momepy.street_profile#

momepy.street_profile(streets, buildings, distance=10, tick_length=50, height=None)[source]#

Calculates the street profile characters.

This functions returns a DataFrame with widths, standard deviation of width, openness, heights, standard deviation of height and ratio height/width. The algorithm generates perpendicular lines to the streets dataframe features every distance and measures values on intersections with features of buildings. If no feature is reached within tick_length its value is set as width (being a theoretical maximum).

Derived from [Araldi and Fusco, 2019].

Parameters:

streetsGeoDataFrame: A GeoDataFrame containing streets to analyse.
buildingsGeoDataFrame: A GeoDataFrame containing buildings along the streets. Only Polygon geometries are currently supported.
distanceint (default 10): The distance between perpendicular ticks.
tick_lengthint (default 50): The length of ticks.
height: pd.Series (default None): The pd.Series where building height are stored. If set to None, height and ratio height/width will not be calculated.

Returns:

DataFrame

Examples

>>> path = momepy.datasets.get_path("bubenec")
>>> buildings = geopandas.read_file(path, layer="buildings")
>>> streets = geopandas.read_file(path, layer="streets")
>>> streets.head()
                                            geometry
0  LINESTRING (1603585.64 6464428.774, 1603413.20...
1  LINESTRING (1603268.502 6464060.781, 1603296.8...
2  LINESTRING (1603607.303 6464181.853, 1603592.8...
3  LINESTRING (1603678.97 6464477.215, 1603675.68...
4  LINESTRING (1603537.194 6464558.112, 1603557.6...

>>> result = momepy.street_profile(streets, buildings)
>>> result.head()
       width  openness  width_deviation
0  47.905964  0.946429         0.020420
1  42.418068  0.615385         2.644521
2  32.131831  0.608696         2.864438
3  50.000000  1.000000              NaN
4  50.000000  1.000000              NaN

If you know height of each building, you can pass that along to get back more information:

>>> import numpy as np
>>> import pandas as pd
>>> rng = np.random.default_rng(seed=42)
>>> height = pd.Series(rng.integers(low=9, high=30, size=len(buildings)))
>>> result = momepy.street_profile(streets, buildings, height=height)
>>> result.head()
       width  openness  width_deviation     height  height_deviation  hw_ratio
0  47.905964  0.946429         0.020420  12.666667          4.618802  0.264407
1  42.418068  0.615385         2.644521  21.500000          6.467869  0.506859
2  32.131831  0.608696         2.864438  17.555556          4.901647  0.546360
3  50.000000  1.000000              NaN        NaN               NaN       NaN
4  50.000000  1.000000              NaN        NaN               NaN       NaN