问题描述
我想使用 Overpy 返回 OSM 数据的地理数据框。使用 api.get 语句中的 verbosity = 'geom' 可以很容易地使用 overpass 和匀称地做到这一点，但是 overpass 不允许您处理关系（据我所知）
所以我正在使用以下查询处理 overpy：
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（粘贴在下面的代码段）......我想将其转换为地理数据框或 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': [278755095,4988895563,4988895561,4988895562,278755096,4988895559,278755097,4988895560,4988895557,4988895558,4988895556,278755098,4988895555,4988895554,4988895553,278755099,278755100,278755101,278755102,278755103,5986523217,5986523216,278755104,278755105,5986523215,5986523214,5986523212,5986523213,278755106,278755107,278755108,278755109,278755110,278755111,278755112,278755113,278755114,278755115,278755116,278755117,278755118,278755119,278755120,278755124,278755125,278755126,278755130,278755131,278755132,278755133,278755134,278755135,278755136,278755137,4988895564,4988895566,4988895565,4988895568,4988895567,4988895569,278755095],'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},{'lat': 64.7345084,'lon': -19.7429107},{'lat': 64.7358988,'lon': -19.7458491},{'lat': 64.7356206,'lon': -19.7496168},{'lat': 64.7360086,'lon': -19.7512644},{'lat': 64.7357056,'lon': -19.7537414},{'lat': 64.7381456,'lon': -19.7541846},{'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},{'lat': 64.7470604,'lon': -19.7232051},{'lat': 64.7448633,{'lat': 64.7488179,'lon': -19.7170253},{'lat': 64.7521862,{'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': 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[{'lat': 64.7401232,'lon': -17.8838301},{'lat': 64.7392308,'lon': -17.8813838},{'lat': 64.7386814,'lon': -17.8779721},{'lat': 64.738425,'lon': -17.8746032},{'lat': 64.7377931,'lon': -17.8736591},{'lat': 64.7368224,'lon': -17.8739595},{'lat': 64.7361997,'lon': -17.8749251},{'lat': 64.7355678,'lon': -17.8799462},{'lat': 64.7360074,'lon': -17.8840875},{'lat': 64.7357784,'lon': -17.8851818},{'lat': 64.7353846,'lon': -17.8852677},{'lat': 64.734771,'lon': -17.8850745},{'lat': 64.7344047,{'lat': 64.7343131,'lon': -17.8857827},{'lat': 64.7343314,'lon': -17.8867697},{'lat': 64.7345329,'lon': -17.8877997},{'lat': 64.7350183,'lon': -17.8887438},{'lat': 64.7350549,'lon': -17.8896236},{'lat': 64.7348993,'lon': -17.8906321},{'lat': 64.7349725,'lon': -17.892456},'lon': -17.8928422},{'lat': 64.7341025,'lon': -17.893486},{'lat': 64.7340842,'lon': -17.8941726},{'lat': 64.7343955,'lon': -17.89649},{'lat': 64.7349084,'lon': -17.8969192},{'lat': 64.7352289,'lon': -17.8966188},{'lat': 64.7349908,'lon': 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64.7270517,'lon': -17.9189116},{'lat': 64.7265405,'lon': -17.9256704},{'lat': 64.7261613,'lon': -17.9287216},{'lat': 64.7253533,'lon': -17.9327382},{'lat': 64.7232095,'lon': -17.9360983},{'lat': 64.7215109,'lon': -17.9417371},{'lat': 64.7207192,'lon': -17.9475304},{'lat': 64.720241,'lon': -17.9532078},{'lat': 64.7206698,'lon': -17.9567996},{'lat': 64.7213625,'lon': -17.9596963},{'lat': 64.7214284,'lon': -17.9635198},{'lat': 64.7207522,'lon': -17.9679227},{'lat': 64.7195482,'lon': -17.9693903},{'lat': 64.7163648,'lon': -17.9710511},{'lat': 64.7138244,'lon': -17.9694676},{'lat': 64.7129996,'lon': -17.969931},{'lat': 64.7145668,'lon': -17.9714759},{'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},{'lat': 64.7284532,'lon': -17.9794706},{'lat': 64.7299701,{'lat': 64.7316022,'lon': -17.9786982},{'lat': 64.7339267,'lon': -17.9808224},{'lat': 64.7354762,'lon': -17.9829466},{'lat': 64.7364322,'lon': -17.9823287},{'lat': 64.7375201,'lon': -17.9840666},{'lat': 64.7392672,'lon': -17.9839894},{'lat': 64.7401077,'lon': -17.9789686},{'lat': 64.7416569,'lon': -17.9761878},{'lat': 64.7428764,'lon': -17.9747588},{'lat': 64.7454141,'lon': -17.9707807},{'lat': 64.7466499,'lon': -17.9663392},{'lat': 64.7482316,'lon': -17.9666482},{'lat': 64.7476055,'lon': -17.9703945},{'lat': 64.7462544,'lon': -17.9758402},'lon': -17.9805134},{'lat': 64.7487259,'lon': -17.984028},{'lat': 64.7518066,'lon': -17.9851867},{'lat': 64.7542282,{'lat': 64.7537669,'lon': -17.9734843},{'lat': 64.7535401,'lon': -17.9713788},{'lat': 64.7519878,'lon': -17.9696221},解决方法
如果您知道返回的所有元素都是多边形（在您的示例中似乎就是这种情况），您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到一个列表。 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)