momepy.StreetAlignment

momepy.StreetAlignment(left, right, orientations, network_id=None, left_network_id=None, right_network_id=None)

Calculate the difference between street orientation and orientation of another object in degrees. The orientation of a street segment is represented by the orientation of line connecting the first and the last point of the segment. A network ID linking each object to specific street segment is needed, and can be generated by momepy.get_network_id(). Either network_id or both left_network_id and right_network_id are required.

\[\left|{\textit{building orientation} - \textit{street orientation}}\right|\]

Parameters:

leftGeoDataFrame

A GeoDataFrame containing objects to analyse.

rightGeoDataFrame

A GeoDataFrame containing a street network.

orientationsstr, list, np.array, pd.Series

The name of the dataframe column, np.array, or pd.Series where object orientation values are stored. The object can be calculated using momepy.Orientation.

network_idstr (default None)

The name of the column storing network ID in both left and right.

left_network_idstr, list, np.array, pd.Series (default None)

The name of the left dataframe column, np.array, or pd.Series where object network IDs are stored.

right_network_idstr, list, np.array, pd.Series (default None)

The name of the right dataframe column, np.array, or pd.Series of streets with unique network IDs. These IDs have to be defined beforehand and can be defined using momepy.unique_id().

Attributes:

seriesSeries

A Series containing resulting values.

leftGeoDataFrame

The original left GeoDataFrame.

rightGeoDataFrame

The original right GeoDataFrame.

network_idstr

The name of the column storing network ID in both left and right.

left_network_idSeries

A Series containing used left ID.

right_network_idSeries

A Series containing used right ID.

Examples

>>> buildings_df['street_alignment'] = momepy.StreetAlignment(buildings_df,
...                                                           streets_df,
...                                                           'orientation',
...                                                           'nID',
...                                                           'nID').series
>>> buildings_df['street_alignment'][0]
0.29073888476702336