\u6b63\u8ddd\u5186\u7b52\u56f3\u6cd5 plate carr\u00e9e projection<\/strong>
\nimport cartopy.crs as ccrs
\nimport matplotlib.pyplot as plt
\nimport numpy as np<\/p>\nfig=plt.figure(figsize=(10,10))
\nax = plt.axes(projection=ccrs.PlateCarree(central_longitude=150))<\/p>\n
import cartopy.feature as cfea
\nax.add_feature(cfea.LAND,color=’#dcdcdc’)
\nax.add_feature(cfea.OCEAN,color=’white’)
\nax.set_global()
\nax.coastlines()<\/p>\n
ax.gridlines(
\ndraw_labels=True, # \u8ef8\u30e9\u30d9\u30eb\u306e\u8a2d\u5b9a
\nxlocs=np.arange(-180, 180, 30), # x\u8ef8\u306e\u76ee\u76db\u308a\u306e\u8a2d\u5b9a
\nylocs=np.arange(-90, 90, 10), # y\u8ef8\u306e\u76ee\u76db\u308a\u306e\u8a2d\u5b9a
\ndms=True, # \u5ea6\u5206\u79d2\u8868\u793a\u306e\u8a2d\u5b9a(True:\u5ea6\u5206\u79d2\u8868\u793a, False:10\u9032\u8868\u793a)
\n)<\/p>\n
ax.plot(139.6,35.6,”ro”,markersize=’6′,transform=ccrs.PlateCarree()) # Tokyo\u5317\u7def35.6894\u5ea6 \u6771\u7d4c139.6917\u5ea6
\nax.plot(-122.2, 37.7,”bo”,markersize=’6′,transform=ccrs.PlateCarree()) #\u30b5\u30f3\u30d5\u30e9\u30f3\u30b7\u30b9\u30b3\u6e7e\u5317\u7def37.70833\u5ea6 \u897f\u7d4c122.28028\u5ea6<\/p>\n
plt.savefig(“map1.png”) # PNG\u30d5\u30a1\u30a4\u30eb\u51fa\u529b
\nplt.show()<\/p>\n
\u6b63\u8ddd\u65b9\u4f4d\u56f3\u6cd5\uff08azimuthal equidistant projection\uff09<\/strong>
\nimport cartopy.crs as ccrs
\nimport matplotlib.pyplot as plt
\nimport numpy as np<\/p>\nfig=plt.figure(figsize=(10,10))
\nax=plt.axes(projection=ccrs.AzimuthalEquidistant(
\ncentral_longitude=139.60, central_latitude=35.50))
\nax.coastlines()<\/p>\n
import matplotlib.ticker as mticker
\ngl=ax.gridlines(crs=ccrs.PlateCarree())
\ngl.xlocator = mticker.FixedLocator(np.arange(-180, 180.1, 20))
\ngl.ylocator = mticker.FixedLocator(np.arange(-90, 90, 10))<\/p>\n
import cartopy.feature as cfea
\nax.add_feature(cfea.LAND,color=’#dcdcdc’)
\nax.add_feature(cfea.OCEAN,color=’white’)
\nax.set_global()<\/p>\n
ax.plot(139.6,35.6,”ro”,markersize=’6′,transform=ccrs.PlateCarree()) # Tokyo\u5317\u7def35.6894\u5ea6 \u6771\u7d4c139.6917\u5ea6
\nax.plot(-122.2, 37.7,”bo”,markersize=’6′,transform=ccrs.PlateCarree()) #\u30b5\u30f3\u30d5\u30e9\u30f3\u30b7\u30b9\u30b3\u6e7e\u5317\u7def37.70833\u5ea6 \u897f\u7d4c122.28028\u5ea6<\/p>\n
plt.savefig(“map2.png”) # PNG\u30d5\u30a1\u30a4\u30eb\u51fa\u529b
\nplt.show()<\/p>\n
\u30e1\u30eb\u30ab\u30c8\u30eb\u56f3\u6cd5\uff08Mercator Projection\uff09<\/strong>
\nimport cartopy.crs as ccrs
