In this example, we will read a shapefile as a Spark DataFrame. First, download a shapefile and upload into your file browser in the EarthAI Notebook app. In this example, we will work with the Natural Earth tiny countries data .
If the shapefile is zipped, unzip it. You can do this in a notebook cell with ! unzip ne_110m_admin_0_tiny_countries.zip.
In [1]:
from earthai.init import *
import requests
import zipfile
import os
# Download a shapefile, upload into your directory
shapefile_url = 'https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/110m/cultural/ne_110m_admin_0_tiny_countries.zip'
shapefile_path = 'ne_110m_admin_0_tiny_countries.zip'
# shapefile_path = os.path.join(
# os.path.abspath(os.curdir),
# shapefile_path)
shapefile_path = os.path.abspath(shapefile_path)
# download the shapefile to the shapefile path
with requests.get(shapefile_url) as r:
with open(shapefile_path, 'wb') as f:
f.write(r.content)
# unzip
with zipfile.ZipFile(shapefile_path, 'r') as zip_ref:
zip_ref.extractall(os.curdir)
In the call to read.shapefile, pass the full path to either the .shp or .zip file.
In [2]:
df = spark.read.shapefile(os.path.abspath('ne_110m_admin_0_tiny_countries.zip'))
df.select('__fid__', 'geometry', 'name')
Out[2]:
We can work with this DataFrame as shown in the RasterFrames documentation and in querying raster data with vectors.
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