Syntax of osmnx.bearing.orientation_entropy() Function

Orientation entropy is the entropy of its edges’ bidirectional bearings across evenly spaced bins. Ignores self-loop edges as their bearings are undefined.

osmnx.bearing.orientation_entropy(Gu, num_bins=36, min_length=0, weight=None)

Parameters:

• Gu (networkx.MultiGraph) – undirected, unprojected graph with bearing attributes on each edge
• num_bins (int) – number of bins; for example, if num_bins=36 is provided, then each bin will represent 10 degrees around the compass
• min_length (float) – ignore edges with length attributes less than min_length; useful to ignore the noise of many very short edges
• weight (string) – if not None, weight edges’ bearings by this (non-null) edge attribute.

Returns: entropy – the graph’s orientation entropy

Return Type: Float

The measure of entropy describes the heterogeneity of data. For street network modeling, the entropy reveals a city’s street order and disorder. Cities with high orientation entropy show that the streets are spread out as much as possible. While low orientation entropy means that the streets are nearly concentrated to a point. In this article, we will calculate the Undirected graph’s Orientation entropy using OSMnx bearing Module in Python.