\nimport matplotlib.pyplot as plt
\nimport numpy as np<\/p>\nfig=plt.figure(figsize=(10,10))<\/p>\n
ax = plt.axes(projection=ccrs.Mercator(central_longitude=150))<\/p>\n
import cartopy.feature as cfea
\nax.add_feature(cfea.LAND,color=’#dcdcdc’)
\nax.add_feature(cfea.OCEAN,color=’white’)
\nax.set_global()
\nax.coastlines()<\/p>\n
ax.gridlines(
\n\u25a1<\/span>draw_labels=True, # \u8ef8\u30e9\u30d9\u30eb\u306e\u8a2d\u5b9a
\n\u25a1<\/span>xlocs=np.arange(-180, 180, 30), # x\u8ef8\u306e\u76ee\u76db\u308a\u306e\u8a2d\u5b9a
\n\u25a1<\/span>ylocs=np.arange(-90, 90, 10), # y\u8ef8\u306e\u76ee\u76db\u308a\u306e\u8a2d\u5b9a
\n\u25a1<\/span>dms=True, # \u5ea6\u5206\u79d2\u8868\u793a\u306e\u8a2d\u5b9a(True:\u5ea6\u5206\u79d2\u8868\u793a, False:10\u9032\u8868\u793a)
\n)<\/p>\nax.plot(139.6,35.6,”ro”,markersize=’6′,transform=ccrs.PlateCarree()) # Tokyo\u5317\u7def35.6894\u5ea6 \u6771\u7d4c139.6917\u5ea6
\nax.plot(-122.2, 37.7,”bo”,markersize=’6′,transform=ccrs.PlateCarree()) #\u30b5\u30f3\u30d5\u30e9\u30f3\u30b7\u30b9\u30b3\u6e7e\u5317\u7def37.70833\u5ea6 \u897f\u7d4c122.28028\u5ea6<\/p>\n
plt.savefig(“map3.png”) # PNG\u30d5\u30a1\u30a4\u30eb\u51fa\u529b
\nplt.show()<\/p>\n
<\/span>\u6b21\u56de\u306f\u3001\u30e1\u30eb\u30ab\u30c8\u30eb\u56f3\u6cd5\u306e\u6570\u5b66<\/span><\/h2>\n\u56f3\u6cd5\u306f\u6570\u5b66\u306b\u3088\u3063\u3066\u652f\u3048\u3089\u308c\u3066\u3044\u307e\u3059\u3002\u5192\u982d\u3067\u7d39\u4ecb\u3057\u305f\u3001\u30e9\u30f3\u30d9\u30eb\u30c8\u6b63\u7a4d\u5186\u7b52\u56f3\u6cd5\u3001\u30ac\u30a6\u30b9\u30fb\u30af\u30ea\u30e5\u30fc\u30b2\u30eb\u56f3\u6cd5\u3068\u3044\u3063\u305f\u540d\u79f0\u306b\u6570\u5b66\u8005\u306e\u540d\u524d\u304c\u4ed8\u3051\u3089\u308c\u3066\u3044\u308b\u3053\u3068\u304b\u3089\u3082\u3001\u305d\u308c\u304c\u308f\u304b\u308a\u307e\u3059\u3002<\/p>\n
\u300c\u6b63\u78ba\u306b\u306f\u6b63\u8ddd\u5186\u7b52\u56f3\u6cd5\u3092\u7def\u5ea6\u3054\u3068\u306b\u3042\u308b\u5272\u5408\u3067\u62e1\u5927\u3057\u305f\u306e\u304c\u30e1\u30eb\u30ab\u30c8\u30eb\u56f3\u6cd5\uff08Mercator Projection\uff09\u300d<\/p>\n
\u6b21\u56de\u306f\u3053\u306e\u3053\u3068\u3092\u8a08\u7b97\u3057\u306a\u304c\u3089\u3001\u30e1\u30eb\u30ab\u30c8\u30eb\u56f3\u6cd5\u306e\u6570\u5b66\u3092\u307f\u3066\u3044\u304d\u307e\u3059\u3002<\/p>\n
\u30a4\u30f3\u30d5\u30a9\u30de\u30c6\u30a3\u30af\u30b9\u304b\u3089\u306e\u304a\u77e5\u3089\u305b<\/span><\/p>
![\"\"](\"https:\/\/club.informatix.co.jp\/wp-content\/uploads\/2026\/02\/20260203-1.jpg\")