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GeoPandas入门 | 03-空间关系和操作

發布時間：2024/8/1 编程问答 26 豆豆

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03-空間關系和操作

源代碼請看此處

%matplotlib inlineimport pandas as pd import geopandas countries = geopandas.read_file("zip://./data/ne_110m_admin_0_countries.zip") cities = geopandas.read_file("zip://./data/ne_110m_populated_places.zip") rivers = geopandas.read_file("zip://./data/ne_50m_rivers_lake_centerlines.zip")

3.1 空間關系

地理空間數據的一個重要方面是我們可以觀察 spatial relationships ：兩個空間對象如何相互關聯(它們是否重疊、相交、包含……)

地理信息系統中的拓撲關系、集合論關系通常基于DE-9IM模型，更多信息請見 https://en.wikipedia.org/wiki/Spatial_relation

(Image by Krauss, CC BY-SA 3.0 )

3.2 單個對象之間的空間關系

首先創建一些空間對象:

多邊形：使用 .squeeze() 從 GeoSeries 中提取幾何對象向量

belgium=countries.loc[countries['name']=='Belgium','geometry']#.squeeze() # .squeeze()從數組的形狀中刪除單維度條目，即把shape中為1的維度去掉 type(belgium) geopandas.geoseries.GeoSeries belgium=countries.loc[countries['name']=='Belgium','geometry'].squeeze() type(belgium) shapely.geometry.polygon.Polygon belgium

兩個點對象

paris=cities.loc[cities['name']=='Paris','geometry'].squeeze() brussels = cities.loc[cities['name'] == 'Brussels', 'geometry'].squeeze() paris

brussels

線段

from shapely.geometry import LineString line = LineString([paris, brussels]) line

把這4個幾何對象一起可視化（把它們放在一個GeoSeries中，以便于用 geopandas.plot() 方法把它們一起顯示出來）

geopandas.GeoSeries([belgium,paris,brussels,line]).plot(cmap='tab10') <matplotlib.axes._subplots.AxesSubplot at 0x7efc106f38d0>

可以認出比利時的抽象形狀

比利時首都布魯塞爾位于比利時境內。這是一種空間關系，我們可以使用以下單個的幾何對象進行測試

brussels.within(belgium) True belgium.contains(brussels) True belgium.contains(paris) False paris.within(belgium) False

從巴黎到布魯塞爾所畫的直線并不完全位于比利時境內，但它確實與比利時相交

belgium.contains(line) False line.intersects(belgium) True

3.3 GeoDataFrame中的空間關系

如上所述，在單個 shapely 矢量對象上可用的方法，也可作為 GeoSeries / GeoDataFrame 對象的方法

例如，如果我們在世界數據集上調用帶有 paris 點的 contains 方法，它將對 world 數據框架中的每個國家進行這種空間檢查

countries.contains(paris) 0 False 1 False 2 False 3 False 4 False... 172 False 173 False 174 False 175 False 176 False Length: 177, dtype: bool

因為上面給了我們一個布爾結果，我們可以用它來篩選GeoDataFrame

countries[countries.contains(paris)]

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事實上，法國是世界上唯一一個巴黎所在的國家

又如，提取南美洲亞馬遜河的線路圖，我們可以查詢河流流經哪些國家。

amazon = rivers[rivers['name'] == 'Amazonas'].geometry.squeeze() amazon

countries[countries.crosses(amazon)]

<div> <style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

.dataframe tbody tr th {vertical-align: top; }.dataframe thead th {text-align: right; }

</style> <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>iso_a3</th> <th>name</th> <th>continent</th> <th>pop_est</th> <th>gdp_md_est</th> <th>geometry</th> </tr> </thead> <tbody> <tr> <th>22</th> <td>BRA</td> <td>Brazil</td> <td>South America</td> <td>207353391.0</td> <td>3081000.0</td> <td>POLYGON ((-57.62513 -30.21629, -56.29090 -28.8...</td> </tr> <tr> <th>35</th> <td>COL</td> <td>Colombia</td> <td>South America</td> <td>47698524.0</td> <td>688000.0</td> <td>POLYGON ((-66.87633 1.25336, -67.06505 1.13011...</td> </tr> <tr> <th>124</th> <td>PER</td> <td>Peru</td> <td>South America</td> <td>31036656.0</td> <td>410400.0</td> <td>POLYGON ((-69.52968 -10.95173, -68.66508 -12.5...</td> </tr> </tbody> </table> </div>

countries[countries.intersects(amazon)]

