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使用Folium绘制多边形#
此示例演示如何在Folium贴图上绘制多边形。
[1]:
import geopandas as gpd
import folium
import matplotlib.pyplot as plt
加载几何图形#
本例使用包含纽约区的多边形的NIBB数据集。
[2]:
path = gpd.datasets.get_path('nybb')
df = gpd.read_file(path)
df.head()
[2]:
| BoroCode | BoroName | Shape_Leng | Shape_Area | geometry | |
|---|---|---|---|---|---|
| 0 | 5 | Staten Island | 330470.010332 | 1.623820e+09 | MULTIPOLYGON (((970217.022 145643.332, 970227.... |
| 1 | 4 | Queens | 896344.047763 | 3.045213e+09 | MULTIPOLYGON (((1029606.077 156073.814, 102957... |
| 2 | 3 | Brooklyn | 741080.523166 | 1.937479e+09 | MULTIPOLYGON (((1021176.479 151374.797, 102100... |
| 3 | 1 | Manhattan | 359299.096471 | 6.364715e+08 | MULTIPOLYGON (((981219.056 188655.316, 980940.... |
| 4 | 2 | Bronx | 464392.991824 | 1.186925e+09 | MULTIPOLYGON (((1012821.806 229228.265, 101278... |
从原始数据集绘制
[3]:
df.plot(figsize=(6, 6))
plt.show()
请注意，多边形几何图形的值并不直接表示地理坐标系中的经纬度的值。要查看几何图形列的坐标参考系，请访问 crs 属性：
[4]:
df.crs
[4]:
<Derived Projected CRS: EPSG:2263>
Name: NAD83 / New York Long Island (ftUS)
Axis Info [cartesian]:
- X[east]: Easting (US survey foot)
- Y[north]: Northing (US survey foot)
Area of Use:
- name: United States (USA) - New York - counties of Bronx; Kings; Nassau; New York; Queens; Richmond; Suffolk.
- bounds: (-74.26, 40.47, -71.8, 41.3)
Coordinate Operation:
- name: SPCS83 New York Long Island zone (US Survey feet)
- method: Lambert Conic Conformal (2SP)
Datum: North American Datum 1983
- Ellipsoid: GRS 1980
- Prime Meridian: Greenwich
这个 epsg:2263 CRS是使用线性单位(在本例中为ft)的投影坐标参考系。由于Folium(即，Folium)默认接受纬度和经度(角度单位)的值作为输入，因此我们需要首先将几何体投影到地理坐标系。
[5]:
# Use WGS 84 (epsg:4326) as the geographic coordinate system
df = df.to_crs(epsg=4326)
print(df.crs)
df.head()
epsg:4326
[5]:
| BoroCode | BoroName | Shape_Leng | Shape_Area | geometry | |
|---|---|---|---|---|---|
| 0 | 5 | Staten Island | 330470.010332 | 1.623820e+09 | MULTIPOLYGON (((-74.05051 40.56642, -74.05047 ... |
| 1 | 4 | Queens | 896344.047763 | 3.045213e+09 | MULTIPOLYGON (((-73.83668 40.59495, -73.83678 ... |
| 2 | 3 | Brooklyn | 741080.523166 | 1.937479e+09 | MULTIPOLYGON (((-73.86706 40.58209, -73.86769 ... |
| 3 | 1 | Manhattan | 359299.096471 | 6.364715e+08 | MULTIPOLYGON (((-74.01093 40.68449, -74.01193 ... |
| 4 | 2 | Bronx | 464392.991824 | 1.186925e+09 | MULTIPOLYGON (((-73.89681 40.79581, -73.89694 ... |
[6]:
df.plot(figsize=(6, 6))
plt.show()
创建Folium贴图#
[7]:
m = folium.Map(location=[40.70, -73.94], zoom_start=10, tiles='CartoDB positron')
m
[7]:
Make this Notebook Trusted to load map: File -> Trust Notebook
将面添加到地图#
在地图上使用弹出的行政区名称覆盖行政区的边界：
[8]:
for _, r in df.iterrows():
# Without simplifying the representation of each borough,
# the map might not be displayed
sim_geo = gpd.GeoSeries(r['geometry']).simplify(tolerance=0.001)
geo_j = sim_geo.to_json()
geo_j = folium.GeoJson(data=geo_j,
style_function=lambda x: {'fillColor': 'orange'})
folium.Popup(r['BoroName']).add_to(geo_j)
geo_j.add_to(m)
m
[8]:
Make this Notebook Trusted to load map: File -> Trust Notebook
添加质心标记#
为了正确计算几何图形的几何属性，在本例中为质心，我们需要将数据投影到投影坐标系。
[9]:
# Project to NAD83 projected crs
df = df.to_crs(epsg=2263)
# Access the centroid attribute of each polygon
df['centroid'] = df.centroid
由于我们再次向Folium地图添加新的几何体，因此需要将该几何体投影回具有经纬度值的地理坐标系。
[10]:
# Project to WGS84 geographic crs
# geometry (active) column
df = df.to_crs(epsg=4326)
# Centroid column
df['centroid'] = df['centroid'].to_crs(epsg=4326)
df.head()
[10]:
| BoroCode | BoroName | Shape_Leng | Shape_Area | geometry | centroid | |
|---|---|---|---|---|---|---|
| 0 | 5 | Staten Island | 330470.010332 | 1.623820e+09 | MULTIPOLYGON (((-74.05051 40.56642, -74.05047 ... | POINT (-74.15340 40.58085) |
| 1 | 4 | Queens | 896344.047763 | 3.045213e+09 | MULTIPOLYGON (((-73.83668 40.59495, -73.83678 ... | POINT (-73.81847 40.70757) |
| 2 | 3 | Brooklyn | 741080.523166 | 1.937479e+09 | MULTIPOLYGON (((-73.86706 40.58209, -73.86769 ... | POINT (-73.94768 40.64472) |
| 3 | 1 | Manhattan | 359299.096471 | 6.364715e+08 | MULTIPOLYGON (((-74.01093 40.68449, -74.01193 ... | POINT (-73.96719 40.77725) |
| 4 | 2 | Bronx | 464392.991824 | 1.186925e+09 | MULTIPOLYGON (((-73.89681 40.79581, -73.89694 ... | POINT (-73.86653 40.85262) |
[11]:
for _, r in df.iterrows():
lat = r['centroid'].y
lon = r['centroid'].x
folium.Marker(location=[lat, lon],
popup='length: {} <br> area: {}'.format(r['Shape_Leng'], r['Shape_Area'])).add_to(m)
m
[11]:
Make this Notebook Trusted to load map: File -> Trust Notebook