Urban Layer¶

This notebook will guide you through creating urban layers—like streets, intersections, and cities features as well as regions — for urban analysis.

Let’s dive in! 🚀

To get started, we need to import the urban_mapper module and create an instance of the UrbanMapper class. This instance, called mapper, will be our tool for building urban layers throughout the notebook.

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import urban_mapper as um

# Fire up UrbanMapper
mapper = um.UrbanMapper()
import urban_mapper as um

# Fire up UrbanMapper
mapper = um.UrbanMapper()

Mapping Streets and Roads¶

Time to create our first urban layer! We’ll map the streets and roads of Downtown Brooklyn using the urban_layer module. This layer type, streets_roads, pulls data from Open Street Map (OSM) via OSMnx, and we’ll specify a driving network. Further could be explored via https://osmnx.readthedocs.io/en/stable/user-reference.html#osmnx.graph.graph_from_place as we wrap that.

Next steps:

Set the layer type to streets_roads.
Define the location as Downtown Brooklyn.
Render the layer visually with static_render().

Here we go! 🛣️

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# Create streets layer
layer = (
    mapper
    .urban_layer # From the urban_layer module
    .with_type("streets_roads")  # With the type streets_roads
    .from_place("Downtown Brooklyn, New York City, USA", network_type="drive") # From a place
    # Note that after the place name we can specify what's called kwargs in Python.
    # Which are extra arguments / parameters that could be passed to the just chosen urban layer's type.
    # Here we chose the streets roads which are leveraged thanks to OSMNX, hence, network_type is one of their parameters.
    # See further in the documentation for the others.
    .build()
)
# Render layer
layer.preview()
layer.static_render()
# Create streets layer
layer = (
    mapper
    .urban_layer # From the urban_layer module
    .with_type("streets_roads")  # With the type streets_roads
    .from_place("Downtown Brooklyn, New York City, USA", network_type="drive") # From a place
    # Note that after the place name we can specify what's called kwargs in Python.
    # Which are extra arguments / parameters that could be passed to the just chosen urban layer's type.
    # Here we chose the streets roads which are leveraged thanks to OSMNX, hence, network_type is one of their parameters.
    # See further in the documentation for the others.
    .build()
)
# Render layer
layer.preview()
layer.static_render()

/home/docs/checkouts/readthedocs.org/user_builds/urbanmapper/envs/80/lib/python3.10/site-packages/osmnx/convert.py:542: FutureWarning: <class 'geopandas.array.GeometryArray'>._reduce will require a `keepdims` parameter in the future
  dupes = edges[mask].dropna(subset=["geometry"])

No description has been provided for this image

Plotting Street Intersections¶

Next up, let’s map the intersections in Downtown Brooklyn. Using the streets_intersections type, we’ll see where the roads meet—perfect for traffic or urban planning studies!

What’s next:

Switch the layer type to streets_intersections.
Use the same location and network type.
Render the intersections visually.

Let’s check it out! 🚦

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# Create streets layer
layer = (
    mapper
    .urban_layer # From the urban_layer module
    .with_type("streets_intersections")  # With the type streets_intersections
    .from_place("Downtown Brooklyn, New York City, USA", network_type="drive") # From a place
    # Note that after the place name we can specify what's called kwargs in Python.
    # Which are extra arguments / parameters that could be passed to the just chosen urban layer's type.
    # Here we chose the streets roads which are leveraged thanks to OSMNX, hence, network_type is one of their parameters.
    # See further in the documentation for the others.
    .build()
)
# Render layer
layer.static_render()
# Create streets layer
layer = (
    mapper
    .urban_layer # From the urban_layer module
    .with_type("streets_intersections")  # With the type streets_intersections
    .from_place("Downtown Brooklyn, New York City, USA", network_type="drive") # From a place
    # Note that after the place name we can specify what's called kwargs in Python.
    # Which are extra arguments / parameters that could be passed to the just chosen urban layer's type.
    # Here we chose the streets roads which are leveraged thanks to OSMNX, hence, network_type is one of their parameters.
    # See further in the documentation for the others.
    .build()
)
# Render layer
layer.static_render()

Adding Sidewalks from Tile2Net¶

Now, let’s bring in sidewalks! The streets_sidewalks type uses a shapefile (from Tile2Net, in this case) instead of a place name. You’ll need to replace the placeholder path with your actual file location.

Follow their readme to have an inference of sidewalks given a location of interest: https://github.com/VIDA-NYU/tile2net

If you believe this to be bothering your workflow, come shout out this issue: https://github.com/VIDA-NYU/UrbanMapper/issues/17

Next up:

Set the layer type to streets_sidewalks.
Load data from a shapefile.
Render the sidewalks.

Ready to walk the walk? 🚶‍♂️

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# Create streets layer
layer = (
    mapper
    .urban_layer # From the urban_layer module
    .with_type("streets_sidewalks")  # With the type streets_sidewalks
    .from_file("<path_towards_a_tile_2_net_shp_inference.shp") # Here you need to infer from Tile2Net to get the shapefile path to put here.
    .build()
)
# Render layer
layer.static_render()
# Create streets layer
layer = (
    mapper
    .urban_layer # From the urban_layer module
    .with_type("streets_sidewalks")  # With the type streets_sidewalks
    .from_file("

---------------------------------------------------------------------------
DataSourceError                           Traceback (most recent call last)
Cell In[4], line 7
      1 # Create streets layer
      2 layer = (
      3     mapper
      4     .urban_layer # From the urban_layer module
      5     .with_type("streets_sidewalks")  # With the type streets_sidewalks
      6     .from_file("<path_towards_a_tile_2_net_shp_inference.shp") # Here you need to infer from Tile2Net to get the shapefile path to put here.
----> 7     .build()
      8 )
      9 # Render layer
     10 layer.static_render()

File <@beartype(urban_mapper.modules.urban_layer.urban_layer_factory.UrbanLayerFactory.build) at 0x7dab09647250>:12, in build(__beartype_object_95637653152528, __beartype_get_violation, __beartype_conf, __beartype_check_meta, __beartype_func, *args, **kwargs)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/utils/helpers/require_attributes_not_none.py:35, in require_attributes_not_none.<locals>.decorator.<locals>.wrapper(self, *args, **kwargs)
     31             else:
     32                 raise ValueError(
     33                     f"Attribute '{name}' is None on {self.__class__.__name__}"
     34                 )
---> 35 return func(self, *args, **kwargs)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/utils/helpers/require_attributes_not_none.py:35, in require_attributes_not_none.<locals>.decorator.<locals>.wrapper(self, *args, **kwargs)
     31             else:
     32                 raise ValueError(
     33                     f"Attribute '{name}' is None on {self.__class__.__name__}"
     34                 )
---> 35 return func(self, *args, **kwargs)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layer_factory.py:252, in UrbanLayerFactory.build(self)
    248     raise ValueError(
    249         f"'{self.loading_method}' is not available for {self.layer_class.__name__}"
    250     )
    251 loading_func = getattr(layer, self.loading_method)
--> 252 loading_func(*self.loading_args, **self.loading_kwargs)
    253 layer.mappings = self.mappings
    254 self._instance = layer

