利用Elasticsearch实现地理位置、城市搜索服务

标签：index geoshape polygon 地理位置 type 搜索 province2023 centroid Elasticsearch

最近用到一些简单的地理位置查询接口，基于当前定位获取用户所在位置信息（省市区），然后基于该信息查询当前区域的......提供服务。

然后就自己研究了下GIS，作为一个程序员。自己能不能实现这个功能呢？答案当然是可以。立即开干。

思路：找到数据，写入数据库，利用Elasticsearch强大的搜索能力和丰富发热GIS数据处理能力实现。

GIS相关专业信息参考（bd上找到，还算专业）：程序员GIS入门|前后端都要懂一点的GIS知识

经过一番寻找，“功夫不负有心人”，在网上找到了锐多宝 数据，比较完整。下载下来，格式是shape格式。

第一步：下载数据，从锐多宝下载

第二步：写python脚本预处理数据：ShapFile 转 GeoJSON，ES处理GeoJSON比较强

import geopandas as gpd

# 读取 Shapefile
shapefile_path = 'D:/data/gis/2023年_CTAmap_1.12版/2023年省级/2023年省级.shp'
gdf = gpd.read_file(shapefile_path)

# 检查 GeoDataFrame
print(gdf.head())

# 如果需要，可以对数据进行预处理，比如过滤、选择特定列等
# gdf = gdf[['column1', 'column2', 'geometry']]

# 将 GeoDataFrame 转换为标准的 Pandas DataFrame (如果需要的话)
df = gdf.drop('geometry', axis=1).join(gdf['geometry'].apply(lambda x: gpd.GeoSeries(x).to_json()))

# 将 Pandas DataFrame 导出为 JSON 文件
output_json_path = 'D:/data/gis/2023-province-GeoJSON.gesjson'
# df.to_json(output_json_path, orient='records')

# 如果你想保留 GeoJSON 格式，可以直接保存 GeoDataFrame
gdf.to_file(output_json_path, driver='GeoJSON')

第三步：利用Python脚本将GeoJSON写入Elasticsearch

from elasticsearch import Elasticsearch
from elasticsearch.helpers import bulk
import json

# 连接到 Elasticsearch
es = Elasticsearch("http://localhost:9200")

# 检查连接
if not es.ping():
    raise ValueError("Connection failed")

# 删除旧索引（如果存在）
if es.indices.exists(index="province2023_geoshape_index_001"):
    es.indices.delete(index="province2023_geoshape_index_001")

# 创建索引并定义 Mapping
mapping = {
    "mappings": {
        "properties": {
            "location": {
                "type": "geo_shape"
            },
            "name": {
                "type": "text"
            }
        }
    }
}

# 创建索引
es.indices.create(index="province2023_geoshape_index_001", body=mapping)

# 读取 GeoJSON 文件
with open("D:/data/gis/2023-province-GeoJSON.gesjson", "r", encoding="utf-8") as file:
    geojson_data = json.load(file)

# 提取 GeoJSON 特征集合
features = geojson_data.get("features", [])

# 准备数据以供导入
documents = []
for feature in features:
    doc = {
        "location": {
            "type": feature["geometry"]["type"],
            "coordinates": feature["geometry"]["coordinates"]
        }
    }
    if "properties" in feature:
        doc.update(feature["properties"])
    documents.append(doc)

# 定义批量大小
batch_size = 100  # 每次批量导入的数量


# 准备 actions
def generate_actions(documents):
    for doc in documents:
        yield {
            "_index": "province2023_geoshape_index_001",
            "_source": doc
        }


# 分批执行批量导入
for i in range(0, len(documents), batch_size):
    end = min(i + batch_size, len(documents))
    success, _ = bulk(es, generate_actions(documents[i:end]))
    print(f"Bulk {i}-{end} completed, {success} documents indexed.")

print("All data indexed.")

第四步：计算出每条数据的区域的中心点（扩展功能，原始数据只有polygon多边形数据）

from elasticsearch import Elasticsearch
from elasticsearch.helpers import bulk
import json
import ssl

# 连接到 Elasticsearch
es = Elasticsearch("http://localhost:9200")

# 检查连接
if not es.ping():
    raise ValueError("Connection failed")

# 删除旧索引（如果存在）
if es.indices.exists(index="province2023_centroid_geoshape_index_001"):
    es.indices.delete(index="province2023_centroid_geoshape_index_001")

# 创建索引并定义 Mapping
mapping = {
    "mappings": {
        "properties": {
            "location": {
                "type": "geo_shape"
            },
            "centroid": {  # 新增字段
                "type": "geo_point"
            },
            "name": {
                "type": "text"
            }
        }
    }
}

