Read and write spatial vectors
Project description
Read and write spatial vectors from shapefiles and CSVs thanks to GDAL and pandas.
Install
sudo dnf -y install gdal-python3 # sudo apt-get -y install python3-gdal virtualenv -p $(which python3) --system-site-packages \ ~/.virtualenvs/crosscompute source ~/.virtualenvs/crosscompute/bin/activate pip install geotable
Use
Load shapefiles.
In [1]: from geotable import GeoTable In [2]: t = GeoTable.load('shp.zip') In [3]: t.iloc[0] Out[3]: name b quantity 2 cost 0.66 date 1990-01-01 00:00:00 geometry_object POINT (-91.5305465 14.8520705) geometry_layer b geometry_proj4 +proj=longlat +datum=WGS84 +no_defs Name: 0, dtype: object
Load CSVs containing spatial information.
GeoTable.load('wkt.csv') # Load single CSV GeoTable.load('csv.zip') # Load archive of multiple CSVs GeoTable.load('csv.zip', parse_dates=['date']) # Configure pandas.read_csv
Handle CSVs with different geometry columns.
$ cat latitude_longitude.csv name,quantity,cost,date,latitude,longitude b,2,0.66,1990-01-01,14.8520705,-91.5305465 $ cat lat_lon.csv name,quantity,cost,date,lat,lon c,3,0.99,2000-01-01,42.2808256,-83.7430378 $ cat latitude_longitude_wkt.csv name,quantity,cost,date,latitude_longitude_wkt a,1,0.33,1980-01-01,POINT(42.3736158 -71.10973349999999) $ cat longitude_latitude_wkt.csv name,quantity,cost,date,longitude_latitude_wkt a,1,0.33,1980-01-01,POINT(-71.10973349999999 42.3736158) $ cat wkt.csv name,quantity,cost,date,wkt aaa,1,0.33,1980-01-01,"POINT(-71.10973349999999 42.3736158)" bbb,1,0.33,1980-01-01,"LINESTRING(-122.1374637 37.3796627,-92.5807231 37.1067189)" ccc,1,0.33,1980-01-01,"POLYGON ((-83.10973350093332 42.37361082304877, -103.5305394806998 14.85206885307358, -95.7430260175515 42.28082607112266, -83.10973350093332 42.37361082304877))"
Handle CSVs with different spatial references.
$ cat proj4_from_file.csv name,wkt aaa,"POLYGON((326299 4693415,-1980130 1771892,-716771 4787516,326299 4693415))" $ cat proj4_from_file.proj4 +proj=utm +zone=17 +ellps=WGS84 +datum=WGS84 +units=m +no_defs $ cat proj4_from_row.csv name,wkt,geometry_layer,geometry_proj4 aaa,"LINESTRING(-122.1374637 37.3796627,-92.5807231 37.1067189)",l1,+proj=longlat +datum=WGS84 +no_defs aaa,"POLYGON((326299 4693415,-1980130 1771892,-716771 4787516,326299 4693415))",l2,+proj=utm +zone=17 +ellps=WGS84 +datum=WGS84 +units=m +no_defs
Load and save in different spatial references.
from geotable.projections import SPHERICAL_MERCATOR_PROJ4 t = GeoTable.load('shp.zip', target_proj4=SPHERICAL_MERCATOR_PROJ4) from geotable.projections import LONGITUDE_LATITUDE_PROJ4 t.to_shp('/tmp/shp.zip', target_proj4=LONGITUDE_LATITUDE_PROJ4)
Use LONGITUDE_LATITUDE_PROJ4 for compatibility with algorithms that use geodesic distance such as those found in geopy and pysal. Geodesic distance is also known as arc distance and is the distance between two points as measured using the curvature of the Earth. If your locations are spread over a large geographic extent, geodesic longitude and latitude coordinates provide greater accuracy than Euclidean XY coordinates.
from geotable.projections import LONGITUDE_LATITUDE_PROJ4 t = GeoTable.load('shp.zip', target_proj4=LONGITUDE_LATITUDE_PROJ4) t.to_csv('/tmp/csv.zip', target_proj4=LONGITUDE_LATITUDE_PROJ4) t.to_shp('/tmp/shp.zip', target_proj4=LONGITUDE_LATITUDE_PROJ4)
Use the Universal Transverse Mercator (UTM) projection for compatibility with algorithms that use Euclidean distance on XY coordinates such as those found in scipy.spatial. If you know that your locations are confined to a small region, you can use the projected XY coordinates with standard Euclidean based algorithms, which tend to be significantly faster than their geodesic variants.
utm_proj4 = GeoTable.load_utm_proj4('shp.zip') t = GeoTable.load('csv.zip', target_proj4=utm_proj4) t.to_csv('/tmp/csv.zip', target_proj4=utm_proj4) t.to_shp('/tmp/shp.zip', target_proj4=utm_proj4)
Use the Spherical Mercator projection when visualization is more important than accuracy. Do not use this projection for algorithms where spatial accuracy is important.
from geotable.projections import SPHERICAL_MERCATOR_PROJ4 t = GeoTable.load('wkt.csv', target_proj4=SPHERICAL_MERCATOR_PROJ4) t.to_csv('/tmp/csv.zip', target_proj4=SPHERICAL_MERCATOR_PROJ4) t.to_shp('/tmp/shp.zip', target_proj4=SPHERICAL_MERCATOR_PROJ4)
You can render your spatial vectors in Jupyter Notebook with the draw function.
t = GeoTable.load('wkt.csv') t.draw() # Render the geometries in Jupyter Notebook
0.3
Support SOPHISTICATED_LONGITUDE and INSPIRING_LATITUDE
0.2
Add GeoTable.load
Add GeoTable.to_csv
Add GeoTable.to_shp
Add GeoTable.draw
Support LONGITUDE_LATITUDE_WKT, LATITUDE_LONGITUDE_WKT
0.1
Add ColorfulGeometryCollection
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