split(node)
|
||||
else:
|
||||
# 选择最佳子节点
|
||||
best_child = self._choose_best_child(node, feature_env)
|
||||
self._insert(best_child, feature)
|
||||
node.update_envelope()
|
||||
|
||||
def _choose_best_child(self, node: RTreeNode, env: Envelope) -> RTreeNode:
|
||||
"""选择插入代价最小的子节点"""
|
||||
best = None
|
||||
best_increase = float('inf')
|
||||
|
||||
for child in node.children:
|
||||
if child.envelope is None:
|
||||
continue
|
||||
union_env = child.envelope.union(env)
|
||||
increase = union_env.area - child.envelope.area
|
||||
|
||||
if increase < best_increase:
|
||||
best_increase = increase
|
||||
best = child
|
||||
|
||||
return best or node.children[0]
|
||||
|
||||
def _split(self, node: RTreeNode) -> None:
|
||||
"""分裂节点 (简化版)"""
|
||||
if node.is_leaf:
|
||||
# 简单分裂：将特征分成两组
|
||||
mid = len(node.features) // 2
|
||||
group1 = node.features[:mid]
|
||||
group2 = node.features[mid:]
|
||||
|
||||
node.features = group1
|
||||
|
||||
new_leaf = RTreeNode(is_leaf=True)
|
||||
new_leaf.features = group2
|
||||
new_leaf.update_envelope()
|
||||
|
||||
# 更新父节点
|
||||
if node == self.root and not node.children:
|
||||
# 根节点分裂
|
||||
new_root = RTreeNode(is_leaf=False)
|
||||
new_root.children = [node, new_leaf]
|
||||
new_root.update_envelope()
|
||||
self.root = new_root
|
||||
else:
|
||||
# 简化：不处理非根节点的分裂
|
||||
pass
|
||||
|
||||
def query(self, envelope: Envelope) -> List[SpatialFeature]:
|
||||
"""查询与包围盒相交的所有特征"""
|
||||
results = []
|
||||
self._query(self.root, envelope, results)
|
||||
return results
|
||||
|
||||
def _query(self, node: RTreeNode, envelope: Envelope,
|
||||
results: List[SpatialFeature]) -> None:
|
||||
"""递归查询"""
|
||||
if node.envelope and not node.envelope.intersects(envelope):
|
||||
return
|
||||
|
||||
if node.is_leaf:
|
||||
for feature in node.features:
|
||||
if feature.envelope().intersects(envelope):
|
||||
results.append(feature)
|
||||
else:
|
||||
for child in node.children:
|
||||
self._query(child, envelope, results)
|
||||
|
||||
def nearest_neighbor(self, point: Point, k: int = 1) -> List[Tuple[SpatialFeature, float]]:
|
||||
"""
|
||||
K近邻查询
|
||||
|
||||
Args:
|
||||
point: 查询点
|
||||
k: 返回的最近邻数量
|
||||
|
||||
Returns:
|
||||
(特征, 距离) 列表
|
||||
"""
|
||||
candidates = []
|
||||
self._collect_candidates(self.root, candidates)
|
||||
|
||||
# 计算距离并排序
|
||||
distances = []
|
||||
for feature in candidates:
|
||||
if isinstance(feature.geometry, Point):
|
||||
dist = feature.geometry.distance_to(point)
|
||||
distances.append((feature, dist))
|
||||
else:
|
||||
# 非点几何：使用包围盒中心距离
|
||||
env = feature.envelope()
|
||||
center = env.center
|
||||
dist = math.sqrt((center.x - point.x)**2 + (center.y - point.y)**2)
|
||||
distances.append((feature, dist))
|
||||
|
||||
distances.sort(key=lambda x: x[1])
|
||||
return distances[:k]
|
||||
|
||||
def _collect_candidates(self, node: RTreeNode, results: List[SpatialFeature]) -> None:
|
||||
"""收集所有候选特征"""
|
||||
if node.is_leaf:
|
||||
results.extend(node.features)
|
||||
else:
|
||||
for child in node.children:
|
||||
self._collect_candidates(child, results)
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# 空间场模型
|
||||
# ============================================================================
|
||||
|
||||
class GridField:
|
||||
"""
|
||||
栅格场模型
|
||||
|
||||
使用规则网格表示连续空间现象。
|
||||
"""
|
||||
|
||||
def __init__(self, bounds: Envelope, rows: int, cols: int,
|
||||
nodata: float = -9999):
|
||||
"""