File <@beartype(urban_mapper.modules.urban_layer.urban_layers.tile2net_sidewalks.Tile2NetSidewalks.from_file) at 0x7dab096440d0>:37, in from_file(__beartype_object_138173813406016, __beartype_get_violation, __beartype_conf, __beartype_args_name_keywordable, __beartype_object_138174842465472, __beartype_check_meta, __beartype_func, *args, **kwargs)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/tile2net_sidewalks.py:69, in Tile2NetSidewalks.from_file(self, file_path, **kwargs)
     43 def from_file(self, file_path: str | Path, **kwargs) -> None:
     44     """Load sidewalk data from a file produced by `Tile2Net`.
     45 
     46     This method reads a spatial data file containing `Tile2Net` output, filters for `sidewalk
   (...)
     67         >>> sidewalks = Tile2NetSidewalks().from_file("path/to/tile2net_output.geojson")
     68     """
---> 69     self.layer = gpd.read_file(file_path)
     70     self.layer = self.layer[self.layer["f_type"] == "sidewalk"]
     71     self.layer = self.layer.to_crs(self.coordinate_reference_system)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/envs/80/lib/python3.10/site-packages/geopandas/io/file.py:294, in _read_file(filename, bbox, mask, columns, rows, engine, **kwargs)
    291             from_bytes = True
    293 if engine == "pyogrio":
--> 294     return _read_file_pyogrio(
    295         filename, bbox=bbox, mask=mask, columns=columns, rows=rows, **kwargs
    296     )
    298 elif engine == "fiona":
    299     if pd.api.types.is_file_like(filename):

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/envs/80/lib/python3.10/site-packages/geopandas/io/file.py:547, in _read_file_pyogrio(path_or_bytes, bbox, mask, rows, **kwargs)
    538     warnings.warn(
    539         "The 'include_fields' and 'ignore_fields' keywords are deprecated, and "
    540         "will be removed in a future release. You can use the 'columns' keyword "
   (...)
    543         stacklevel=3,
    544     )
    545     kwargs["columns"] = kwargs.pop("include_fields")
--> 547 return pyogrio.read_dataframe(path_or_bytes, bbox=bbox, **kwargs)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/envs/80/lib/python3.10/site-packages/pyogrio/geopandas.py:265, in read_dataframe(path_or_buffer, layer, encoding, columns, read_geometry, force_2d, skip_features, max_features, where, bbox, mask, fids, sql, sql_dialect, fid_as_index, use_arrow, on_invalid, arrow_to_pandas_kwargs, **kwargs)
    260 if not use_arrow:
    261     # For arrow, datetimes are read as is.
    262     # For numpy IO, datetimes are read as string values to preserve timezone info
    263     # as numpy does not directly support timezones.
    264     kwargs["datetime_as_string"] = True
--> 265 result = read_func(
    266     path_or_buffer,
    267     layer=layer,
    268     encoding=encoding,
    269     columns=columns,
    270     read_geometry=read_geometry,
    271     force_2d=gdal_force_2d,
    272     skip_features=skip_features,
    273     max_features=max_features,
    274     where=where,
    275     bbox=bbox,
    276     mask=mask,
    277     fids=fids,
    278     sql=sql,
    279     sql_dialect=sql_dialect,
    280     return_fids=fid_as_index,
    281     **kwargs,
    282 )
    284 if use_arrow:
    285     meta, table = result

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/envs/80/lib/python3.10/site-packages/pyogrio/raw.py:198, in read(path_or_buffer, layer, encoding, columns, read_geometry, force_2d, skip_features, max_features, where, bbox, mask, fids, sql, sql_dialect, return_fids, datetime_as_string, **kwargs)
     59 """Read OGR data source into numpy arrays.
     60 
     61 IMPORTANT: non-linear geometry types (e.g., MultiSurface) are converted
   (...)
    194 
    195 """
    196 dataset_kwargs = _preprocess_options_key_value(kwargs) if kwargs else {}
--> 198 return ogr_read(
    199     get_vsi_path_or_buffer(path_or_buffer),
    200     layer=layer,
    201     encoding=encoding,
    202     columns=columns,
    203     read_geometry=read_geometry,
    204     force_2d=force_2d,
    205     skip_features=skip_features,
    206     max_features=max_features or 0,
    207     where=where,
    208     bbox=bbox,
    209     mask=_mask_to_wkb(mask),
    210     fids=fids,
    211     sql=sql,
    212     sql_dialect=sql_dialect,
    213     return_fids=return_fids,
    214     dataset_kwargs=dataset_kwargs,
    215     datetime_as_string=datetime_as_string,
    216 )

File pyogrio/_io.pyx:1240, in pyogrio._io.ogr_read()

File pyogrio/_io.pyx:220, in pyogrio._io.ogr_open()

DataSourceError: <path_towards_a_tile_2_net_shp_inference.shp: No such file or directory

Mapping Crosswalks from Tile2Net¶

Crosswalks are up next! Similar to sidewalks, the streets_crosswalks type uses a shapefile. Update the path to point to your Tile2Net output, and we’ll visualise pedestrian crossings.

Follow their readme to have an inference of sidewalks given a location of interest: https://github.com/VIDA-NYU/tile2net

What’s happening:

Use streets_crosswalks as the layer type.
Load the shapefile data.
Display the crosswalks.

Let’s cross over! 🚸

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# Create streets layer
layer = (
    mapper
    .urban_layer # From the urban_layer module
    .with_type("streets_crosswalks")  # With the type streets_crosswalks
    .from_file("<path_towards_a_tile_2_net_shp_inference.shp") # Here you need to infer from Tile2Net to get the shapefile path to put here.
    .build()
)
# Render layer
layer.static_render()
# Create streets layer
layer = (
    mapper
    .urban_layer # From the urban_layer module
    .with_type("streets_crosswalks")  # With the type streets_crosswalks
    .from_file("

---------------------------------------------------------------------------
DataSourceError                           Traceback (most recent call last)
Cell In[5], line 7
      1 # Create streets layer
      2 layer = (
      3     mapper
      4     .urban_layer # From the urban_layer module
      5     .with_type("streets_crosswalks")  # With the type streets_crosswalks
      6     .from_file("<path_towards_a_tile_2_net_shp_inference.shp") # Here you need to infer from Tile2Net to get the shapefile path to put here.
----> 7     .build()
      8 )
      9 # Render layer
     10 layer.static_render()

File <@beartype(urban_mapper.modules.urban_layer.urban_layer_factory.UrbanLayerFactory.build) at 0x7dab09647250>:12, in build(__beartype_object_95637653152528, __beartype_get_violation, __beartype_conf, __beartype_check_meta, __beartype_func, *args, **kwargs)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/utils/helpers/require_attributes_not_none.py:35, in require_attributes_not_none.<locals>.decorator.<locals>.wrapper(self, *args, **kwargs)
     31             else:
     32                 raise ValueError(
     33                     f"Attribute '{name}' is None on {self.__class__.__name__}"
     34                 )
---> 35 return func(self, *args, **kwargs)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/utils/helpers/require_attributes_not_none.py:35, in require_attributes_not_none.<locals>.decorator.<locals>.wrapper(self, *args, **kwargs)
     31             else:
     32                 raise ValueError(
     33                     f"Attribute '{name}' is None on {self.__class__.__name__}"
     34                 )
---> 35 return func(self, *args, **kwargs)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layer_factory.py:252, in UrbanLayerFactory.build(self)
    248     raise ValueError(
    249         f"'{self.loading_method}' is not available for {self.layer_class.__name__}"
    250     )
    251 loading_func = getattr(layer, self.loading_method)
--> 252 loading_func(*self.loading_args, **self.loading_kwargs)
    253 layer.mappings = self.mappings
    254 self._instance = layer