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空間關系函數 :

檢查空間關系的不同函數概述（空間謂詞函數）

equals
contains
crosses
disjoint
intersects
overlaps
touches
within
covers

關于這些方法的概述，見 https://shapely.readthedocs.io/en/stable/manual.html#predicates-and-relationships

關于這些操作的語義，詳見 https://en.wikipedia.org/wiki/DE-9IM

3.3 練習

我們將再次使用巴黎的數據集來做一些練習，再次導入

districts = geopandas.read_file("data/paris_districts.geojson").to_crs(epsg=2154) stations = geopandas.read_file("data/paris_bike_stations.geojson").to_crs(epsg=2154)

3.3.1 練習一：埃菲爾鐵塔

埃菲爾鐵塔是一座建于19世紀的鐵格子塔，可能是巴黎最具代表性的景觀

埃菲爾鐵塔的位置是：x：648237.3，y：6862271.9

以埃菲爾鐵塔的坐標創建一個 Shapely 點對象，并將其分配給一個名為 eiffel_tower 的變量，輸出結果
檢查埃菲爾鐵塔是否位于Montparnasse區
檢查 Montparnasse 區是否包含自行車站
計算埃菲爾鐵塔和自行車站之間的距離（注意：在這種情況下，距離以米為單位返回）。

from shapely.geometry import Point eiffel_tower=Point(648237.3,6862271.9) print(eiffel_tower) POINT (648237.3 6862271.9) district_montparnasse=districts[districts['district_name']=='Montparnasse']['geometry'].squeeze() district_montparnasse

# 檢查埃菲爾鐵塔是否位于Montparnasse區 district_montparnasse.contains(eiffel_tower) False bike_station = stations.loc[293, 'geometry'] bike_station

# 檢查 Montparnasse 區是否包含自行車站 district_montparnasse.contains(bike_station) True # 計算埃菲爾鐵塔和自行車站之間的距離 eiffel_tower.distance(bike_station) 3540.1534488921966

3.3.2 練習二：埃菲爾鐵塔位于哪個區？

在上一個練習中，我們為埃菲爾鐵塔的位置構造了一個 Point 對象，我們檢查了它不在montparnasse蒙帕納斯區，現在我們的問題是確定它位于巴黎的哪個區

創建一個布爾掩模（或篩選器），表示每個區是否包含埃菲爾鐵塔。調用結果 mask 。
用布爾掩碼篩選 districts 數據框并打印結果。

mask=districts.contains(eiffel_tower) districts[mask]

<div> <style scoped> .dataframe tbody tr th:only-of-type { vertical-align: middle; }

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</style> <table border="1" class="dataframe"> <thead> <tr style="text-align: right;"> <th></th> <th>id</th> <th>district_name</th> <th>population</th> <th>geometry</th> </tr> </thead> <tbody> <tr> <th>27</th> <td>28</td> <td>Gros-Caillou</td> <td>25156</td> <td>POLYGON ((649336.752 6861767.761, 649110.815 6...</td> </tr> </tbody> </table> </div>

3.3.3 練習三：找出距離埃菲爾鐵塔最近的自行車站點

現在，我們可能對埃菲爾鐵塔附近的自行車站感興趣。為了探索它們，讓我們將埃菲爾鐵塔本身以及1公里內的自行車站可視化。

要做到這一點，我們可以計算出每個站點到埃菲爾鐵塔的距離。根據這個結果，我們就可以創建一個掩模，如果站點在1km以內，則取 True ，否則取 False ，然后用它來過濾站點的GeoDataFrame。最后，我們對這個子集進行可視化處理。