|
||||
初始化栅格场
|
||||
|
||||
Args:
|
||||
bounds: 空间范围
|
||||
rows: 行数
|
||||
cols: 列数
|
||||
nodata: 无数据值
|
||||
"""
|
||||
self.bounds = bounds
|
||||
self.rows = rows
|
||||
self.cols = cols
|
||||
self.nodata = nodata
|
||||
self.data = [[nodata for _ in range(cols)] for _ in range(rows)]
|
||||
|
||||
@property
|
||||
def cell_width(self) -> float:
|
||||
"""获取单元格宽度"""
|
||||
return self.bounds.width / self.cols
|
||||
|
||||
@property
|
||||
def cell_height(self) -> float:
|
||||
"""获取单元格高度"""
|
||||
return self.bounds.height / self.rows
|
||||
|
||||
def get_cell_index(self, point: Point) -> Optional[Tuple[int, int]]:
|
||||
"""
|
||||
获取点对应的栅格索引
|
||||
|
||||
Args:
|
||||
point: 空间点
|
||||
|
||||
Returns:
|
||||
(行索引, 列索引) 或 None
|
||||
"""
|
||||
if not self.bounds.contains(point):
|
||||
return None
|
||||
|
||||
col = int((point.x - self.bounds.min_x) / self.cell_width)
|
||||
row = int((self.bounds.max_y - point.y) / self.cell_height)
|
||||
|
||||
col = max(0, min(col, self.cols - 1))
|
||||
row = max(0, min(row, self.rows - 1))
|
||||
|
||||
return (row, col)
|
||||
|
||||
def set_value(self, row: int, col: int, value: float) -> None:
|
||||
"""设置栅格值"""
|
||||
if 0 <= row < self.rows and 0 <= col < self.cols:
|
||||
self.data[row][col] = value
|
||||
|
||||
def get_value(self, row: int, col: int) -> float:
|
||||
"""获取栅格值"""
|
||||
if 0 <= row < self.rows and 0 <= col < self.cols:
|
||||
return self.data[row][col]
|
||||
return self.nodata
|
||||
|
||||
def get_value_at_point(self, point: Point) -> float:
|
||||
"""获取点位置的值（最近邻）"""
|
||||
idx = self.get_cell_index(point)
|
||||
if idx:
|
||||
return self.get_value(idx[0], idx[1])
|
||||
return self.nodata
|
||||
|
||||
def interpolate_at_point(self, point: Point) -> float:
|
||||
"""双线性插值获取点位置的值"""
|
||||
idx = self.get_cell_index(point)
|
||||
if not idx:
|
||||
return self.nodata
|
||||
|
||||
row, col = idx
|
||||
|
||||
# 获取四个角点的值
|
||||
values = []
|
||||
for r in range(row, min(row + 2, self.rows)):
|
||||
for c in range(col, min(col + 2, self.cols)):
|
||||
values.append((r, c, self.data[r][c]))
|
||||
|
||||
if len(values) < 4 or any(v[2] == self.nodata for v in values):
|
||||
return self.get_value(row, col) # 回退到最近邻
|
||||
|
||||
# 双线性插值
|
||||
# 简化实现
|
||||
return self.get_value(row, col)
|
||||
|
||||
def get_statistics(self) -> Dict[str, float]:
|
||||
"""获取统计信息"""
|
||||
values = [v for row in self.data for v in row if v != self.nodata]
|
||||
|
||||
if not values:
|
||||
return {"count": 0, "min": self.nodata, "max": self.nodata,
|
||||
"mean": self.nodata, "std": 0}
|
||||
|
||||
import statistics
|
||||
return {
|
||||
"count": len(values),
|
||||
"min": min(values),
|
||||
"max": max(values),
|
||||
"mean": statistics.mean(values),
|
||||
"std": statistics.stdev(values) if len(values) > 1 else 0
|
||||
}
|
||||
|
||||
def to_geojson(self) -> Dict:
|
||||
"""转换为GeoJSON（简化：输出为点集）"""
|
||||
features = []
|
||||
for r in range(self.rows):
|
||||
for c in range(self.cols):
|
||||
val = self.data[r][c]
|
||||
if val != self.nodata:
|
||||
# 计算点坐标
|
||||
x = self.bounds.min_x + (c + 0.5) * self.cell_width
|
||||
y = self.bounds.max_y - (r + 0.5) * self.cell_height
|
||||
|
||||
features.append({
|
||||
"type": "Feature",
|
||||
"geometry": {"type": "Point", "coordinates": [x, y]},
|
||||
"properties": {"value": val}
|
||||
})
|
||||
|
||||
return {
|
||||
"type": "FeatureCollection",
|
||||
"features": features
|
||||
}
|
||||
|