File <@beartype(urban_mapper.modules.urban_layer.urban_layers.tile2net_crosswalks.Tile2NetCrosswalks.from_file) at 0x7dab09644dc0>:37, in from_file(__beartype_object_138173813406016, __beartype_get_violation, __beartype_conf, __beartype_args_name_keywordable, __beartype_object_138174842465472, __beartype_check_meta, __beartype_func, *args, **kwargs)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/tile2net_crosswalks.py:70, in Tile2NetCrosswalks.from_file(self, file_path, **kwargs)
     44 def from_file(self, file_path: str | Path, **kwargs) -> None:
     45     """Load crosswalk data from a file produced by `Tile2Net`.
     46 
     47     This method reads a spatial data file containing `Tile2Net` output, filters for `crosswalk
   (...)
     68         >>> crosswalks = Tile2NetCrosswalks().from_file("path/to/tile2net_output.geojson")
     69     """
---> 70     self.layer = gpd.read_file(file_path)
     71     self.layer = self.layer[self.layer["f_type"] == "crosswalk"]
     72     self.layer = self.layer.to_crs(self.coordinate_reference_system)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/envs/80/lib/python3.10/site-packages/geopandas/io/file.py:294, in _read_file(filename, bbox, mask, columns, rows, engine, **kwargs)
    291             from_bytes = True
    293 if engine == "pyogrio":
--> 294     return _read_file_pyogrio(
    295         filename, bbox=bbox, mask=mask, columns=columns, rows=rows, **kwargs
    296     )
    298 elif engine == "fiona":
    299     if pd.api.types.is_file_like(filename):

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/envs/80/lib/python3.10/site-packages/geopandas/io/file.py:547, in _read_file_pyogrio(path_or_bytes, bbox, mask, rows, **kwargs)
    538     warnings.warn(
    539         "The 'include_fields' and 'ignore_fields' keywords are deprecated, and "
    540         "will be removed in a future release. You can use the 'columns' keyword "
   (...)
    543         stacklevel=3,
    544     )
    545     kwargs["columns"] = kwargs.pop("include_fields")
--> 547 return pyogrio.read_dataframe(path_or_bytes, bbox=bbox, **kwargs)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/envs/80/lib/python3.10/site-packages/pyogrio/geopandas.py:265, in read_dataframe(path_or_buffer, layer, encoding, columns, read_geometry, force_2d, skip_features, max_features, where, bbox, mask, fids, sql, sql_dialect, fid_as_index, use_arrow, on_invalid, arrow_to_pandas_kwargs, **kwargs)
    260 if not use_arrow:
    261     # For arrow, datetimes are read as is.
    262     # For numpy IO, datetimes are read as string values to preserve timezone info
    263     # as numpy does not directly support timezones.
    264     kwargs["datetime_as_string"] = True
--> 265 result = read_func(
    266     path_or_buffer,
    267     layer=layer,
    268     encoding=encoding,
    269     columns=columns,
    270     read_geometry=read_geometry,
    271     force_2d=gdal_force_2d,
    272     skip_features=skip_features,
    273     max_features=max_features,
    274     where=where,
    275     bbox=bbox,
    276     mask=mask,
    277     fids=fids,
    278     sql=sql,
    279     sql_dialect=sql_dialect,
    280     return_fids=fid_as_index,
    281     **kwargs,
    282 )
    284 if use_arrow:
    285     meta, table = result

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/envs/80/lib/python3.10/site-packages/pyogrio/raw.py:198, in read(path_or_buffer, layer, encoding, columns, read_geometry, force_2d, skip_features, max_features, where, bbox, mask, fids, sql, sql_dialect, return_fids, datetime_as_string, **kwargs)
     59 """Read OGR data source into numpy arrays.
     60 
     61 IMPORTANT: non-linear geometry types (e.g., MultiSurface) are converted
   (...)
    194 
    195 """
    196 dataset_kwargs = _preprocess_options_key_value(kwargs) if kwargs else {}
--> 198 return ogr_read(
    199     get_vsi_path_or_buffer(path_or_buffer),
    200     layer=layer,
    201     encoding=encoding,
    202     columns=columns,
    203     read_geometry=read_geometry,
    204     force_2d=force_2d,
    205     skip_features=skip_features,
    206     max_features=max_features or 0,
    207     where=where,
    208     bbox=bbox,
    209     mask=_mask_to_wkb(mask),
    210     fids=fids,
    211     sql=sql,
    212     sql_dialect=sql_dialect,
    213     return_fids=return_fids,
    214     dataset_kwargs=dataset_kwargs,
    215     datetime_as_string=datetime_as_string,
    216 )

File pyogrio/_io.pyx:1240, in pyogrio._io.ogr_read()

File pyogrio/_io.pyx:220, in pyogrio._io.ogr_open()

DataSourceError: <path_towards_a_tile_2_net_shp_inference.shp: No such file or directory

Exploring Parks in Brooklyn¶

Let’s switch gears and map some urban features! Using streets_features, we’ll find all the parks in Brooklyn by setting a leisure tag. See how tags works here https://osmnx.readthedocs.io/en/stable/user-reference.html#osmnx.features.features_from_place and here for more precision over the tags https://wiki.openstreetmap.org/wiki/Map_features

Next steps:

Set the layer type to streets_features.
Filter for parks in Brooklyn.
Render the result.

Time to analyse around the park! 🌳

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# Create streets layer
layer = (
    mapper.urban_layer.with_type("streets_features")  # From with the urban_layer module and with the type streets_features
    .from_place(
        "Brooklyn, New York City, USA", 
        tags={"leisure": "park"} # get the parks.
    )
    # Note that after the place name we can specify what's called kwargs in Python.
    # Which are extra arguments / parameters that could be passed to the just chosen urban layer's type.
    # Here we chose the streets roads which are leveraged thanks to OSMNX, hence, network_type is one of their parameters.
    # See further in the documentation for the others.
    .build()
)
layer.static_render()
# Create streets layer
layer = (
    mapper.urban_layer.with_type("streets_features")  # From with the urban_layer module and with the type streets_features
    .from_place(
        "Brooklyn, New York City, USA", 
        tags={"leisure": "park"} # get the parks.
    )
    # Note that after the place name we can specify what's called kwargs in Python.
    # Which are extra arguments / parameters that could be passed to the just chosen urban layer's type.
    # Here we chose the streets roads which are leveraged thanks to OSMNX, hence, network_type is one of their parameters.
    # See further in the documentation for the others.
    .build()
)
layer.static_render()

Finding Restaurants in Brooklyn¶

Hungry? Let’s map restaurants in Brooklyn with the amenity tag set to restaurant. This shows how flexible streets_features is for urban exploration.

