Geometry and topology (lines and polygons)

In this chapter, we will use the Brazil GPS Trajectories dataset from UCI. It contains GPS points (latitude and longitude) with timestamps. It also contains unique track_id for each trajectory.

Here is what the first five rows of the data look like:

GPS trajectory: first five rows

We create a GeoDataFrame using a function we created in Chapter 4, Making Sense of Humongous Location Datasets:

def create_gdf(df, lat, lon):
    """ Convert pandas dataframe into a Geopandas GeoDataFrame"""
    crs = {'init': 'epsg:4326'}
    geometry = [Point(xy) for xy in zip(df[lon], df[lat])]
    gdf = gpd.GeoDataFrame(df, crs=crs, geometry=geometry)
    return gdf

traj_gdf =  create_gdf(trajectories, "latitude", "longitude")
aracaju_city_points = traj_gdf[(traj_gdf['latitude']&lt...