진행하고 있던 프로젝트에서 지도 정보가 필요하여 찾아봤던 자료들 입니다.
무료로 좌표계를 받을수 있는 곳을 찾기가 힘들어 고생했던 기억이 있어서 그당시 찾아봤던 site들을 정리해봤습니다.
결국 위도 경도 정보를 data로 구해야 해서 저는 python panda로 data를 구했었습니다.
https://www.esrikr.com/arcgis-guide/arcgis-pro-master-2/
[ArcGIS Pro 완전 정복!] ②기본 | 한국에스리
☞ 2차원 맵과 3차원 씬 연결 [보기(View)] → [뷰 연결(Link Views)] → [중심 및 축척(Center and Scale)] 클릭 데이터 탭을 클릭하여 드래그 → 뷰에서 십자형 사각형 중 원하는 모양에 배치하면 2차원 맵과
www.esrikr.com
지도 정보 찾기
Earthdata
The Earth Observing System Data and Information System is a key core capability in NASA’s Earth Science Data Systems Program. It provides end-to-end capabilities for managing NASA’s Earth science data from various sources—satellites, aircraft, field
earthdata.nasa.gov
Global Map data archives
Global Map data archives
Use of geospatial information is crucial for solving various issues around the world including global environmental problems. In order to solve these diversified issues and to advance sustainable development, it is important to make fundamental geospatial
globalmaps.github.io
https://globalmaps.github.io/glcnmo.html#code
Land Cover (GLCNMO) - Global version - Global Map
The Global Land Cover by National Mapping Organizations (GLCNMO) is geospatial information in raster format which classifies the status of land cover of the whole globe into 20 categories. The classification is based on LCCS developed by FAO. Therefore, it
globalmaps.github.io
http://www.diva-gis.org/datadown
Spatial Data Download | DIVA-GIS
www.diva-gis.org
https://datascienceschool.net/view-notebook/ef921dc25e01437b9b5c532ba3b89b02/
Data Science School
Data Science School is an open space!
datascienceschool.net
pandas
import geopandas as gpd
import matplotlib.pyplot as plt
import matplotlib.path as mpath
import matplotlib.patches as mpatches
gpd.__version__
def get_pos(PosData) :
return PosData[0],PosData[1]
print(gpd.__version__)
countries = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
cities = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))
#countries.tail(3)
print(countries.name)
china = countries[countries.name == "China"].geometry
china.boundary.squeeze()
china.plot()
rect = china.total_bounds
fig = plt.figure()
ax =fig.add_axes(rect,)
fig.delaxes(ax)
fig.add_axes(ax)
# sub plot
# ax = plt.subplots()
# pos = [[china.total_bounds[0],china.total_bounds[1]],
# [china.total_bounds[2],china.total_bounds[1]],
# [china.total_bounds[2],china.total_bounds[3]],
# [china.total_bounds[0],china.total_bounds[3]]]
# Path = mpath.Path
# path_data = [
# (Path.MOVETO, (pos[0])),
# (Path.LINETO, (pos[1])),
# (Path.LINETO, (pos[2])),
# (Path.LINETO, (pos[3])),
# (Path.CLOSEPOLY, (pos[0])),
# ]
# ax.plot(path_data)
# #
# codes, verts = zip(*path_data)
# path = mpath.Path(verts, codes)
# patch = mpatches.PathPatch(path, facecolor='r', alpha=0.5)
# ax.add_patch(patch)
# x, y = zip(*path.vertices)
# line, = ax.plot(x, y, 'go-')
# ax.grid()
# ax.axis('equal')
plt.figimage(fig)
plt.show()
#china.
with open("china_map.txt", mode='wt') as f:
f.write(china)
print(china)
#ax.set_title("세계 지도")
#ax.set_axis_off()
#print(countries)
i=0
while 1 :
i+=i
결과
result:
"{\"type\": \"FeatureCollection\", \"features\": [{\"id\": \"139\", \"type\": \"Feature\", \"properties\": {}, \"geometry\": {\"type\": \"MultiPolygon\", \"coordinates\": [[[[109.47520958866365, 18.197700913968575], [108.65520796105616, 18.507681993071387], [108.62621748254044, 19.367887885001906], [109.11905561730804, 19.821038519769345], [110.21159874882281, 20.101253973872033], [110.78655073450221, 20.077534491450052], [111.01005130416458, 19.69592987719072], [110.57064660038677, 19.25587921800927], [110.33918786015147, 18.678395087147592], [109.47520958866365, 18.197700913968575]]], [[[80.2599902688853, 42.34999929459906], [80.1801501809943, 42.92006785742694], [80.86620649610126, 43.18036204688101], [79.96610639844141, 44.91751699480463], [81.9470707539181, 45.31702749285312], [82.45892581576906, 45.539649563166506], [83.18048383986047, 47.33003123635086], [85.16429039911324, 47.0009557155161], [85.72048383987067, 47.452969468773105], [85.7682328633083, 48.45575063739699], [86.59877648310336, 48.549181626980626], [87.35997033076265, 49.21498078062912], [87.75126427607671, 49.297197984405486], [88.01383222855173, 48.599462795600616], [88.85429772334676, 48.069081732772965], [90.28082563676392, 47.69354909930793], ...
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