What’s next:

Keep using streets_features.
Filter for restaurants.
Visualise the dining spots.

Bon appétit! 🍽️

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# Create streets layer
layer = (
    mapper.urban_layer.with_type("streets_features")  # From with the urban_layer module and with the type streets_features
    .from_place(
        "Brooklyn, New York City, USA", 
        tags={"amenity": "restaurant"} # get the restaurants.
    )
    .build()
)
layer.static_render()
# Create streets layer
layer = (
    mapper.urban_layer.with_type("streets_features")  # From with the urban_layer module and with the type streets_features
    .from_place(
        "Brooklyn, New York City, USA", 
        tags={"amenity": "restaurant"} # get the restaurants.
    )
    .build()
)
layer.static_render()

Locating Schools in Brooklyn¶

Now, let’s find schools! Using the amenity tag again, we’ll map educational institutions across Brooklyn.

Next up:

Target schools with streets_features.
Render the school locations.

Class is in session! 📚

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# Create streets layer
layer = (
    mapper.urban_layer.with_type("streets_features")  # From with the urban_layer module and with the type streets_features
    .from_place(
        "Brooklyn, New York City, USA", 
        tags={"amenity": "school"} # get the schools.
    )
    .build()
)
layer.static_render()
# Create streets layer
layer = (
    mapper.urban_layer.with_type("streets_features")  # From with the urban_layer module and with the type streets_features
    .from_place(
        "Brooklyn, New York City, USA", 
        tags={"amenity": "school"} # get the schools.
    )
    .build()
)
layer.static_render()

Mapping Hospitals in Brooklyn¶

Health matters! Let’s map hospitals in Brooklyn using the amenity tag set to hospital.

What’s happening:

Use streets_features to find hospitals.
Display the results.

Stay healthy! 🏥

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# Create streets layer
layer = (
    mapper.urban_layer.with_type("streets_features")  # From with the urban_layer module and with the type streets_features
    .from_place(
        "Brooklyn, New York City, USA", 
        tags={"amenity": "hospital"} # get the hospitals.
    )
    .build()
)
layer.static_render()
# Create streets layer
layer = (
    mapper.urban_layer.with_type("streets_features")  # From with the urban_layer module and with the type streets_features
    .from_place(
        "Brooklyn, New York City, USA", 
        tags={"amenity": "hospital"} # get the hospitals.
    )
    .build()
)
layer.static_render()

Charting Cycleways in Brooklyn¶

For the cyclists out there, let’s map cycleways using the highway tag. This highlights bike-friendly paths in Brooklyn.

Next steps:

Set the tag to cycleway.
Render the bike lanes.

Pedal on! 🚲

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# Create streets layer
layer = (
    mapper.urban_layer.with_type("streets_features")  # From with the urban_layer module and with the type streets_features
    .from_place(
        "Brooklyn, New York City, USA", 
        tags={"highway": "cycleway"} # get the cycleways.
    )
    .build()
)
layer.static_render()
# Create streets layer
layer = (
    mapper.urban_layer.with_type("streets_features")  # From with the urban_layer module and with the type streets_features
    .from_place(
        "Brooklyn, New York City, USA", 
        tags={"highway": "cycleway"} # get the cycleways.
    )
    .build()
)
layer.static_render()

Pinpointing Public Transport Stops¶

Let’s map public transport stops in Brooklyn with the public_transport tag. Great for transit planning!

What’s next:

Filter for stop_position.
Visualise the stops.

All aboard! 🚌

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# Create streets layer
layer = (
    mapper.urban_layer.with_type("streets_features")  # From with the urban_layer module and with the type streets_features
    .from_place(
        "Brooklyn, New York City, USA", 
        tags={"public_transport": "stop_position"} # get the public transport stops.
    )
    .build()
)
layer.static_render()
# Create streets layer
layer = (
    mapper.urban_layer.with_type("streets_features")  # From with the urban_layer module and with the type streets_features
    .from_place(
        "Brooklyn, New York City, USA", 
        tags={"public_transport": "stop_position"} # get the public transport stops.
    )
    .build()
)
layer.static_render()

Highlighting Water Bodies¶

Nature time! Let’s map water bodies in Brooklyn using the natural tag set to water.

Next up:

Target water features.
Render the map.

Splash! 💧

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# Create streets layer
layer = (
    mapper.urban_layer.with_type("streets_features")  # From with the urban_layer module and with the type streets_features
    .from_place(
        "Brooklyn, New York City, USA", 
        tags={"natural": "water"} # get the water bodies.
    )
    .build()
)
layer.static_render()
# Create streets layer
layer = (
    mapper.urban_layer.with_type("streets_features")  # From with the urban_layer module and with the type streets_features
    .from_place(
        "Brooklyn, New York City, USA", 
        tags={"natural": "water"} # get the water bodies.
    )
    .build()
)
layer.static_render()

Combining Parks and Playgrounds¶

Why choose one when you can have both? Let’s map parks and playgrounds in Brooklyn by using a list in the leisure tag.

What’s happening:

Use a list to filter multiple features.
Display the combined result.

Fun for all! 🎠

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# Create streets layer
layer = (
    mapper.urban_layer.with_type("streets_features")  # From with the urban_layer module and with the type streets_features
    .from_place(
        "Brooklyn, New York City, USA", 
        tags = {"leisure": ["park", "playground"]} # get the parks and playgrounds.
    )
    .build()
)
layer.static_render()
# Create streets layer
layer = (
    mapper.urban_layer.with_type("streets_features")  # From with the urban_layer module and with the type streets_features
    .from_place(
        "Brooklyn, New York City, USA", 
        tags = {"leisure": ["park", "playground"]} # get the parks and playgrounds.
    )
    .build()
)
layer.static_render()

Savoring French Cuisine¶

Craving something specific? Let’s find French restaurants in Brooklyn with the cuisine tag.

Next steps:

Filter for French cuisine.
Render the tasty spots.

Ooh la la! 🇫🇷

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# Create streets layer
layer = (
    mapper.urban_layer.with_type("streets_features")  # From with the urban_layer module and with the type streets_features
    .from_place(
        "Brooklyn, New York City, USA", 
        tags={ "cuisine": "french" } # get the french restaurants.
    )
    .build()
)
layer.static_render()
# Create streets layer
layer = (
    mapper.urban_layer.with_type("streets_features")  # From with the urban_layer module and with the type streets_features
    .from_place(
        "Brooklyn, New York City, USA", 
        tags={ "cuisine": "french" } # get the french restaurants.
    )
    .build()
)
layer.static_render()

___________________________________¶

Mapping Cities¶

We will be switching to what we call as regions based urban layers from now on. Having a larger zoom at the world map layer.

First, let’s map the cities in Hérault, France. We’re using the region_cities layer type to fetch city boundaries from OpenStreetMap. The from_place method targets 'Hérault, France', and we’ll build and render the layer, adding an interactive map with city names in tooltips. Note that the tooltip is not necessary, but open street map returns a lot of noise attributes that are not needed for this very example, feel free to explore that yourself.

