What is rcrisp?

rcrisp automates the morphological delineation of riverside urban areas following a method developed by Forgaci (2018, pp. 88–89). It overcomes the challenge of arbitrary urban river corridor delineation by providing a reliable workflow to produce morphologically grounded spatial analytical units.

Such spatial units enable integrated local analyses (many different layers within one case) and large-scale cross-case analyses (many cases using comparable spatial units) in a wide range of domains of application, such as urban planning, environmental management, public space design, and disaster risk reduction.

In short, given a city name and a river name, it:

• identifies corridor boundaries on the street network along the edges of the river valley;
• (optionally) segments the corridor; and
• (optionally) delineates the river space.

Workflow

1. Acquire base data:
   • OpenStreetMap layers using get_osm_*() functions
   • (Optional) global Digital Elevation Model data
2. Delineate the river valley, corridor, segments and/or riverspace with the all-in-one delineate() function or with the dedicated delineate_*() functions
3. Visualize, validate, and export results for use in downstream analyses

Data considerations

• Use an appropriate projected CRS (e.g., a relevant UTM EPSG code).
• Verify OSM coverage and elevation availability for your area.
• Spatial (street and railway) network completeness and elevation data quality may affect corridor and segment accuracy.
• The delineate() function retrieves OSM data and global DEM data by default, so no additional data retrieval is needed.
• The delineate_*() functions allow for any data input, not only OSM and global DEM data.

Example

library(rcrisp)

# Parameters
city_name <- "Bucharest"
river_name <- "Dâmbovița"
epsg_code <- 32635

# Delineation
bd <- delineate(city_name, river_name, segments = TRUE)

# Base layers for visualisation
bb <- get_osm_bb(city_name)
streets <- get_osm_streets(bb, epsg_code)$geometry
railways <- get_osm_railways(bb, epsg_code)$geometry

# Plot
plot(bd$corridor)
plot(railways, col = "darkgrey", add = TRUE, lwd = 0.5)
plot(streets, add = TRUE)
plot(bd$segments, border = "orange", add = TRUE, lwd = 3)
plot(bd$corridor, border = "red", add = TRUE, lwd = 3)

Interpretation and next steps

• Use the segments and/or river spaces for comparative analyses along the river; or
• Integrate relevant data layers within a segment and/or river space of interest; or
• Run the analysis on other cities to compare a phenomenon of interest across corridors, segments and/or river spaces;
• Export to GIS formats for further processing.