momepy.FaceArtifacts#

class momepy.FaceArtifacts(gdf, index='circular_compactness', height_mins=-inf, height_maxs=0.008, prominence=0.00075)[source]#

Identify face artifacts in street networks

For a given street network composed of transportation-oriented geometry containing features representing things like roundabouts, dual carriegaways and complex intersections, identify areas enclosed by geometry that is considered a face artifact as per [Fleischmann and Vybornova, under review]. Face artifacts highlight areas with a high likelihood of being of non-morphological (e.g. transporation) origin and may require simplification prior morphological analysis. See [Fleischmann and Vybornova, under review] for more details.

Parameters:

gdfgeopandas.GeoDataFrame: GeoDataFrame containing street network represented as (Multi)LineString geometry
indexstr, optional: A type of the shape compacntess index to be used. Available are [‘circlular_compactness’, ‘isoperimetric_quotient’, ‘diameter_ratio’], by default “circular_compactness”
height_minsfloat, optional: Required depth of valleys, by default np.NINF
height_maxsfloat, optional: Required height of peaks, by default 0.008
prominencefloat, optional: Required prominence of peaks, by default 0.00075

Examples

>>> fa = momepy.FaceArtifacts(street_network_prague)
>>> fa.threshold
6.9634555986177045
>>> fa.face_artifacts.head()
                                             geometry  face_artifact_index
6   POLYGON ((-744164.625 -1043922.362, -744167.39...             5.112844
9   POLYGON ((-744154.119 -1043804.734, -744152.07...             6.295660
10  POLYGON ((-744101.275 -1043738.053, -744103.80...             2.862871
12  POLYGON ((-744095.511 -1043623.478, -744095.35...             3.712403
17  POLYGON ((-744488.466 -1044533.317, -744489.33...             5.158554

Attributes:

thresholdfloat: Identified threshold between face polygons and face artifacts
face_artifactsGeoDataFrame: A GeoDataFrame of geometries identified as face artifacts
polygonsGeoDataFrame: All polygons resulting from polygonization of the input gdf with the face_artifact_index
kdescipy.stats._kde.gaussian_kde: Representation of a kernel-density estimate using Gaussian kernels.
pdfnumpy.ndarray: Probability density function
peaksnumpy.ndarray: locations of peaks in pdf
valleysnumpy.ndarray: locations of valleys in pdf

__init__(gdf, index='circular_compactness', height_mins=-inf, height_maxs=0.008, prominence=0.00075)[source]#

Methods

__init__(gdf[, index, height_mins, ...])