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layer = (
    mapper.urban_layer.with_type("region_cities") # From with the urban_layer module and with the type region_cities
    .from_place("Hérault, France") # From a place
    .build()
)
layer.static_render()
layer.get_layer().explore(tooltip=["name"])
layer = (
    mapper.urban_layer.with_type("region_cities") # From with the urban_layer module and with the type region_cities
    .from_place("Hérault, France") # From a place
    .build()
)
layer.static_render()
layer.get_layer().explore(tooltip=["name"])

/home/docs/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/admin_regions_.py:103: UserWarning: Administrative levels vary across regions. The system will infer the most appropriate admin_level based on the data and division type, but you can (and is recommended to) override it with 'overwrite_admin_level'.
  warnings.warn(

/home/docs/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/admin_regions_.py:168: UserWarning: Inferred admin_level for city: 8. Other available levels: ['10', '11', '6', '7', '8', '9']. You can override this with 'overwrite_admin_level' if desired.
  warnings.warn(

Out[15]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Mapping States¶

In this section, we’re mapping states in Grenada. The region_states layer type retrieves state or province boundaries. Using from_place with 'Grenada', we’ll construct and visualise the layer interactively.

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layer = (
    mapper.urban_layer.with_type("region_states") # From with the urban_layer module and with the type region_states
    .from_place("Grenada") # From a place
    .build()
)
layer.static_render()
layer.get_layer().explore()
layer = (
    mapper.urban_layer.with_type("region_states") # From with the urban_layer module and with the type region_states
    .from_place("Grenada") # From a place
    .build()
)
layer.static_render()
layer.get_layer().explore()

/home/docs/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/admin_regions_.py:103: UserWarning: Administrative levels vary across regions. The system will infer the most appropriate admin_level based on the data and division type, but you can (and is recommended to) override it with 'overwrite_admin_level'.
  warnings.warn(

/home/docs/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/admin_regions_.py:168: UserWarning: Inferred admin_level for state: 6. Other available levels: ['2', '6']. You can override this with 'overwrite_admin_level' if desired.
  warnings.warn(

Out[16]:

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Mapping Neighbourhoods¶

Here, we’re mapping neighbourhoods in Paris, France. The region_neighbourhoods layer type focuses on smaller administrative areas like districts. We’ll use from_place with 'Paris, France' to build and display the layer, complete with neighbourhood names in an interactive map.

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layer = (
    mapper.urban_layer.with_type("region_neighborhoods") # From with the urban_layer module and with the type region_neighborhoods
    .from_place("Paris, France") # From a place
    .build()
)
layer.static_render()
layer.get_layer().explore(tooltip=["name"])
layer = (
    mapper.urban_layer.with_type("region_neighborhoods") # From with the urban_layer module and with the type region_neighborhoods
    .from_place("Paris, France") # From a place
    .build()
)
layer.static_render()
layer.get_layer().explore(tooltip=["name"])

/home/docs/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/admin_regions_.py:103: UserWarning: Administrative levels vary across regions. The system will infer the most appropriate admin_level based on the data and division type, but you can (and is recommended to) override it with 'overwrite_admin_level'.
  warnings.warn(

/home/docs/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/admin_regions_.py:168: UserWarning: Inferred admin_level for neighborhood: 10. Other available levels: ['10', '6', '7', '8', '9']. You can override this with 'overwrite_admin_level' if desired.
  warnings.warn(

Out[17]:

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Next, we’re applying the same approach to map neighbourhoods in Montpellier, France. This shows how region_neighbourhoods can be used across different cities to visualise local administrative boundaries.

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layer = (
    mapper.urban_layer.with_type("region_neighborhoods") # From with the urban_layer module and with the type region_neighborhoods
    .from_place("Montpellier, France") # From a place
    .build()
)
layer.static_render()
layer.get_layer().explore(tooltip=["name"])
layer = (
    mapper.urban_layer.with_type("region_neighborhoods") # From with the urban_layer module and with the type region_neighborhoods
    .from_place("Montpellier, France") # From a place
    .build()
)
layer.static_render()
layer.get_layer().explore(tooltip=["name"])

/home/docs/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/admin_regions_.py:103: UserWarning: Administrative levels vary across regions. The system will infer the most appropriate admin_level based on the data and division type, but you can (and is recommended to) override it with 'overwrite_admin_level'.
  warnings.warn(

/home/docs/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/admin_regions_.py:168: UserWarning: Inferred admin_level for neighborhood: 11. Other available levels: ['10', '11', '8']. You can override this with 'overwrite_admin_level' if desired.
  warnings.warn(

Out[18]:

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Now, we’re mapping neighbourhoods in New York City, United States. Due to incomplete data in OpenStreetMap, we’re overriding the default admin level to '7' with overwrite_admin_level to get accurate boundaries, then displaying them interactively.

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layer = (
    mapper.urban_layer.with_type("region_neighborhoods") # From with the urban_layer module and with the type region_neighborhoods
    .from_place("New York City, United States", overwrite_admin_level="7") # Here we overwrite because the inference do not find a correct enough level due to the lack of information on OSM.
    .build()
)
layer.get_layer().reset_index().explore(tooltip=["name"])
layer = (
    mapper.urban_layer.with_type("region_neighborhoods") # From with the urban_layer module and with the type region_neighborhoods
    .from_place("New York City, United States", overwrite_admin_level="7") # Here we overwrite because the inference do not find a correct enough level due to the lack of information on OSM.
    .build()
)
layer.get_layer().reset_index().explore(tooltip=["name"])

/home/docs/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/admin_regions_.py:103: UserWarning: Administrative levels vary across regions. The system will infer the most appropriate admin_level based on the data and division type, but you can (and is recommended to) override it with 'overwrite_admin_level'.
  warnings.warn(

Out[19]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Let’s switch back to cities and map those in Corsica. Using region_cities, we’ll retrieve and render city boundaries for this region, adding an interactive map with tooltips.

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layer = (
    mapper.urban_layer.with_type("region_cities") # From with the urban_layer module and with the type region_cities
    .from_place("Corsica") # From a place
    .build()
)
layer.static_render()
layer.get_layer().reset_index().explore(tooltip=["name"])
layer = (
    mapper.urban_layer.with_type("region_cities") # From with the urban_layer module and with the type region_cities
    .from_place("Corsica") # From a place
    .build()
)
layer.static_render()
layer.get_layer().reset_index().explore(tooltip=["name"])

/home/docs/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/admin_regions_.py:103: UserWarning: Administrative levels vary across regions. The system will infer the most appropriate admin_level based on the data and division type, but you can (and is recommended to) override it with 'overwrite_admin_level'.
  warnings.warn(

/home/docs/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/admin_regions_.py:168: UserWarning: Inferred admin_level for city: 8. Other available levels: ['4', '6', '7', '8']. You can override this with 'overwrite_admin_level' if desired.
  warnings.warn(

Out[20]:

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Here, we’re mapping neighbourhoods around a specific address in London: 'Tower Bridge Rd, London SE1 2UP, United Kingdom'. The from_address method with a 1000-metre radius lets us focus on a precise area, which we’ll visualise interactively.