||||
|
||||
# ============================================================================
|
||||
# 主程序
|
||||
# ============================================================================
|
||||
|
||||
def main():
|
||||
"""主程序 - 演示空间表征的使用"""
|
||||
|
||||
print("="*70)
|
||||
print("空间表征示例演示")
|
||||
print("="*70)
|
||||
|
||||
# ========================================================================
|
||||
# 1. 基础几何对象
|
||||
# ========================================================================
|
||||
print("\n[部分 1] 基础几何对象")
|
||||
print("-" * 50)
|
||||
|
||||
# 创建点
|
||||
point1 = Point(120.5, 30.2)
|
||||
point2 = Point(121.0, 30.5)
|
||||
print(f"\n点对象:")
|
||||
print(f" point1 = {point1}")
|
||||
print(f" point2 = {point2}")
|
||||
print(f" 距离 = {point1.distance_to(point2):.4f}")
|
||||
|
||||
# 创建线
|
||||
linestring = LineString([
|
||||
Point(120.0, 30.0),
|
||||
Point(120.5, 30.2),
|
||||
Point(121.0, 30.5),
|
||||
Point(121.5, 30.3)
|
||||
])
|
||||
print(f"\n线对象:")
|
||||
print(f" {linestring}")
|
||||
print(f" 长度 = {linestring.length:.4f}")
|
||||
print(f" 中点 = {linestring.get_point_at(0.5)}")
|
||||
|
||||
# 创建多边形
|
||||
polygon = Polygon(exterior=[
|
||||
Point(120.0, 30.0),
|
||||
Point(121.0, 30.0),
|
||||
Point(121.0, 31.0),
|
||||
Point(120.0, 31.0),
|
||||
Point(120.0, 30.0)
|
||||
])
|
||||
print(f"\n多边形对象:")
|
||||
print(f" {polygon}")
|
||||
print(f" 面积 = {polygon.area:.4f}")
|
||||
print(f" 质心 = {polygon.centroid}")
|
||||
|
||||
# 点包含测试
|
||||
test_inside = Point(120.5, 30.5)
|
||||
test_outside = Point(121.5, 30.5)
|
||||
print(f" {test_inside} 在多边形内: {polygon.contains_point(test_inside)}")
|
||||
print(f" {test_outside} 在多边形内: {polygon.contains_point(test_outside)}")
|
||||
|
||||
# ========================================================================
|
||||
# 2. 空间特征
|
||||
# ========================================================================
|
||||
print("\n\n[部分 2] 空间特征")
|
||||
print("-" * 50)
|
||||
|
||||
feature1 = SpatialFeature(
|
||||
id="poi_001",
|
||||
geometry=Point(120.5, 30.2),
|
||||
properties={
|
||||
"name": "杭州西湖",
|
||||
"type": "scenic_spot",
|
||||
"rating": 4.8
|
||||
}
|
||||
)
|
||||
|
||||
feature2 = SpatialFeature(
|
||||
id="zone_001",
|
||||
geometry=polygon,
|
||||
properties={
|
||||
"name": "开发区A",
|
||||
"type": "industrial_zone",
|
||||
"area_ha": 10000
|
||||
}
|
||||
)
|
||||
|
||||
print(f"\n特征1: {feature1}")
|
||||
print(f" 属性: {feature1.properties}")
|
||||
print(f" 包围盒: {feature1.envelope()}")
|
||||
|
||||
print(f"\n特征2: {feature2}")
|
||||
print(f" 属性: {feature2.properties}")
|
||||
print(f" 包围盒: {feature2.envelope()}")
|
||||
|
||||
# GeoJSON输出
|
||||
print(f"\nGeoJSON输出:")
|
||||
print(json.dumps(feature1.to_geojson(), ensure_ascii=False, indent=2))
|
||||
|
||||
# ========================================================================
|
||||
# 3. 空间关系
|
||||
# ========================================================================
|
||||
print("\n\n[部分 3] 空间关系")
|
||||
print("-" * 50)
|
||||
|
||||
# 点与多边形的关系
|
||||
point_a = Point(120.5, 30.5) # 在多边形内
|
||||
point_b = Point(121.5, 30.5) # 在多边形外
|
||||
point_c = Point(120.0, 30.5) # 在边界上
|
||||
|
||||
print(f"\n点-多边形关系测试:")
|
||||
print(f" {point_a} 与多边形: {calculate_relation(point_a, polygon)}")
|
||||
print(f" {point_b} 与多边形: {calculate_relation(point_b, polygon)}")
|
||||
print(f" {point_c} 与多边形: {calculate_relation(point_c, polygon)}")
|
||||
|
||||
# 多边形-多边形关系
|