# 创建索引
es.indices.create(index="province2023_centroid_geoshape_index_001", body=mapping)

# 读取 GeoJSON 文件
with open("D:/data/gis/2023-province-GeoJSON.gesjson", "r", encoding="utf-8") as file:
    geojson_data = json.load(file)

# 提取 GeoJSON 特征集合
features = geojson_data.get("features", [])


def calculate_centroid(polygons):
    total_area = 0.0
    total_x = 0.0
    total_y = 0.0

    for polygon in polygons:
        # 现在 polygon 是一个包含多个坐标的列表
        centroid = calculate_simple_polygon_centroid(polygon)
        area = calculate_polygon_area(polygon)

        total_area += area
        total_x += centroid[0] * area
        total_y += centroid[1] * area

    if total_area == 0:
        # 如果总面积为零，则返回原点作为中心点
        return [0, 0]
    else:
        return [total_x / total_area, total_y / total_area]


# is_coordinates_list方法
# 以下结构返回True，polygon 是一个包含坐标列表的列表
# [
#     [[x1, y1], [x2, y2], [x3, y3], ...],
#     [[x1, y1], [x2, y2], [x3, y3], ...]  # 如果有内部孔洞
# ]
# 以下结构返回Fasle，包含单个坐标的列表
# [
#     [x1, y1],
#     [x2, y2],
#     [x3, y3],
#     ...
# ]

def is_coordinate(coord):
    return (
            isinstance(coord, (list, tuple)) and
            len(coord) == 2 and
            all(isinstance(c, (int, float)) for c in coord)
    )


def is_coordinates_list(coords):
    # 检查 coords 是否是一个包含坐标列表的列表
    if isinstance(coords, list):
        if all(isinstance(c, list) and all(is_coordinate(coord) for coord in c) for c in coords):
            return True
    return False


def calculate_simple_polygon_centroid(polygon):
    # 确定 polygon 的结构
    if is_coordinates_list(polygon):
        # polygon 是一个包含坐标列表的列表
        x_sum = sum(coord[0] for coord in polygon[0])
        y_sum = sum(coord[1] for coord in polygon[0])
        num_points = len(polygon[0])
    else:
        # print(False, polygon[0])
        # polygon 是一个包含多个坐标的列表
        x_sum = sum(coord[0] for coord in polygon)
        y_sum = sum(coord[1] for coord in polygon)
        num_points = len(polygon)
    # 计算平均坐标
    centroid_x = x_sum / num_points
    centroid_y = y_sum / num_points

    return [centroid_x, centroid_y]


def calculate_polygon_area(polygon):
    # 计算简单多边形的面积
    area = 0.0
    if is_coordinates_list(polygon):  # polygon 是一个包含坐标列表的列表
        num_points = len(polygon[0])
        for i in range(num_points):
            j = (i + 1) % num_points
            area += polygon[0][i][0] * polygon[0][j][1]
            area -= polygon[0][j][0] * polygon[0][i][1]

    else:  # polygon 是一个包含多个坐标的列表
        num_points = len(polygon)
        for i in range(num_points):
            j = (i + 1) % num_points
            area += polygon[i][0] * polygon[j][1]
            area -= polygon[j][0] * polygon[i][1]
    return abs(area) / 2.0


# 准备数据以供导入
documents = []
for feature in features:
    # 检查坐标是否在有效范围内
    coordinates = feature["geometry"]["coordinates"]
    centroid = calculate_centroid(coordinates)

    doc = {
        "location": {
            "type": feature["geometry"]["type"],
            "coordinates": coordinates
        },
        "centroid": centroid,  # 添加中心点
    }
    if "properties" in feature:
        doc.update(feature["properties"])
    documents.append(doc)

# 定义批量大小
batch_size = 100  # 每次批量导入的数量


# 准备 actions
def generate_actions(documents):
    for doc in documents:
        yield {
            "_index": "district2023_centroid_geoshape_index_001",
            "_source": doc
        }


# 分批执行批量导入
for i in range(0, len(documents), batch_size):
    end = min(i + batch_size, len(documents))
    success, errors = bulk(es, generate_actions(documents[i:end]))
    if errors:
        print(f"Bulk {i}-{end} completed, {success} documents indexed, but {len(errors)} documents failed.")
        for error in errors:
            print(error)
    else:
        print(f"Bulk {i}-{end} completed, {success} documents indexed.")

print("All data indexed.")