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layer = (
    mapper.urban_layer.with_type("region_neighborhoods") # From with the urban_layer module and with the type region_neighborhoods
    .from_address("Tower Bridge Rd, London SE1 2UP, United Kingdom", dist=1000.0) # From a specific address and a distance radius from it
    .build()
)
layer.static_render()
layer.get_layer().reset_index().explore(tooltip=["name"])
layer = (
    mapper.urban_layer.with_type("region_neighborhoods") # From with the urban_layer module and with the type region_neighborhoods
    .from_address("Tower Bridge Rd, London SE1 2UP, United Kingdom", dist=1000.0) # From a specific address and a distance radius from it
    .build()
)
layer.static_render()
layer.get_layer().reset_index().explore(tooltip=["name"])

/home/docs/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/admin_regions_.py:230: UserWarning: Administrative levels vary across regions. The system will infer the most appropriate admin_level based on the data and division type, but you can (and is recommended to) override it with 'overwrite_admin_level'.
  warnings.warn(

/home/docs/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/admin_regions_.py:299: UserWarning: Inferred admin_level for neighborhood: 8. Other available levels: ['6', '8']. You can override this with 'overwrite_admin_level' if desired.
  warnings.warn(

Out[21]:

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Similarly, we’re mapping neighbourhoods around '370 Jay St, Brooklyn, New York 11201, United States'. Using from_address with a 1000-metre distance, we’ll build and display the local administrative areas.

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layer = (
    mapper.urban_layer.with_type("region_neighborhoods") # From with the urban_layer module and with the type region_neighborhoods
    .from_address("370 Jay St, Brooklyn, New York 11201, United States", dist=1000.0) # From a specific address and a distance radius from it
    .build()
)
layer.static_render()
layer.get_layer().reset_index().explore(tooltip=["name"])
layer = (
    mapper.urban_layer.with_type("region_neighborhoods") # From with the urban_layer module and with the type region_neighborhoods
    .from_address("370 Jay St, Brooklyn, New York 11201, United States", dist=1000.0) # From a specific address and a distance radius from it
    .build()
)
layer.static_render()
layer.get_layer().reset_index().explore(tooltip=["name"])

/home/docs/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/admin_regions_.py:230: UserWarning: Administrative levels vary across regions. The system will infer the most appropriate admin_level based on the data and division type, but you can (and is recommended to) override it with 'overwrite_admin_level'.
  warnings.warn(

/home/docs/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/admin_regions_.py:299: UserWarning: Inferred admin_level for neighborhood: 6. Other available levels: ['6', '7']. You can override this with 'overwrite_admin_level' if desired.
  warnings.warn(

Out[22]:

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In this example, we’re mapping neighbourhoods in Barcelona, Spain, using a polygon. We geocode 'Barcelona, Spain' to get its shape, then use from_polygon to build and render the neighbourhood layer interactively.

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from geopy.geocoders import Nominatim
from shapely.wkt import loads

# Geocoding Tokyo Japan as a polygon form.
geolocator = Nominatim(user_agent="urban_mapper")
place_polygon = None
location = geolocator.geocode("Barcelona, Spain", geometry="wkt")
place_polygon = loads(location.raw["geotext"])

layer = (
    mapper.urban_layer.with_type("region_neighborhoods") # From with the urban_layer module and with the type region_neighborhoods
    .from_polygon(place_polygon) # From a polygon
    .build()
)
layer.static_render()
layer.get_layer().reset_index().explore(tooltip=["name"])
from geopy.geocoders import Nominatim
from shapely.wkt import loads

# Geocoding Tokyo Japan as a polygon form.
geolocator = Nominatim(user_agent="urban_mapper")
place_polygon = None
location = geolocator.geocode("Barcelona, Spain", geometry="wkt")
place_polygon = loads(location.raw["geotext"])

layer = (
    mapper.urban_layer.with_type("region_neighborhoods") # From with the urban_layer module and with the type region_neighborhoods
    .from_polygon(place_polygon) # From a polygon
    .build()
)
layer.static_render()
layer.get_layer().reset_index().explore(tooltip=["name"])

/home/docs/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/admin_regions_.py:364: UserWarning: Administrative levels vary across regions. The system will infer the most appropriate admin_level based on the data and division type, but you can (and is recommended to) override it with 'overwrite_admin_level'.
  warnings.warn(

/home/docs/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/admin_regions_.py:413: UserWarning: Inferred admin_level for neighborhood: 10. Other available levels: ['10', '8', '9']. You can override this with 'overwrite_admin_level' if desired.
  warnings.warn(

Out[23]:

Make this Notebook Trusted to load map: File -> Trust Notebook

Custom Urban Layer's File & Re-using Previous Urban Layer's¶

Here, we’re mapping showing that you could use your own geojson && shapefile file to load a urban layer for a complete urban mapper's analysis. For instance, in the very case below, we show the very detailed map of neighbourhoods in New York City, USA; as well as, the European Union's countries. On the other case, we also show that we could load the urban layer of a previous analysis.

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from urban_mapper.modules.urban_layer import UrbanLayerFactory

# Think of the following as a full urban mapper's analysis
streets_layer = (
    UrbanLayerFactory()
    .with_type("streets_roads")
    .from_place("Downtown Brooklyn, NY")
    .build()
)

# Think of the following as the start of a new urba analysis using the previous one's enriched layer
custom_layer = (
    UrbanLayerFactory()
    .with_type("custom_urban_layer")
    .from_urban_layer(streets_layer)
    .build()
)

print(custom_layer.preview())
custom_layer.static_render()
from urban_mapper.modules.urban_layer import UrbanLayerFactory

# Think of the following as a full urban mapper's analysis
streets_layer = (
    UrbanLayerFactory()
    .with_type("streets_roads")
    .from_place("Downtown Brooklyn, NY")
    .build()
)

# Think of the following as the start of a new urba analysis using the previous one's enriched layer
custom_layer = (
    UrbanLayerFactory()
    .with_type("custom_urban_layer")
    .from_urban_layer(streets_layer)
    .build()
)

print(custom_layer.preview())
custom_layer.static_render()

/home/docs/checkouts/readthedocs.org/user_builds/urbanmapper/envs/80/lib/python3.10/site-packages/osmnx/convert.py:542: FutureWarning: <class 'geopandas.array.GeometryArray'>._reduce will require a `keepdims` parameter in the future
  dupes = edges[mask].dropna(subset=["geometry"])

Urban Layer: CustomUrbanLayer
  Source: urban_layer
  CRS: EPSG:4326
  Mappings:
    No mappings

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from urban_mapper.modules.urban_layer import UrbanLayerFactory

custom_layer = (
    UrbanLayerFactory()
    .with_type("custom_urban_layer")
    .from_file("./NYC Neighborhoods.geojson")
    .build()
)

print(custom_layer.preview())
custom_layer.static_render()
from urban_mapper.modules.urban_layer import UrbanLayerFactory

custom_layer = (
    UrbanLayerFactory()
    .with_type("custom_urban_layer")
    .from_file("./NYC Neighborhoods.geojson")
    .build()
)

print(custom_layer.preview())
custom_layer.static_render()

