momepy API reference

The current version of momepy includes two implementations of most of the functionality due to the ongoing migration period moving from the legacy class-based API to a new function-based API. This page outlines the stable API. For the legacy functionality, see Legacy API.

Managing morphological elements

Momepy allows creation of a small subset of bespoke morphological geometric features.

morphological_tessellation(geometry[, clip, ...])	Generate morphological tessellation.
enclosed_tessellation(geometry, enclosures)	Generate enclosed tessellation
enclosures(primary_barriers[, limit, ...])	Generate enclosures based on passed barriers.
generate_blocks(tessellation, edges, buildings)	Generate blocks based on buildings, tessellation, and street network.

Additionally, it contains tools supporting these.

buffered_limit(gdf[, buffer, min_buffer, ...])	Define limit for tessellation as a buffer around buildings.
verify_tessellation(tessellation, geometry)	Check whether result matches buildings and contains only Polygons.

And tools linking various elements together.

get_nearest_street(buildings, streets[, ...])	Identify the nearest street for each building.
get_nearest_node(buildings, nodes, edges, ...)	Identify the nearest node for each building.
get_network_ratio(df, edges[, initial_buffer])	Link polygons to network edges based on the proportion of overlap (if a cell intersects more than one edge).

Measuring dimension

A set of functions to measure dimensions of geometric elements:

courtyard_area(geometry)	Calculates area of holes within geometry - area of courtyards.
floor_area(area, height[, floor_height])	Calculates floor area of each object based on height and area.
longest_axis_length(geometry)	Calculates the length of the longest axis of object.
perimeter_wall(geometry[, graph, buffer])	Calculate the perimeter wall length of the joined structure.
street_profile(streets, buildings[, ...])	Calculates the street profile characters.
volume(area, height)	Calculates volume of each object in given GeoDataFrame based on its height and area.
weighted_character(y, area, graph)	Calculates the weighted character.

Measuring shape

A set of functions to measure shape of geometric elements:

centroid_corner_distance(geometry[, eps, ...])	Calculates the centroid-corner distance of each object given its geometry.
circular_compactness(geometry)	Calculates the circular compactness of each object given its geometry.
compactness_weighted_axis(geometry[, ...])	Calculates the compactness-weighted axis of each object in a given GeoDataFrame.
convexity(geometry)	Calculates the convexity of each object given its geometry.
corners(geometry[, eps, include_interiors])	Calculates the number of corners of each object given its geometry.
courtyard_index(geometry[, courtyard_area])	Calculates the courtyard index of each object given its geometry.
elongation(geometry)	Calculates the elongation of each object given its geometry.
equivalent_rectangular_index(geometry)	Calculates the equivalent rectangular index of each object given its geometry.
facade_ratio(geometry)	Calculates the facade ratio of each object given its geometry.
form_factor(geometry, height)	Calculates the form factor of each object given its geometry and height.
fractal_dimension(geometry)	Calculates fractal dimension based on area and perimeter.
linearity(geometry)	Calculates the linearity of each LineString
rectangularity(geometry)	Calculates the rectangularity of each object given its geometry.
shape_index(geometry[, longest_axis_length])	Calculates the shape index of each object given its geometry.
square_compactness(geometry)	Calculates the square compactness of each object given its geometry.
squareness(geometry[, eps, include_interiors])	Calculates the squareness of each object given its geometry.

Measuring spatial distribution

A set of functions to measure spatial distribution of geometric elements:

alignment(orientation, graph)	Calculate the mean deviation of orientation adjacent elements
building_adjacency(contiguity_graph, ...)	Calculate the level of building adjacency.
cell_alignment(left_orientation, ...)	Calculate the difference between cell orientation and the orientation of object.
mean_interbuilding_distance(geometry, ...)	Calculate the mean distance between adjacent geometries within a set neighborhood
neighbor_distance(geometry, graph)	Calculate the mean distance to adjacent elements.
neighbors(geometry, graph[, weighted])	Calculate the number of neighbours captured by graph.
orientation(geometry)	Calculate the orientation of objects.
shared_walls(geometry[, strict, tolerance])	Calculate the length of shared walls of adjacent elements (typically buildings).
street_alignment(building_orientation, ...)	Calulate the deviation of the building orientation from the street orientation.

Measuring intensity

A set of functions to measure intensity characters:

courtyards(geometry, graph[, buffer])

Calculate the number of courtyards within the joined structure.

Note that additional intensity characters can be directly derived using libpysal.graph.Graph.describe() and functions describe_agg() and describe_reached_agg().

