python OSM重叠几何

hivapdat 于 2023-04-19 发布在 Python

关注(0)|答案(2)|浏览(93)

我想使用Overpy返回OSM数据的Geo Dataframe 。这很容易使用override和shapely在API中使用verbosity = 'geom'来完成，但override不允许您使用关系（据我所知）
因此，我使用overpy进行以下查询：

import overpy
import requests
import json
import geojson

overpass_url = "http://overpass-api.de/api/interpreter"
overpass_query = """
[out:json];
area["ISO3166-1"="IS"][admin_level=2];
(
 way["natural"="glacier"](area);
 rel["natural"="glacier"](area);
);
out geom;
"""
response = requests.get(overpass_url, 
                        params={'data': overpass_query})
response.raise_for_status()
data = response.json()

这将返回一个dict（下面粘贴的片段）......我想将其转换为geodaframe或geojson格式：

{'version': 0.6,
 'generator': 'Overpass API 0.7.56.8 7d656e78',
 'osm3s': {'timestamp_osm_base': '2021-02-15T15:22:03Z',
  'timestamp_areas_base': '2021-02-15T15:11:02Z',
  'copyright': 'The data included in this document is from www.openstreetmap.org. The data is made available under ODbL.'},
 'elements': [{'type': 'way',
   'id': 25572229,
   'bounds': {'minlat': 64.7317767,
    'minlon': -19.7595973,
    'maxlat': 64.7600927,
    'maxlon': -19.6928364},
   'nodes': [278755095,
    4988895563,
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    278755095],
   'geometry': [{'lat': 64.7332811, 'lon': -19.7278524},
    {'lat': 64.7329093, 'lon': -19.7341986},
    {'lat': 64.7334309, 'lon': -19.7383334},
    {'lat': 64.7342309, 'lon': -19.7400297},
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    {'lat': 64.7358988, 'lon': -19.7458491},
    {'lat': 64.7356206, 'lon': -19.7496168},
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    {'lat': 64.7357056, 'lon': -19.7537414},
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    {'lat': 64.7385295, 'lon': -19.7531667},
    {'lat': 64.7394417, 'lon': -19.7538338},
    {'lat': 64.7407874, 'lon': -19.7544468},
    {'lat': 64.741594, 'lon': -19.7539147},
    {'lat': 64.7429881, 'lon': -19.7545682},
    {'lat': 64.744698, 'lon': -19.7551248},
    {'lat': 64.7508682, 'lon': -19.7575374},
    {'lat': 64.7555541, 'lon': -19.7595973},
    {'lat': 64.7598548, 'lon': -19.7564644},
    {'lat': 64.7600927, 'lon': -19.7548981},
    {'lat': 64.7583663, 'lon': -19.7500575},
    {'lat': 64.7597724, 'lon': -19.7487397},
    {'lat': 64.7573843, 'lon': -19.7443194},
    {'lat': 64.7569176, 'lon': -19.7436757},
    {'lat': 64.7538784, 'lon': -19.7439045},
    {'lat': 64.7508483, 'lon': -19.737696},
    {'lat': 64.7489433, 'lon': -19.7360508},
    {'lat': 64.7488348, 'lon': -19.7349065},
    {'lat': 64.7502824, 'lon': -19.7324748},
    {'lat': 64.7551148, 'lon': -19.724235},
    {'lat': 64.7589216, 'lon': -19.7129054},
    {'lat': 64.7568718, 'lon': -19.7125621},
    {'lat': 64.7518933, 'lon': -19.7225184},
    {'lat': 64.7489644, 'lon': -19.7218318},
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    {'lat': 64.7545291, 'lon': -19.7074122},
    {'lat': 64.752772, 'lon': -19.7022624},
    {'lat': 64.7468923, 'lon': -19.7110574},
    {'lat': 64.7471989, 'lon': -19.7043811},
    {'lat': 64.746467, 'lon': -19.7010805},
    {'lat': 64.7449527, 'lon': -19.7017042},
    {'lat': 64.7428125, 'lon': -19.7032924},
    {'lat': 64.7410546, 'lon': -19.7101588},
    {'lat': 64.7377485, 'lon': -19.7079655},
    {'lat': 64.7356578, 'lon': -19.6992603},
    {'lat': 64.7362158, 'lon': -19.694227},
    {'lat': 64.7356134, 'lon': -19.6928364},
    {'lat': 64.7351069, 'lon': -19.6939175},
    {'lat': 64.7337628, 'lon': -19.6957344},
    {'lat': 64.7317767, 'lon': -19.6957953},
    {'lat': 64.7329861, 'lon': -19.703479},
    {'lat': 64.736968, 'lon': -19.7112033},
    {'lat': 64.7357176, 'lon': -19.7191862},
    {'lat': 64.7358998, 'lon': -19.7209692},
    {'lat': 64.7344614, 'lon': -19.7209821},
    {'lat': 64.7335286, 'lon': -19.7222981},
    {'lat': 64.732916, 'lon': -19.7276224},
    {'lat': 64.7332811, 'lon': -19.7278524}],
   'tags': {'alt_name': 'Regnbúðajökull',
    'name': 'Hrútfellsjökull',
    'natural': 'glacier'}},
  {'type': 'way',
   'id': 250745561,
   'bounds': {'minlat': 64.7129996,
    'minlon': -17.9851867,
    'maxlat': 64.7859787,
    'maxlon': -17.8182245},
   'nodes': [8117616831,
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    {'lat': 64.7163483, 'lon': -17.9731753},
    {'lat': 64.7185751, 'lon': -17.9742567},
    {'lat': 64.7201255, 'lon': -17.9746815},
    {'lat': 64.722319, 'lon': -17.9763036},
    {'lat': 64.7243309, 'lon': -17.9801272},
    {'lat': 64.7257985, 'lon': -17.9799341},
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    {'lat': 64.7354762, 'lon': -17.9829466},
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    {'lat': 64.7519878, 'lon': -17.9696221},

python

来源：https://stackoverflow.com/questions/66210813/osm-overpy-geometries

2条答案

按热度按时间

7uhlpewt1#

如果你知道返回的所有元素都是多边形（在你的例子中似乎是这样），你可以编写一个函数，将立交桥API返回的每个元素转换为GeoJSON Feature，以便将所有这些特征收集在一个列表中。然后很容易为这个特征列表创建一个GeoDataFrame：

import geopandas as gpd

def elem_to_feature(elem):
    return {
        "geometry": {
                "type": "Polygon",
                "coordinates": [[[d["lon"], d["lat"]] for d in elem["geometry"]]]
        },
        "properties": elem["tags"],
    }

# the 'data' variable is from the code in the question
features = [elem_to_feature(elem) for elem in data["elements"]]

gdf = gpd.GeoDataFrame.from_features(features)

赞(0）回复(0）举报 2023-04-19

pcrecxhr2#

我知道这是一个古老的故事，但仍然：你可以这样使用osm2geojson。

import osm2geojson    

    overpass_url = "http://overpass-api.de/api/interpreter"
    response = requests.get(overpass_url, params={'data': request})
    if response.status_code == 200:
        # convert result to GeoDataFrame
        data = response.json()
        geojson = osm2geojson.json2geojson(data)
        geojsonDF = gpd.GeoDataFrame.from_features(geojson)

赞(0）回复(0）举报 2023-04-19

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python OSM重叠几何

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