---------------------------------------------------------------------------
DataSourceError                           Traceback (most recent call last)
Cell In[25], line 7
      1 from urban_mapper.modules.urban_layer import UrbanLayerFactory
      3 custom_layer = (
      4     UrbanLayerFactory()
      5     .with_type("custom_urban_layer")
      6     .from_file("./NYC Neighborhoods.geojson")
----> 7     .build()
      8 )
     10 print(custom_layer.preview())
     11 custom_layer.static_render()

File <@beartype(urban_mapper.modules.urban_layer.urban_layer_factory.UrbanLayerFactory.build) at 0x7dab09647250>:12, in build(__beartype_object_95637653152528, __beartype_get_violation, __beartype_conf, __beartype_check_meta, __beartype_func, *args, **kwargs)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/utils/helpers/require_attributes_not_none.py:35, in require_attributes_not_none.<locals>.decorator.<locals>.wrapper(self, *args, **kwargs)
     31             else:
     32                 raise ValueError(
     33                     f"Attribute '{name}' is None on {self.__class__.__name__}"
     34                 )
---> 35 return func(self, *args, **kwargs)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/utils/helpers/require_attributes_not_none.py:35, in require_attributes_not_none.<locals>.decorator.<locals>.wrapper(self, *args, **kwargs)
     31             else:
     32                 raise ValueError(
     33                     f"Attribute '{name}' is None on {self.__class__.__name__}"
     34                 )
---> 35 return func(self, *args, **kwargs)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layer_factory.py:252, in UrbanLayerFactory.build(self)
    248     raise ValueError(
    249         f"'{self.loading_method}' is not available for {self.layer_class.__name__}"
    250     )
    251 loading_func = getattr(layer, self.loading_method)
--> 252 loading_func(*self.loading_args, **self.loading_kwargs)
    253 layer.mappings = self.mappings
    254 self._instance = layer

File <@beartype(urban_mapper.modules.urban_layer.urban_layers.custom_urban_layer.CustomUrbanLayer.from_file) at 0x7dab09646170>:37, in from_file(__beartype_object_138173813406016, __beartype_get_violation, __beartype_conf, __beartype_args_name_keywordable, __beartype_object_95637653628976, __beartype_check_meta, __beartype_func, *args, **kwargs)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/custom_urban_layer.py:82, in CustomUrbanLayer.from_file(self, file_path, **kwargs)
     77 if not (str(file_path).endswith(".shp") or str(file_path).endswith(".geojson")):
     78     raise ValueError(
     79         "Only shapefiles (.shp) and GeoJSON (.geojson) are supported for loading from file."
     80     )
---> 82 self.layer = gpd.read_file(file_path)
     83 if self.layer.crs is None:
     84     self.layer.set_crs(DEFAULT_CRS, inplace=True)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/envs/80/lib/python3.10/site-packages/geopandas/io/file.py:294, in _read_file(filename, bbox, mask, columns, rows, engine, **kwargs)
    291             from_bytes = True
    293 if engine == "pyogrio":
--> 294     return _read_file_pyogrio(
    295         filename, bbox=bbox, mask=mask, columns=columns, rows=rows, **kwargs
    296     )
    298 elif engine == "fiona":
    299     if pd.api.types.is_file_like(filename):

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/envs/80/lib/python3.10/site-packages/geopandas/io/file.py:547, in _read_file_pyogrio(path_or_bytes, bbox, mask, rows, **kwargs)
    538     warnings.warn(
    539         "The 'include_fields' and 'ignore_fields' keywords are deprecated, and "
    540         "will be removed in a future release. You can use the 'columns' keyword "
   (...)
    543         stacklevel=3,
    544     )
    545     kwargs["columns"] = kwargs.pop("include_fields")
--> 547 return pyogrio.read_dataframe(path_or_bytes, bbox=bbox, **kwargs)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/envs/80/lib/python3.10/site-packages/pyogrio/geopandas.py:265, in read_dataframe(path_or_buffer, layer, encoding, columns, read_geometry, force_2d, skip_features, max_features, where, bbox, mask, fids, sql, sql_dialect, fid_as_index, use_arrow, on_invalid, arrow_to_pandas_kwargs, **kwargs)
    260 if not use_arrow:
    261     # For arrow, datetimes are read as is.
    262     # For numpy IO, datetimes are read as string values to preserve timezone info
    263     # as numpy does not directly support timezones.
    264     kwargs["datetime_as_string"] = True
--> 265 result = read_func(
    266     path_or_buffer,
    267     layer=layer,
    268     encoding=encoding,
    269     columns=columns,
    270     read_geometry=read_geometry,
    271     force_2d=gdal_force_2d,
    272     skip_features=skip_features,
    273     max_features=max_features,
    274     where=where,
    275     bbox=bbox,
    276     mask=mask,
    277     fids=fids,
    278     sql=sql,
    279     sql_dialect=sql_dialect,
    280     return_fids=fid_as_index,
    281     **kwargs,
    282 )
    284 if use_arrow:
    285     meta, table = result

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/envs/80/lib/python3.10/site-packages/pyogrio/raw.py:198, in read(path_or_buffer, layer, encoding, columns, read_geometry, force_2d, skip_features, max_features, where, bbox, mask, fids, sql, sql_dialect, return_fids, datetime_as_string, **kwargs)
     59 """Read OGR data source into numpy arrays.
     60 
     61 IMPORTANT: non-linear geometry types (e.g., MultiSurface) are converted
   (...)
    194 
    195 """
    196 dataset_kwargs = _preprocess_options_key_value(kwargs) if kwargs else {}
--> 198 return ogr_read(
    199     get_vsi_path_or_buffer(path_or_buffer),
    200     layer=layer,
    201     encoding=encoding,
    202     columns=columns,
    203     read_geometry=read_geometry,
    204     force_2d=force_2d,
    205     skip_features=skip_features,
    206     max_features=max_features or 0,
    207     where=where,
    208     bbox=bbox,
    209     mask=_mask_to_wkb(mask),
    210     fids=fids,
    211     sql=sql,
    212     sql_dialect=sql_dialect,
    213     return_fids=return_fids,
    214     dataset_kwargs=dataset_kwargs,
    215     datetime_as_string=datetime_as_string,
    216 )

File pyogrio/_io.pyx:1240, in pyogrio._io.ogr_read()

File pyogrio/_io.pyx:220, in pyogrio._io.ogr_open()

DataSourceError: ./NYC Neighborhoods.geojson: No such file or directory

In [26]:

Copied!





from urban_mapper.modules.urban_layer import UrbanLayerFactory

custom_layer = (
    UrbanLayerFactory()
    .with_type("custom_urban_layer")
    .from_file("./Europe_merged.shp")
    .build()
)

print(custom_layer.preview())
custom_layer.static_render()
from urban_mapper.modules.urban_layer import UrbanLayerFactory

custom_layer = (
    UrbanLayerFactory()
    .with_type("custom_urban_layer")
    .from_file("./Europe_merged.shp")
    .build()
)

print(custom_layer.preview())
custom_layer.static_render()

---------------------------------------------------------------------------
DataSourceError                           Traceback (most recent call last)
Cell In[26], line 7
      1 from urban_mapper.modules.urban_layer import UrbanLayerFactory
      3 custom_layer = (
      4     UrbanLayerFactory()
      5     .with_type("custom_urban_layer")
      6     .from_file("./Europe_merged.shp")
----> 7     .build()
      8 )
     10 print(custom_layer.preview())
     11 custom_layer.static_render()

