<jupyter_start><jupyter_text>How many vessels are there in this data set?<jupyter_code>ais['mmsi'].nunique()<jupyter_output><empty_output><jupyter_text>Distribution of AIS observations geographically binned<jupyter_code>sns.jointplot(x=ais.lon, y=ais.lat, kind="hex", color="b")<jupyter_output><empty_output><jupyter_text>See also: Seaborn documentation: [Visualizing the distribution of a dataset](https://seaborn.pydata.org/tutorial/distributions.html)<jupyter_code>import geopandas as gpd<jupyter_output><empty_output><jupyter_text>Task: How many vessels visited Kemi during 16.3.2018?1) Draw the port of Kemi as a shapefile polygon with this tool (or any other tool): http://gis.ucla.edu/apps/click2shp/Hint: The port of Kemi is here: http://www.keminsatama.fi/fi/pallopanoraama.html  2) Read the Kemi port area from a shape file:<jupyter_code>kemi_port = gpd.read_file(DATA_DIR + 'kemi.prj')<jupyter_output><empty_output><jupyter_text>3) Read AIS-data to a GeoDataFrame:<jupyter_code>gdf = gpd.GeoDataFrame(ais, crs=crs, geometry=geometry)<jupyter_output><empty_output><jupyter_text>See more information on geopandas from tutorial: https://cambridgespark.com/content/tutorials/geopandas/index.html4) Make a geospatial inner join of the intersection of AIS observations and the port area:<jupyter_code>gpd.sjoin(gdf, kemi_port, how='inner', op='intersects')<jupyter_output><empty_output>
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