0 效果图

点开某一个区域后，内容是这个区域的用地类型分布

1 读取数据

import folium
import matplotlib.pyplot as plt
import re
import geopandas as gpd

subzone=gpd.read_file('MasterPlan2019PlanningAreaBoundaryNoSea.geojson')
subzone

2 提取subzone 信息

对description列进行正则表达式操作，提取subzone信息

def extract_name_from_description(description):
    match = re.search(r"<th>PLN_AREA_N</th> <td>(.*?)</td>", description)
    #使用正则表达式查找subzone的名字
    #查找的是' <th>PLN_AREA_N</th> <td>BUKIT BATOK</td>'这一部分内容
    #BUKIT BATOK 是第一个group
    return match.group(1) if match else None
    #返回对应的subzone的名字


subzone['subzone_name']=subzone['Description'].apply(extract_name_from_description)
subzone=subzone[['subzone_name','geometry']]
subzone

3 找到地图的中心点，并绘制subzone图

center_sg=subzone.geometry.unary_union.centroid
print(center_sg)
#POINT (103.82073869660411 1.3524404921177333)

m = folium.Map(location=(list(center_sg.coords)[0][1],list(center_sg.coords)[0][0]), zoom_start=11)
#绘制地图

folium.GeoJson(subzone,
            style_function=lambda x: {"fillColor": "lightgrey", "color": "blue", "weight": 0.5},).add_to(m)
m
#将subzone数据作为GeoJson添加到map中

4 制造伪数据，四个区域的用地类型分布

data={'JURONG WEST':{'industry':12.1,
                     'residential':20.4,
                    'comercial':1.5,
                    'open space':36.3,
                    'others':100-12.1-20.4-1.5-36.3},
     'ORCHARD':{'industry':0,
                     'residential':49.1,
                    'comercial':20.2,
                    'open space':20.9,
                    'others':100-0-49.1-20.2-20.9},
     'BISHAN':{'industry':0,
                     'residential':48.2,
                    'comercial':3.1,
                    'open space':22.4,
                    'others':100-0-48.2-3.1-22.4},
     'TUAS':{'industry':62.7,
                     'residential':0,
                    'comercial':0,
                    'open space':19.8,
                    'others':100-62.7-0-0-19.8}}

5 绘制饼图

def pie_chart_popup(area_name, data):
    plt.figure(figsize=(5, 4))
    plt.pie([data[area_name]['industry'],data[area_name]['residential'],data[area_name]['comercial'],data[area_name]['open space'],data[area_name]['others']],
           labels=['industry', 'residential','comercial','open space','others'],
           autopct='%1.1f%%')
    plt.title('Land use of '+f"{area_name}")
    #绘制饼图
    
    plt.savefig(f'{area_name}'+'.png')
    return folium.Popup(f'<img src="{area_name}.png">', max_width=265)
    #Popup就是点这块区域，就会弹出后面的这个html，也就是我们所画的饼图

6 将饼图和folium图结合

for subzone_name in data.keys():
    row=subzone[subzone['subzone_name']==subzone_name]
    #找到subzone对应的行

    
    folium.GeoJson(
            row,
            style_function=lambda x: {"fillColor": "green", "color": "#000000", "weight": 0.5},
            tooltip=subzone_name,  
            popup=pie_chart_popup(subzone_name, data)
        ).add_to(m)
    #在地图中画出这个subzone
    #tooltip就是鼠标滑过这片区域，就会显示的字
    #popup就是点击后弹出对应的饼图

m.save('subzone_proportion.html')
#保存html