File <@beartype(urban_mapper.modules.urban_layer.urban_layer_factory.UrbanLayerFactory.build) at 0x7dab09647250>:12, in build(__beartype_object_95637653152528, __beartype_get_violation, __beartype_conf, __beartype_check_meta, __beartype_func, *args, **kwargs)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/utils/helpers/require_attributes_not_none.py:35, in require_attributes_not_none.<locals>.decorator.<locals>.wrapper(self, *args, **kwargs)
     31             else:
     32                 raise ValueError(
     33                     f"Attribute '{name}' is None on {self.__class__.__name__}"
     34                 )
---> 35 return func(self, *args, **kwargs)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/utils/helpers/require_attributes_not_none.py:35, in require_attributes_not_none.<locals>.decorator.<locals>.wrapper(self, *args, **kwargs)
     31             else:
     32                 raise ValueError(
     33                     f"Attribute '{name}' is None on {self.__class__.__name__}"
     34                 )
---> 35 return func(self, *args, **kwargs)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layer_factory.py:252, in UrbanLayerFactory.build(self)
    248     raise ValueError(
    249         f"'{self.loading_method}' is not available for {self.layer_class.__name__}"
    250     )
    251 loading_func = getattr(layer, self.loading_method)
--> 252 loading_func(*self.loading_args, **self.loading_kwargs)
    253 layer.mappings = self.mappings
    254 self._instance = layer

File <@beartype(urban_mapper.modules.urban_layer.urban_layers.custom_urban_layer.CustomUrbanLayer.from_file) at 0x7dab09646170>:37, in from_file(__beartype_object_138173813406016, __beartype_get_violation, __beartype_conf, __beartype_args_name_keywordable, __beartype_object_95637653628976, __beartype_check_meta, __beartype_func, *args, **kwargs)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/checkouts/80/src/urban_mapper/modules/urban_layer/urban_layers/custom_urban_layer.py:82, in CustomUrbanLayer.from_file(self, file_path, **kwargs)
     77 if not (str(file_path).endswith(".shp") or str(file_path).endswith(".geojson")):
     78     raise ValueError(
     79         "Only shapefiles (.shp) and GeoJSON (.geojson) are supported for loading from file."
     80     )
---> 82 self.layer = gpd.read_file(file_path)
     83 if self.layer.crs is None:
     84     self.layer.set_crs(DEFAULT_CRS, inplace=True)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/envs/80/lib/python3.10/site-packages/geopandas/io/file.py:294, in _read_file(filename, bbox, mask, columns, rows, engine, **kwargs)
    291             from_bytes = True
    293 if engine == "pyogrio":
--> 294     return _read_file_pyogrio(
    295         filename, bbox=bbox, mask=mask, columns=columns, rows=rows, **kwargs
    296     )
    298 elif engine == "fiona":
    299     if pd.api.types.is_file_like(filename):

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/envs/80/lib/python3.10/site-packages/geopandas/io/file.py:547, in _read_file_pyogrio(path_or_bytes, bbox, mask, rows, **kwargs)
    538     warnings.warn(
    539         "The 'include_fields' and 'ignore_fields' keywords are deprecated, and "
    540         "will be removed in a future release. You can use the 'columns' keyword "
   (...)
    543         stacklevel=3,
    544     )
    545     kwargs["columns"] = kwargs.pop("include_fields")
--> 547 return pyogrio.read_dataframe(path_or_bytes, bbox=bbox, **kwargs)

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/envs/80/lib/python3.10/site-packages/pyogrio/geopandas.py:265, in read_dataframe(path_or_buffer, layer, encoding, columns, read_geometry, force_2d, skip_features, max_features, where, bbox, mask, fids, sql, sql_dialect, fid_as_index, use_arrow, on_invalid, arrow_to_pandas_kwargs, **kwargs)
    260 if not use_arrow:
    261     # For arrow, datetimes are read as is.
    262     # For numpy IO, datetimes are read as string values to preserve timezone info
    263     # as numpy does not directly support timezones.
    264     kwargs["datetime_as_string"] = True
--> 265 result = read_func(
    266     path_or_buffer,
    267     layer=layer,
    268     encoding=encoding,
    269     columns=columns,
    270     read_geometry=read_geometry,
    271     force_2d=gdal_force_2d,
    272     skip_features=skip_features,
    273     max_features=max_features,
    274     where=where,
    275     bbox=bbox,
    276     mask=mask,
    277     fids=fids,
    278     sql=sql,
    279     sql_dialect=sql_dialect,
    280     return_fids=fid_as_index,
    281     **kwargs,
    282 )
    284 if use_arrow:
    285     meta, table = result

File ~/checkouts/readthedocs.org/user_builds/urbanmapper/envs/80/lib/python3.10/site-packages/pyogrio/raw.py:198, in read(path_or_buffer, layer, encoding, columns, read_geometry, force_2d, skip_features, max_features, where, bbox, mask, fids, sql, sql_dialect, return_fids, datetime_as_string, **kwargs)
     59 """Read OGR data source into numpy arrays.
     60 
     61 IMPORTANT: non-linear geometry types (e.g., MultiSurface) are converted
   (...)
    194 
    195 """
    196 dataset_kwargs = _preprocess_options_key_value(kwargs) if kwargs else {}
--> 198 return ogr_read(
    199     get_vsi_path_or_buffer(path_or_buffer),
    200     layer=layer,
    201     encoding=encoding,
    202     columns=columns,
    203     read_geometry=read_geometry,
    204     force_2d=force_2d,
    205     skip_features=skip_features,
    206     max_features=max_features or 0,
    207     where=where,
    208     bbox=bbox,
    209     mask=_mask_to_wkb(mask),
    210     fids=fids,
    211     sql=sql,
    212     sql_dialect=sql_dialect,
    213     return_fids=return_fids,
    214     dataset_kwargs=dataset_kwargs,
    215     datetime_as_string=datetime_as_string,
    216 )

File pyogrio/_io.pyx:1240, in pyogrio._io.ogr_read()

File pyogrio/_io.pyx:220, in pyogrio._io.ogr_open()

DataSourceError: ./Europe_merged.shp: No such file or directory

Previewing Your Layer¶

Curious about your layer’s details? The preview() method gives you a quick rundown of the layer type, source, and more—super useful for sharing or debugging.

Next up:

Print a preview of the last layer you executed.

Take a peek! 👀

In [27]:

Copied!

# Preview layer
print(layer.preview())
# Preview layer
print(layer.preview())

Urban Layer: Region_neighborhood
  Focussing tags: {'boundary': 'administrative'}
  CRS: EPSG:4326
  Mappings:
    No mappings

Congratulations!¶

Awesome work! 🎉 You’ve built a variety of urban layers, from streets to state boundaries, and even explored them interactively. You’re now ready to take it further—maybe enrich your layers with more data or analyze them with enricher?

What’s next:

Experiment with new tags or areas.
Dive into the urban_mapper docs for more tricks.

Happy mapping! 🗺️