Python地理可视化：plotly绘制mapbox地图热力密度图

野性酷女 2022-12-27 14:16 95阅读 0赞

import plotly.graph_objects as go
    import numpy as np
    
    KEYS = ['中心点经纬度', '维度', '经度', '内容']
    
    
    def get_data():
        city_name = ['北京', '上海', '广州', '深圳', '成都']
    
        latlngs = [[39.929986, 116.395645],  # 北京中心点经纬度
                   [31.249162, 121.487899],  # 上海中心点经纬度
                   [23.120049, 113.30765],  # 广州中心点经纬度
                   [22.546054, 114.025974],  # 深圳中心点经纬度
                   [30.679943, 104.067923]]  # 成都中心点经纬度
    
        lats = []
        lons = []
        for ll in latlngs:
            lats.append(ll[0])
            lons.append(ll[1])
    
        ret = {KEYS[0]: latlngs[4],
               KEYS[1]: lats,
               KEYS[2]: lons,
               KEYS[3]: city_name}
    
        return ret
    
    
    if __name__ == '__main__':
        data = get_data()
    
        center_loc = data[KEYS[0]]
        lats = data[KEYS[1]]
        lons = data[KEYS[2]]
        texts = data[KEYS[3]]
    
        z_value = np.random.random(size=5)
        print(z_value)
        fig = go.Figure(go.Densitymapbox(lat=lats,
                                         lon=lons,
                                         z=z_value,
                                         radius=25, ))
    
        fig.update_layout(mapbox_style="stamen-terrain",  # open-street-map
                          mapbox_center_lon=center_loc[1],
                          mapbox_center_lat=center_loc[0],
                          mapbox_zoom=3)
        fig.update_layout(margin={'l': 50, 't': 50, 'b': 50, 'r': 50})
    
        fig.show()

输出结果：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3poYW5ncGhpbA_size_16_color_FFFFFF_t_70][]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3poYW5ncGhpbA_size_16_color_FFFFFF_t_70]: /images/20221120/0c5d6625c1194c7099da937587062d23.png