Measuring diversity

A set of functions to measure spatial diversity of elements and their values:

describe_agg(y, aggregation_key[, q, statistics])	Describe the distribution of values within the groups of an aggregation.
describe_reached_agg(y, graph_index, graph)	Describe the distribution of values reached on a neighbourhood graph.
gini(y, graph[, q])	Calculates the Gini index of values within neighbours defined in graph.
percentile(y, graph[, q])	Calculates linearly weighted percentiles of y values using the neighbourhoods and weights defined in graph.
shannon(y, graph[, binning, categorical, ...])	Calculates the Shannon index of values within neighbours defined in graph.
simpson(y, graph[, binning, gini_simpson, ...])	Calculates the Simpson's diversity index of values within neighbours defined in graph.
theil(y, graph[, q])	Calculates the Theil measure of inequality of values within neighbours defined in graph.
values_range(y, graph[, q])	Calculates the range of values within neighbours defined in graph.
mean_deviation(y, graph)	Calculate the mean deviation of each y value and its graph neighbours.

Note that additional diversity characters can be directly derived using libpysal.graph.Graph.describe().

Underlying components of shannon() and simpson() are also exposed for direct use:

shannon_diversity(data[, bins, categorical, ...])	Calculates the Shannon's diversity index of data.
simpson_diversity(values[, bins, categorical])	Calculates the Simpson's diversity index of data.

Measuring connectivity

A set of functions for the analysis of connectivity and configuration of street networks:

betweenness_centrality(graph[, name, mode, ...])	Calculates the shortest-path betweenness centrality for nodes.
cds_length(graph[, radius, mode, name, ...])	Calculates length of cul-de-sacs for subgraph around each node if radius is set, or for whole graph, if radius=None.
closeness_centrality(graph[, name, weight, ...])	Calculates the closeness centrality for nodes.
clustering(graph[, name])	Calculates the squares clustering coefficient for nodes.
cyclomatic(graph[, radius, name, distance, ...])	Calculates cyclomatic complexity for subgraph around each node if radius is set, or for whole graph, if radius=None.
edge_node_ratio(graph[, radius, name, ...])	Calculates edge / node ratio for subgraph around each node if radius is set, or for whole graph, if radius=None.
gamma(graph[, radius, name, distance, verbose])	Calculates connectivity gamma index for subgraph around each node if radius is set, or for whole graph, if radius=None.
mean_node_degree(graph[, radius, name, ...])	Calculates mean node degree for subgraph around each node if radius is set, or for whole graph, if radius=None.
mean_node_dist(graph[, name, length, verbose])	Calculates mean distance to neighbouring nodes.
mean_nodes(graph, attr)	Calculates mean value of nodes attr for each edge.
meshedness(graph[, radius, name, distance, ...])	Calculates meshedness for subgraph around each node if radius is set, or for whole graph, if radius=None.
node_degree(graph[, name])	Calculates node degree for each node.
node_density(graph, radius[, length, ...])	Calculate the density of a node's neighbours (for all nodes) on the street network defined in graph.
proportion(graph[, radius, three, four, ...])	Calculates the proportion of intersection types for subgraph around each node if radius is set, or for whole graph, if radius=None.
straightness_centrality(graph[, weight, ...])	Calculates the straightness centrality for nodes.
subgraph(graph[, radius, distance, ...])	Calculates all subgraph-based characters.
COINS(edge_gdf[, angle_threshold, flow_mode])	Calculates natural continuity and hierarchy of street networks in a given GeoDataFrame using the COINS algorithm.

With utilities allowing conversion between networkx objects and GeoPandas objects.

gdf_to_nx(gdf_network[, approach, length, ...])	Convert a LineString GeoDataFrame to a networkx.MultiGraph or other Graph as per specification.
nx_to_gdf(net[, points, lines, ...])	Convert a networkx.Graph to a LineString GeoDataFrame and Point GeoDataFrame.

Data preprocessing

Most of the algorithms have certain expectations about the quality of input data. The preprocessing module helps adapting the input data and fixing common issues.

close_gaps(gdf, tolerance)	Close gaps in LineString geometry where it should be contiguous.
extend_lines(gdf, tolerance[, target, ...])	Extends lines from gdf to itself or target within a set tolerance
remove_false_nodes(gdf)	Clean topology of existing LineString geometry by removal of nodes of degree 2.
consolidate_intersections(graph[, ...])	Consolidate close street intersections into a single node, collapsing short edges.
roundabout_simplification(edges[, polys, ...])	Selects the roundabouts from polys to create a center point to merge all incoming edges.

Additionally, there are methods for data assessment.

CheckTessellationInput(gdf[, shrink, ...])	Check input data for Tessellation for potential errors.
FaceArtifacts(gdf[, index, height_mins, ...])	Identify face artifacts in street networks

Further analysis can be done directly using methods available in libpysal.graph.Graph.