zh-CN First-Level Administration Centers

Import easyclimate-map for loading China first-level administration centers, matplotlib.pyplot for plotting, and cartopy.crs for map projections. These libraries together support the retrieval and visualization of geographic data.

import easyclimate_map as eclmap
import matplotlib.pyplot as plt
import cartopy.crs as ccrs

<easyclimate-map notice>: Maps are provided as-is. Users assume all risk. No
liability. No political or territorial claims.

Points

Use easyclimate_map.get_zh_CN_1st_administration() to retrieve the point-type GeoDataFrame of China’s first-level administration centers. This data includes provincial-level government seats and can be used to mark capital locations.

zh_provinces_line = eclmap.get_zh_CN_provinces(type = "line")
zh_admin1_points = eclmap.get_zh_CN_1st_administration()
zh_admin1_points

	AREA	PERIMETER	RES1_4M_	RES1_4M_ID	GBCODE	NAME	ADCODE93	ADCODE99	ADCLASS	PINYIN	geometry
0	0.0	0.0	1	61	31010	北京	110100	110100	1	Beijing	POINT (116.38094 39.92361)
1	0.0	0.0	2	70	31020	天津	120100	120100	2	Tianjin	POINT (117.2035 39.13112)
2	0.0	0.0	3	121	31020	石家庄	130101	130101	2	Shijiazhuang	POINT (114.48978 38.04513)
3	0.0	0.0	4	220	31020	太原	140101	140101	2	Taiyuan	POINT (112.56935 37.87111)
4	0.0	0.0	5	322	31020	呼和浩特	150101	150101	2	Huhehaote	POINT (111.6633 40.82094)
5	0.0	0.0	6	412	31020	沈阳	210101	210101	2	Shenyang	POINT (123.41168 41.79662)
6	0.0	0.0	7	465	31020	长春	220101	220101	2	Changchun	POINT (125.31543 43.89256)
7	0.0	0.0	8	508	31020	哈尔滨	230101	230101	2	Haerbin	POINT (126.64334 45.74149)
8	0.0	0.0	9	584	31020	上海	310100	310100	2	Shanghai	POINT (121.46927 31.23818)
9	0.0	0.0	10	594	31020	南京	320101	320101	2	Nanjing	POINT (118.77278 32.04762)
10	0.0	0.0	11	668	31020	杭州	330101	330101	2	Hangzhou	POINT (120.15925 30.266)
11	0.0	0.0	12	741	31020	合肥	340101	340101	2	Hefei	POINT (117.2757 31.86325)
12	0.0	0.0	13	823	31020	福州	350101	350101	2	Fuzhou	POINT (119.29781 26.07859)
13	0.0	0.0	14	898	31020	南昌	360101	360101	2	Nanchang	POINT (115.89992 28.67599)
14	0.0	0.0	15	981	31020	济南	370101	370101	2	Jinan	POINT (117.0056 36.66707)
15	0.0	0.0	16	1084	31020	郑州	410101	410101	2	Zhengzhou	POINT (113.65005 34.75703)
16	0.0	0.0	17	1207	31020	武汉	420101	420101	2	Wuhan	POINT (114.29194 30.56751)
17	0.0	0.0	18	1344	31020	长沙	430101	430101	2	Changsha	POINT (112.98127 28.20082)
18	0.0	0.0	19	1403	31020	广州	440101	440101	2	Guangzhou	POINT (113.26143 23.11891)
19	0.0	0.0	20	1509	31020	南宁	450101	450101	2	Nanning	POINT (108.31177 22.80654)
20	0.0	0.0	21	1580	31020	海口	460100	460100	2	Haikou	POINT (110.34651 20.03179)
21	0.0	0.0	22	1620	31020	成都	510101	510101	2	Chengdu	POINT (104.08176 30.66106)
22	0.0	0.0	23	1622	31030	重庆	510201	500100	2	Chongqing	POINT (106.51034 29.55818)
23	0.0	0.0	24	1838	31020	贵阳	520101	520101	2	Guiyang	POINT (106.71137 26.57687)
24	0.0	0.0	25	1852	31020	昆明	530101	530101	2	Kunming	POINT (102.70457 25.04384)
25	0.0	0.0	26	1972	31020	拉萨	540101	540101	2	Lhasa	POINT (91.13205 29.65759)
26	0.0	0.0	27	2041	31020	西安	610101	610101	2	Xi'an	POINT (108.94903 34.26168)
27	0.0	0.0	28	2134	31020	兰州	620101	620101	2	Lanzhou	POINT (103.75005 36.06804)
28	0.0	0.0	29	2213	31020	西宁	630100	630100	2	Xining	POINT (101.78745 36.60945)
29	0.0	0.0	30	2253	31020	银川	640101	640101	2	Yinchuan	POINT (106.27194 38.46801)
30	0.0	0.0	31	2274	31020	乌鲁木齐	650101	650101	2	Wulumuqi	POINT (87.60612 43.79094)
31	0.0	0.0	32	2358	31020	台北	710001	710001	2	Taipei Shih	POINT (121.51428 25.04913)
32	0.0	0.0	33	1384	31030	澳门	820000	820000	9	Macao	POINT (113.55006 22.2008)
33	0.0	0.0	34	2377	31030	香港	810000	810000	9	Hong Kong	POINT (114.1544 22.28069)

Use GeoPandas’ plot() method for quick visualization of the point locations. This step is for initial data inspection without custom projections.

zh_admin1_points.plot()

<Axes: >

Create a subplot with PlateCarree projection (central longitude 180°), set geographic extent [70-140°E, 0-50°N]. Add gridlines, coastlines, China’s national boundary line geometries (red lines, no fill), and administration center points (blue markers). This step demonstrates point overlays for administrative centers on top of national boundaries. Parameter Details:

• set_extent: Defines the map display range.

• gridlines: Adds latitude/longitude grid with labels.

• coastlines: Draws global coastlines (50m resolution).

• add_geometries: Overlays national boundaries with red edges, line width 0.3.

• scatter: Plots administration centers with blue markers.

fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree(central_longitude=180)})

ax.set_extent([70, 140, 0, 50])
ax.gridlines(
    draw_labels=["left", "bottom"],
    color="grey",
    alpha=0.5, linestyle="--"
)
ax.coastlines(color="k", lw = 0.5, resolution = "50m")
ax.add_geometries(
    zh_provinces_line.geometry,
    crs = ccrs.PlateCarree(),
    facecolor = "none",
    edgecolor = "r",
    lw = 0.3
)
ax.scatter(
    zh_admin1_points.geometry.x,
    zh_admin1_points.geometry.y,
    s = 12,
    color = "b",
    transform = ccrs.PlateCarree()
)

<matplotlib.collections.PathCollection object at 0x7f299d69e1b0>