計算每個站點到埃菲爾鐵塔的距離，并調用結果 dist_eiffel
打印距離最近的車站的距離（這是 dist_eiffel 的最小值）
選擇距離埃菲爾鐵塔小于1公里的 "站點 "GeoDataFrame行（注意，距離是以米為單位）。調用結果 stations_eiffel

dist_eiffel=stations.distance(eiffel_tower) print(dist_eiffel.min()) 232.34672323449053 stations_eiffel=stations[dist_eiffel<1000] ax = stations_eiffel.to_crs(epsg=3857).plot()geopandas.GeoSeries([eiffel_tower], crs='EPSG:2154').to_crs(epsg=3857).plot(ax=ax, color='red') <matplotlib.axes._subplots.AxesSubplot at 0x7efc0e046e48>

import contextily contextily.add_basemap(ax) ---------------------------------------------------------------------------KeyboardInterrupt Traceback (most recent call last)<ipython-input-43-2923a97d25c0> in <module>1 import contextily ----> 2 contextily.add_basemap(ax)/usr/local/lib/python3.6/dist-packages/contextily/plotting.py in add_basemap(ax, zoom, source, interpolation, attribution, attribution_size, reset_extent, crs, resampling, url, **extra_imshow_args)142 # Download image143 image, extent = bounds2img( --> 144 left, bottom, right, top, zoom=zoom, source=source, ll=False145 )146 # Warping/usr/local/lib/python3.6/dist-packages/contextily/tile.py in bounds2img(w, s, e, n, zoom, source, ll, wait, max_retries, url)246 x, y, z = t.x, t.y, t.z247 tile_url = _construct_tile_url(provider, x, y, z) --> 248 image = _fetch_tile(tile_url, wait, max_retries)249 tiles.append(t)250 arrays.append(image)/usr/local/lib/python3.6/dist-packages/joblib/memory.py in __call__(self, *args, **kwargs)563 564 def __call__(self, *args, **kwargs): --> 565 return self._cached_call(args, kwargs)[0]566 567 def __getstate__(self):/usr/local/lib/python3.6/dist-packages/joblib/memory.py in _cached_call(self, args, kwargs, shelving)529 530 if must_call: --> 531 out, metadata = self.call(*args, **kwargs)532 if self.mmap_mode is not None:533 # Memmap the output at the first call to be consistent with/usr/local/lib/python3.6/dist-packages/joblib/memory.py in call(self, *args, **kwargs)725 if self._verbose > 0:726 print(format_call(self.func, args, kwargs)) --> 727 output = self.func(*args, **kwargs)728 self.store_backend.dump_item(729 [func_id, args_id], output, verbose=self._verbose)/usr/local/lib/python3.6/dist-packages/contextily/tile.py in _fetch_tile(tile_url, wait, max_retries)301 @memory.cache302 def _fetch_tile(tile_url, wait, max_retries): --> 303 request = _retryer(tile_url, wait, max_retries)304 with io.BytesIO(request.content) as image_stream:305 image = Image.open(image_stream).convert("RGB")/usr/local/lib/python3.6/dist-packages/contextily/tile.py in _retryer(tile_url, wait, max_retries)441 """442 try: --> 443 request = requests.get(tile_url, headers={"user-agent": USER_AGENT})444 request.raise_for_status()445 except requests.HTTPError:/usr/local/lib/python3.6/dist-packages/requests/api.py in get(url, params, **kwargs)74 75 kwargs.setdefault('allow_redirects', True) ---> 76 return request('get', url, params=params, **kwargs)77 78 /usr/local/lib/python3.6/dist-packages/requests/api.py in request(method, url, **kwargs)59 # cases, and look like a memory leak in others.60 with sessions.Session() as session: ---> 61 return session.request(method=method, url=url, **kwargs)62 63 /usr/local/lib/python3.6/dist-packages/requests/sessions.py in request(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)528 }529 send_kwargs.update(settings) --> 530 resp = self.send(prep, **send_kwargs)531 532 return resp/usr/local/lib/python3.6/dist-packages/requests/sessions.py in send(self, request, **kwargs)641 642 # Send the request --> 643 r = adapter.send(request, **kwargs)644 645 # Total elapsed time of the request (approximately)/usr/local/lib/python3.6/dist-packages/requests/adapters.py in send(self, request, stream, timeout, verify, cert, proxies)447 decode_content=False,448 retries=self.max_retries, --> 449 timeout=timeout450 )451 /usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py in urlopen(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)675 body=body,676 headers=headers, --> 677 chunked=chunked,678 )679 /usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py in _make_request(self, conn, method, url, timeout, chunked, **httplib_request_kw)379 # Trigger any extra validation we need to do.380 try: --> 381 self._validate_conn(conn)382 except (SocketTimeout, BaseSSLError) as e:383 # Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout./usr/local/lib/python3.6/dist-packages/urllib3/connectionpool.py in _validate_conn(self, conn)974 # Force connect early to allow us to validate the connection.975 if not getattr(conn, "sock", None): # AppEngine might not have `.sock` --> 976 conn.connect()977 978 if not conn.is_verified:/usr/local/lib/python3.6/dist-packages/urllib3/connection.py in connect(self)368 ca_cert_data=self.ca_cert_data,369 server_hostname=server_hostname, --> 370 ssl_context=context,371 )372 /usr/local/lib/python3.6/dist-packages/urllib3/util/ssl_.py in ssl_wrap_socket(sock, keyfile, certfile, cert_reqs, ca_certs, server_hostname, ssl_version, ciphers, ssl_context, ca_cert_dir, key_password, ca_cert_data)375 ) or IS_SECURETRANSPORT:376 if HAS_SNI and server_hostname is not None: --> 377 return context.wrap_socket(sock, server_hostname=server_hostname)378 379 warnings.warn(/usr/lib/python3.6/ssl.py in wrap_socket(self, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, session)405 suppress_ragged_eofs=suppress_ragged_eofs,406 server_hostname=server_hostname, --> 407 _context=self, _session=session)408 409 def wrap_bio(self, incoming, outgoing, server_side=False,/usr/lib/python3.6/ssl.py in __init__(self, sock, keyfile, certfile, server_side, cert_reqs, ssl_version, ca_certs, do_handshake_on_connect, family, type, proto, fileno, suppress_ragged_eofs, npn_protocols, ciphers, server_hostname, _context, _session)815 # non-blocking816 raise ValueError("do_handshake_on_connect should not be specified for non-blocking sockets") --> 817 self.do_handshake()818 819 except (OSError, ValueError):/usr/lib/python3.6/ssl.py in do_handshake(self, block)1075 if timeout == 0.0 and block:1076 self.settimeout(None) -> 1077 self._sslobj.do_handshake()1078 finally:1079 self.settimeout(timeout)/usr/lib/python3.6/ssl.py in do_handshake(self)687 def do_handshake(self):688 """Start the SSL/TLS handshake.""" --> 689 self._sslobj.do_handshake()690 if self.context.check_hostname:691 if not self.server_hostname:KeyboardInterrupt:

