Python制图 | 02. 矢量数据绘制

Section Two - Plotting Spatial Data

在本节中,将学习如何使用Python创建和个性化绘制空间数据(包括 矢量 栅格 数据)的图形

源代码

学习目标:

  • 创建一个包含多个矢量数据集的地图,并根据属性进行着色
  • 为矢量数据地图添加自定义图例
  • 设置地图的显示范围
  • 使用 folium 实现交互式地图
  • 实现栅格数据的叠加显示
  • 实现栅格地图加载到 folium 中实现交互
  • 使用Matplotlib个性化显示栅格数据

1 矢量数据绘制

学习目标:

  • 为多个矢量数据集绘制地图,并根据属性进行配色
  • 自定义地图图例

创建自定义地图

在本节中,将学习如何自定义地图符号和用于在Python中表示矢量数据的颜色和符号,使用geopandas和matplotlib进行地图绘制

首先导入需要使用到的包:

import os
import matplotlib.pyplot as plt
import numpy as np
from shapely.geometry import box
import geopandas as gpd
import earthpy as et
# 下载数据
# data = et.data.get_data('spatial-vector-lidar')
os.chdir(os.path.join(et.io.HOME, 'learning','python_data_plot'))
# 导入数据
sjer_roads_path="data/california/madera-county-roads/tl_2013_06039_roads.shp"
sjer_roads = gpd.read_file(sjer_roads_path)
print(type(sjer_roads['RTTYP']))
print(sjer_roads['RTTYP'].unique())

['M' None 'S' 'C']

缺失值替换

可以看出道路类型中有一些缺失的值,由于需要绘制所有的道路类型,甚至那些设置为 None 的道路类型,下面将 RTTYP 属性 None Unknown

sjer_roads['RTTYP'].replace(np.nan,"Unknown",inplace=True)
# sjer_roads.loc[sjer_roads['RTTYP'].isnull(), 'RTTYP'] = 'Unknown'
print(sjer_roads['RTTYP'].unique())
['M' 'Unknown' 'S' 'C']

如果使用 geopandas.Plot() 绘制数据,当设置了 column = 参数后,则geopandas将为线条 自动选择颜色 ,可以使用 legend = True 参数添加图例

fig, ax = plt.subplots(figsize=(14,6))

sjer_roads.plot(column='RTTYP',
                categorical=True,
                legend=True,
                ax=ax
               )

# 调整图例位置
leg = ax.get_legend()
leg.set_bbox_to_anchor((1.15,0.5))

# 隐藏边框
ax.set_axis_off()

plt.show()

Python制图 | 02. 矢量数据绘制_第1张图片

根据属性指定颜色

为了按属性值绘制一个矢量图层,这样每条道路图层就会根据它各自的属性值来着色,所以图例也代表了同样的符号,需要三个步骤:

  1. 创建一个将特定颜色与特定属性值关联的字典
  2. 循环遍历并将该颜色应用于每个属性值
  3. 最后,在绘图中添加一个 label 参数,以便调用 ax.legend() 生成最终的图例

下面,先创建一个字典来定义您希望使用哪种颜色绘制每种道路类型:

# Create a dictionary where you assign each attribute value to a particular color
roadPalette = {'M': 'blue',
               'S': 'green',
               'C': 'purple',
               'Unknown': 'grey'}
roadPalette
{'M': 'blue', 'S': 'green', 'C': 'purple', 'Unknown': 'grey'}

接下来,循环遍历每个属性值,并使用字典中指定的颜色用该属性值绘制线条

fig, ax = plt.subplots(figsize=(10,10))

# 根据道路类型分组进行绘制
for ctype,data in sjer_roads.groupby('RTTYP'):
    
    color = roadPalette[ctype]
    
    data.plot(color=color,
              ax=ax,
              label=ctype
             )

ax.legend(bbox_to_anchor=(1.0, .5), prop={'size': 12})
ax.set(title='Madera County Roads')

ax.set_axis_off()
plt.show()    

Python制图 | 02. 矢量数据绘制_第2张图片

调整线条宽度

可以通过 linewidth = 属性对线条宽度进行设置,

fig, ax = plt.subplots(figsize=(10, 10))

# Loop through each group (unique attribute value) in the roads layer and assign it a color
for ctype, data in sjer_roads.groupby('RTTYP'):
    color = roadPalette[ctype]
    data.plot(color=color,
              ax=ax,
              label=ctype,
              linewidth=4)  # Make all lines thicker

# Add title and legend to plot
ax.legend()
ax.set(title='Madera County Roads')
ax.set_axis_off()

plt.show()

Python制图 | 02. 矢量数据绘制_第3张图片

根据属性调整线条宽度

与着色相同,先创建线条宽度与类型的映射关系,然后分组进行循环绘制

# Create dictionary to map each attribute value to a line width
lineWidths = {'M': 1, 'S': 1, 'C': 4, 'Unknown': .5}

# Plot data adjusting the linewidth attribute
fig, ax = plt.subplots(figsize=(10, 10))
ax.set_axis_off()

for ctype, data in sjer_roads.groupby('RTTYP'):
    color = roadPalette[ctype]    
    data.plot(color=color,
              ax=ax,
              label=ctype,
              
              # Assign each group to a line width using the dictionary created above
              linewidth=lineWidths[ctype])

ax.legend()
ax.set(title='Madera County \n Line width varies by TYPE Attribute Value')
plt.show()

