filter_clusters

Create groups of edges that are crossing the river in nearby locations,
using a density-based clustering method (DBSCAN). This is to make sure that
edges representing e.g. different lanes of the same street are treated as
part of the same crossing. For each cluster, select the shortest edge.

internal

Provides tools to automate the morphological delineation of
riverside urban areas based on a method introduced in Forgaci (2018)
<doi:10.7480/abe.2018.31>. Delineation entails the identification of
corridor boundaries, segmentation of the corridor, and delineation of
the river space using two-dimensional spatial information from street
network data and digital elevation data in a projected CRS. The
resulting delineation can be used to characterise spatial phenomena
that can be related to the river as a central element.

Claudiu Forgaci

rcrisp

Automate the Delineation of Urban River Spaces

Francesco Nattino

Fakhereh Alidoost

Meiert Willem Grootes

Netherlands eScience Center 

filter_clusters function

<dl><dt>crossings</dt>
<dd>Crossing edge geometries as a simple feature object</dd>
<dt>river</dt>
<dd>The river geometry as a simple feature object</dd>
<dt>eps</dt>
<dd>DBSCAN parameter referring to the size (radius) distance of the
neighborhood. Should approximate the distance between edges that we want
to consider as a single river crossing</dd></dl>

Arguments

Cluster the river crossings and select the shortest crossing per cluster — filter_clusters

<dl>

<dt>crossings</dt>
<dd>Crossing edge geometries as a simple feature object</dd>


<dt>river</dt>
<dd>The river geometry as a simple feature object</dd>


<dt>eps</dt>
<dd>DBSCAN parameter referring to the size (radius) distance of the
neighborhood. Should approximate the distance between edges that we want
to consider as a single river crossing</dd>

</dl>

filter_clusters: Cluster the river crossings and select the shortest crossing per cluster

Description

Usage

Value

Arguments