3.4 空間操作

除了返回布爾值的空間謂詞判斷外，Shapely和GeoPandas還提供了返回新幾何對象的操作

二元操作

緩沖區:

詳見 https://shapely.readthedocs.io/en/stable/manual.html#spatial-analysis-methods。

例如，使用上面的實驗數據，在布魯塞爾周圍構造一個緩沖區（返回一個多邊形）

geopandas.GeoSeries([belgium,brussels.buffer(1)]).plot(alpha=0.5,cmap='tab10') <matplotlib.axes._subplots.AxesSubplot at 0x7efc0e60bfd0>

現在取這兩個多邊形的交、并、差

brussels.buffer(1).intersection(belgium)

brussels.buffer(1).union(belgium)

brussels.buffer(1).difference(belgium)

另一個有用的屬性是 .unary_union ，它通過取所有這些矢量對象的并集，將GeoDataFrame中的一組矢量對象轉換為一個單一的矢量對象（即矢量數據的融合）

例如，我們可以為非洲大陸構造一個單一對象

africa_countries = countries[countries['continent'] == 'Africa'] africa=africa_countries.unary_union africa

print(str(africa)[:1000]) MULTIPOLYGON (((32.83012047702888 -26.7421916643362, 32.58026492689768 -27.47015756603182, 32.46213260267845 -28.30101124442056, 32.20338870619304 -28.75240488049007, 31.52100141777888 -29.25738697684626, 31.325561150851 -29.40197763439891, 30.90176272962535 -29.90995696382804, 30.62281334811382 -30.42377573010613, 30.05571618014278 -31.14026946383296, 28.92555260591954 -32.1720411109725, 28.2197558936771 -32.77195281344886, 27.46460818859597 -33.2269637997788, 26.41945234549283 -33.61495045342619, 25.90966434093349 -33.6670402971764, 25.7806282895007 -33.94464609144834, 25.17286176931597 -33.79685149509358, 24.67785322439212 -33.98717579522455, 23.59404340993464 -33.79447437920815, 22.98818891774474 -33.91643075941698, 22.57415734222224 -33.86408253350531, 21.54279910654103 -34.25883879978294, 20.689052768647 -34.41717538832523, 20.07126102059763 -34.79513681410799, 19.61640506356457 -34.81916635512371, 19.19327843595872 -34.46259897230979, 18.85531456876987 -34.44430551527847, 18.424

注意

GeoPandas（和Shapely的單個對象）提供了大量的基本方法來分析地理空間數據（距離、長度、中心點、邊界、凸殼、簡化、變換......），比我們在本教程中能接觸到的幾種方法多得多。

GeoPandas提供的所有方法的概述可以在這里找到： http://geopandas.readthedocs.io/en/latest/reference.html

</div>

3.5 空間操作練習

3.5.1 練習一：塞納河流經哪些區

下面，以類似于GeoJSON的特征字典（創建于 http://geojson.io）的方式提供了巴黎附近的塞納河的坐標

根據這個 seine 對象，我們想知道哪些地區離塞納河很近（最多150米）

在塞納河周圍建立一個150米的緩沖區
檢查哪些區域與該緩沖對象相交
將各區的情況可視化，說明哪些區位于塞納河附近

districts = geopandas.read_file("data/paris_districts.geojson").to_crs(epsg=2154) # 根據http://geojson.io的geojson格式創建一條曲線 s_seine = geopandas.GeoDataFrame.from_features({"type":"FeatureCollection","features":[{"type":"Feature","properties":{},"geometry":{"type":"LineString","coordinates":[[2.408924102783203,48.805619828930226],[2.4092674255371094,48.81703747481909],[2.3927879333496094,48.82325391133874],[2.360687255859375,48.84912860497674],[2.338714599609375,48.85827758964043],[2.318115234375,48.8641501307046],[2.298717498779297,48.863246707697],[2.2913360595703125,48.859519915404825],[2.2594070434570312,48.8311646245967],[2.2436141967773438,48.82325391133874],[2.236919403076172,48.82347994904826],[2.227306365966797,48.828339513221444],[2.2224998474121094,48.83862215329593],[2.2254180908203125,48.84856379804802],[2.2240447998046875,48.85409863123821],[2.230224609375,48.867989496547864],[2.260265350341797,48.89192242750887],[2.300262451171875,48.910203080780285]]}}]},crs={'init': 'epsg:4326'}) s_seine_utm = s_seine.to_crs(epsg=2154) /usr/local/lib/python3.6/dist-packages/pyproj/crs/crs.py:53: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6return _prepare_from_string(" ".join(pjargs)) import matplotlib.pyplot as plt fig, ax = plt.subplots(figsize=(16, 7)) districts.plot(ax=ax, color='grey', alpha=0.4, edgecolor='k') s_seine_utm.plot(ax=ax) <matplotlib.axes._subplots.AxesSubplot at 0x7efc0e114048>

seine=s_seine_utm.geometry.squeeze() seine_buffer=seine.buffer(150) seine_buffer

districts_seine = districts[districts.intersects(seine_buffer)] districts_seine.plot() <matplotlib.axes._subplots.AxesSubplot at 0x7efc06f1d748>

fig, ax = plt.subplots(figsize=(16, 7)) districts.plot(ax=ax,color='grey',alpha=0.4,edgecolor='k') districts_seine.plot(ax=ax,color='red',alpha=0.4,edgecolor='k') s_seine_utm.plot(ax=ax,color='blue') <matplotlib.axes._subplots.AxesSubplot at 0x7efc06ea8630>

總結

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