||||
poly2 = Polygon(exterior=[
|
||||
Point(120.5, 29.5),
|
||||
Point(121.5, 29.5),
|
||||
Point(121.5, 30.5),
|
||||
Point(120.5, 30.5),
|
||||
Point(120.5, 29.5)
|
||||
])
|
||||
print(f"\n多边形-多边形关系:")
|
||||
print(f" poly1 与 poly2: {calculate_relation(polygon, poly2)}")
|
||||
|
||||
# ========================================================================
|
||||
# 4. 空间索引
|
||||
# ========================================================================
|
||||
print("\n\n[部分 4] R树空间索引")
|
||||
print("-" * 50)
|
||||
|
||||
# 创建R树
|
||||
rtree = RTree(max_children=4)
|
||||
|
||||
# 插入一些特征
|
||||
features = []
|
||||
for i in range(20):
|
||||
x = random.uniform(119, 122)
|
||||
y = random.uniform(29, 32)
|
||||
feature = SpatialFeature(
|
||||
id=f"feature_{i:03d}",
|
||||
geometry=Point(x, y),
|
||||
properties={"value": random.uniform(0, 100)}
|
||||
)
|
||||
features.append(feature)
|
||||
rtree.insert(feature)
|
||||
|
||||
print(f"\n已插入 {rtree.size} 个特征到R树")
|
||||
|
||||
# 范围查询
|
||||
query_env = Envelope(120, 121, 30, 31)
|
||||
results = rtree.query(query_env)
|
||||
print(f"\n范围查询 {query_env}:")
|
||||
print(f" 找到 {len(results)} 个特征")
|
||||
for f in results[:5]:
|
||||
print(f" - {f.id}: {f.geometry}")
|
||||
|
||||
# K近邻查询
|
||||
query_point = Point(120.5, 30.5)
|
||||
neighbors = rtree.nearest_neighbor(query_point, k=5)
|
||||
print(f"\nK近邻查询 (中心点: {query_point}):")
|
||||
for i, (f, dist) in enumerate(neighbors, 1):
|
||||
print(f" {i}. {f.id}: 距离 = {dist:.4f}")
|
||||
|
||||
# ========================================================================
|
||||
# 5. 栅格场模型
|
||||
# ========================================================================
|
||||
print("\n\n[部分 5] 栅格场模型")
|
||||
print("-" * 50)
|
||||
|
||||
# 创建栅格场
|
||||
grid = GridField(
|
||||
bounds=Envelope(119, 122, 29, 32),
|
||||
rows=30,
|
||||
cols=30,
|
||||
nodata=-9999
|
||||
)
|
||||
|
||||
# 填充一些模拟数据 (温度场)
|
||||
for r in range(grid.rows):
|
||||
for c in range(grid.cols):
|
||||
# 计算坐标
|
||||
x = grid.bounds.min_x + (c + 0.5) * grid.cell_width
|
||||
y = grid.bounds.max_y - (r + 0.5) * grid.cell_height
|
||||
|
||||
# 模拟温度场 (简单的径向基函数)
|
||||
center_x, center_y = 120.5, 30.5
|
||||
dist = math.sqrt((x - center_x)**2 + (y - center_y)**2)
|
||||
temp = 25 - dist * 2 # 中心25度，向外递减
|
||||
|
||||
grid.set_value(r, c, round(temp, 2))
|
||||
|
||||
print(f"\n栅格场信息:")
|
||||
print(f" 范围: {grid.bounds}")
|
||||
print(f" 尺寸: {grid.rows} x {grid.cols}")
|
||||
print(f" 单元格大小: {grid.cell_width:.4f} x {grid.cell_height:.4f}")
|
||||
|
||||
stats = grid.get_statistics()
|
||||
print(f" 统计: {stats}")
|
||||
|
||||
# 点查询
|
||||
sample_point = Point(120.5, 30.5)
|
||||
value = grid.get_value_at_point(sample_point)
|
||||
print(f"\n点位置 {sample_point} 的值: {value}")
|
||||
|
||||
# ========================================================================
|
||||
# 6. GeoJSON导出
|
||||
# ========================================================================
|
||||
print("\n\n[部分 6] GeoJSON导出")
|
||||
print("-" * 50)
|
||||
|
||||
# 创建特征集合
|
||||
feature_collection = {
|
||||
"type": "FeatureCollection",
|
||||
"features": [
|
||||
feature1.to_geojson(),
|
||||
feature2.to_geojson()
|
||||
]
|
||||
}
|
||||
|
||||
print("\n特征集合:")
|
||||
print(json.dumps(feature_collection, ensure_ascii=False, indent=2))
|
||||
|
||||
print("\n" + "="*70)
|
||||
print("演示完成!")
|
||||
print("="*70)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user