第五步：利用elasticsearch的pipeline和reindex能力预处理数据

# geo_centroid 聚合是一种高级聚合，它可以计算一组地理位置的中心点。在 Elasticsearch 中，这个功能属于高级特性，通常只在 X-Pack（现在称为 Elastic Security 和 Elastic Observability）的许可证中可用。
# 试用30天可以体验
POST /province2023_geoshape_index_001/_search
{
  "size": 0,
  "aggs": {
    "centroid": {
      "geo_centroid": {
        "field": "location"
      }
    }
  }
}


POST province2023_centroid_geoshape_index_001/_search
{
  "query": {
    "term": {
      "省.keyword": {
        "value": "陕西省" 
      }
    }
  }
}

PUT _ingest/pipeline/copy_field_pipeline
{
  "description": "Copy the value of one field to another",
  "processors": [
    {
      "copy": {
        "from": "省", 
        "to": "province_name"
      }
    }
  ]
}
GET province2023_centroid_geoshape_index_001/_mapping

GET province2023_centroid_geoshape_index_001/_mapping


PUT _ingest/pipeline/province_multiple_copy_fields_pipeline
{
  "description": "Copy multiple fields to new fields and rename fields to new fields",
  "processors": [
    {
      "set": {
        "field": "province_name",
        "value": "{{{省}}}"
      }
    },
    {
      "remove": {
        "field": "省"
      }
    },
    {
      "rename": {
        "field": "省级码",
        "target_field": "province_code"
      }
    },
    {
      "rename": {
        "field": "省类型",
        "target_field": "province_type"
      }
    },
    {
      "rename": {
        "field": "VAR_NAME",
        "target_field": "var_name"
      }
    },
    {
      "rename": {
        "field": "ENG_NAME",
        "target_field": "eng_name"
      }
    },
    {
      "rename": {
        "field": "FIRST_GID",
        "target_field": "first_gid"
      }
    },
    {
      "rename": {
        "field": "FIRST_TYPE",
        "target_field": "first_type"
      }
    }
  ]
}

GET province2023_centroid_geoshape_index_002/_count

GET province2023_centroid_geoshape_index_002/_mapping
DELETE province2023_centroid_geoshape_index_002

PUT province2023_centroid_geoshape_index_002
{
  "mappings": {
    "properties": {
      "eng_name": {
        "type": "text",
        "fields": {
          "keyword": {
            "type": "keyword",
            "ignore_above": 256
          }
        }
      },
      "first_gid": {
        "type": "text",
        "fields": {
          "keyword": {
            "type": "keyword",
            "ignore_above": 256
          }
        }
      },
      "first_type": {
        "type": "text",
        "fields": {
          "keyword": {
            "type": "keyword",
            "ignore_above": 256
          }
        }
      },
      "var_name": {
        "type": "text",
        "fields": {
          "keyword": {
            "type": "keyword",
            "ignore_above": 256
          }
        }
      },
      "centroid": {
        "type": "geo_point"
      },
      "location": {
        "type": "geo_shape"
      },
      "name": {
        "type": "text"
      },
      "year": {
        "type": "text",
        "fields": {
          "keyword": {
            "type": "keyword",
            "ignore_above": 256
          }
        }
      }
    }
  }
}

POST _reindex
{
  "source": {
    "index": "province2023_centroid_geoshape_index_001"
  },
  "dest": {
    "index": "province2023_centroid_geoshape_index_002",
    "pipeline": "province_multiple_copy_fields_pipeline"
  }
}

GET province2023_centroid_geoshape_index_002/_search

第六步：查询数据 geo_distance

# centroid字段的type是 geo_point，存储的经纬度形式是数组Geopoint as an array
# geo_bounding_box 可查找边框内的所有地理坐标点。
POST province2023_centroid_geoshape_index_002/_search
{
  "query": {
    "geo_bounding_box": { 
      "centroid": {
        "top_left": {
          "lat": 42,
          "lon": -72
        },
        "bottom_right": {
          "lat": 40,
          "lon": -74
        }
      }
    }
  }
}

POST province2023_centroid_geoshape_index_002/_search
{
  "query": {
    "geo_distance": {
      "distance": 100,
      "centroid": {
        "lat": 40.09937484066758,
        "lon": 116.41960604340115
      }
    }
  }
}

POST province2023_centroid_geoshape_index_002/_search
{
  "query": {
    "bool": {
      "must": {
        "match": {
          "province_name":"xx市"
        }
      },
      "filter": {
        "geo_distance": {
          "distance": "2km",
          "centroid": {
            "lat": 40.09937484066758,
            "lon": 116.41960604340115
          }
        }
      }
    }
  }
}


POST province2023_centroid_geoshape_index_002/_search
{
  "query": {
    "bool": {
      "must": {
        "match": {
          "province_name":"xx市"
        }
      },
      "filter": {
        "geo_distance": {
          "distance": "200km",
          "location": {
            "lat": 40.09937484066758,
            "lon": 116.41960604340115
          }
        }
      }
    }
  }
}

标签：index,geoshape,polygon,地理位置,type,搜索,province2023,centroid,Elasticsearch
From： https://www.cnblogs.com/hbuuid/p/18327500