Date | Operation | Operator | Power Unit | Location |
---|---|---|---|---|
2024-04-24 | Spread/Spray | Evan | Hagie STS12 | 200 |
2024-04-27 | Spread/Spray | Bryan | Hagie STS12 | 6 |
2024-04-27 | Spread/Spray | Bryan | Hagie STS12 | 5 |
Lat = 40.47693586173173
Lon = -87.01022508566594
Coordinates
Coordinates
Length
Coordinates
Length
Area
Manipulation and analysis of geometric objects in the Cartesian plane
docsCheck out this Jupiter Notebook
GeoPy | Link with map services |
PySAL, GeoPlot | Visualize Geospatial data |
ArcPy | Working with ArcGIS |
GDAL | Driver for GIS software |
GeoPandas | Pandas with GeoSpatial functionalities |
import geopandas as gpd
world_df = gpd.read_file(
gpd.datasets.get_path('naturalearth_lowres')
)
pop_est | continent | name | iso_a3 | gdp_md_est | geometry | |
---|---|---|---|---|---|---|
0 | 889953.0 | Oceania | Fiji | FJI | 5496 | MULTIPOLYGON (((180.00000 -16.06713, 180.00000... |
1 | 58005463.0 | Africa | Tanzania | TZA | 63177 | POLYGON ((33.90371 -0.95000, 34.07262 -1.05982... |
2 | 603253.0 | Africa | W. Sahara | ESH | 907 | POLYGON ((-8.66559 27.65643, -8.66512 27.58948... |
3 | 37589262.0 | North America | Canada | CAN | 1736425 | MULTIPOLYGON (((-122.84000 49.00000, -122.9742... |
4 | 328239523.0 | North America | United States of America | USA | 21433226 | MULTIPOLYGON (((-122.84000 49.00000, -120.0000... |
world_df.plot()
import numpy as np
world_df['log_gdp'] = np.log(world_df['gdp_md_est'])
world_df.plot("log_gdp")
world_df = world_df.to_crs(
"+proj=eck4 +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs"
)
world_df.area
https://www.learndatasci.com/tutorials/geospatial-data-python-geopandas-shapely/
ShapeFile | ESRI standard. Commonly used by GIS software like ArcGIS etc. |
GeoJSON | JSON-style with standard specificationfor holding spatial data. |
KML | XML format with spatial data. Commonly used with Google Earth. |
Geo-Database | Database with spatial data feature e.g. SpatiaLite, PostGIS. |
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