自定义绘制图例

在上面的实验中,使用 label=True 显示图例, ax.legend() loc= 参数可以对图例位置进行调整, ax.legend() 的常用参数有:

  • loc=(how-far-right,how-far-above)
  • fontsize= ,设置图例字体大小
  • frameon= ,是否显示图例边框
lineWidths = {'M': 1, 'S': 2, 'C': 1.5, 'Unknown': 3}

fig, ax = plt.subplots(figsize=(10, 10))

# Loop through each attribute value and assign each 
# with the correct color & width specified in the dictionary
for ctype, data in sjer_roads.groupby('RTTYP'):
    color = roadPalette[ctype]
    label = ctype    
    data.plot(color=color,
              ax=ax,
              linewidth=lineWidths[ctype],
              label=label)


ax.set(title='Madera County \n Line width varies by TYPE Attribute Value')

# Place legend in the lower right hand corner of the plot
ax.legend(loc='lower right',
          fontsize=15,
          frameon=True)

ax.set_axis_off()
plt.show()

观察当将图例 frameon 属性设置为 False 并调整线宽时会发生什么情况,注意 loc = () 参数被赋予一个元组,它定义了图例相对于绘图区域的 x y 的位置

lineWidths = {'M': 1, 'S': 2, 'C': 1.5, 'Unknown': 3}

fig, ax = plt.subplots(figsize=(10, 10))

for ctype, data in sjer_roads.groupby('RTTYP'):
    color = roadPalette[ctype]
    label = ctype
    data.plot(color=color,
              ax=ax,
              linewidth=lineWidths[ctype],
              label=label)

ax.set(title='Madera County \n Line width varies by TYPE Attribute Value')
ax.legend(loc=(1, 0.5),
          fontsize=15,
          frameon=False,
          title="LEGEND")

ax.set_axis_off()
plt.show()

同时对线宽和颜色进行调整

roadPalette = {'M': 'grey', 'S': "blue",
               'C': "magenta", 'Unknown': "lightgrey"}

lineWidths = {'M': 1, 'S': 2, 'C': 1.5, 'Unknown': 3}

fig, ax = plt.subplots(figsize=(10, 10))

for ctype, data in sjer_roads.groupby('RTTYP'):
    color = roadPalette[ctype]
    label = ctype
    
    data.plot(color=color,
              ax=ax,
              linewidth=lineWidths[ctype],
              label=label)
    
ax.set(title='Madera County Roads \n Pretty Colors')

ax.legend(loc='lower right',
          fontsize=20,
          frameon=False)

ax.set_axis_off()

plt.show()

向地图中添加点图层

接下来,向地图添加另一个图层,看看如何创建一个更复杂的地图,添加SJER_plot_centroids shapefile,并同时表示两个图层的图例

该点图层包含三种类型:grass,soil,trees

# 导入点图层
sjer_plots_path ="data/california/neon-sjer-site/vector_data/SJER_plot_centroids.shp"
sjer_plots = gpd.read_file(sjer_plots_path)
sjer_plots.head(5)

Plot_ID Point northing easting plot_type geometry
0 SJER1068 center 4111567.818 255852.376 trees POINT (255852.376 4111567.818)
1 SJER112 center 4111298.971 257406.967 trees POINT (257406.967 4111298.971)
2 SJER116 center 4110819.876 256838.760 grass POINT (256838.760 4110819.876)
3 SJER117 center 4108752.026 256176.947 trees POINT (256176.947 4108752.026)
4 SJER120 center 4110476.079 255968.372 grass POINT (255968.372 4110476.079)

就像上面所做的一样,创建一个字典来指定与每个图形类型相关联的颜色

pointsPalette = {'trees': 'chartreuse',
                 'grass': 'darkgreen', 'soil': 'burlywood'}

lineWidths = {'M': .5, 'S': 2, 'C': 2, 'Unknown': .5}

fig, ax = plt.subplots(figsize=(10, 10))

for ctype, data in sjer_plots.groupby('plot_type'):
    color = pointsPalette[ctype]
    label = ctype
    data.plot(color=color,
              ax=ax,
              label=label,
              markersize=100)
    
ax.set(title='Study area plot locations\n by plot type (grass, soil and trees)')

ax.legend(fontsize=20,
          frameon=True,
          loc=(1, .1),
          title="LEGEND")

ax.set_axis_off()
plt.show()

将点图层叠加到道路图层

接下来,在道路图层上叠加绘制点数据,然后创建一个包含线和点的自定义图例

注意: 在这个例子中,两个图层的投影信息必须匹配

# Reproject the data
# 数据投影
sjer_roads_utm = sjer_roads.to_crs(sjer_plots.crs)
fig, ax = plt.subplots(figsize=(10, 10))

# 点图层绘制
for ctype, data in sjer_plots.groupby('plot_type'):
    color = pointsPalette[ctype]
    label = ctype # label参数对于图例的生成很重要
    data.plot(color=color,
              ax=ax,
              label=label,
              markersize=100)
# 道路图层绘制    
for ctype, data in sjer_roads_utm.groupby('RTTYP'):
    color = roadPalette[ctype]
    label = ctype    
    data.plot(color=color,
              ax=ax,
              linewidth=lineWidths[ctype],
              label=label)
    
ax.set(title='Study area plot locations\n by plot type (grass, soil and trees)')

ax.legend(fontsize=15,
          frameon=False,
          loc=('lower right'),
          title="LEGEND")

ax.set_axis_off()
plt.show()

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