Urban metrics
In osmnxr: Download, Model and Analyze 'OpenStreetMap' Street Networks

knitr::opts_chunk$set(collapse = TRUE, comment = "#>", fig.width = 6.5, fig.height = 5.5)

library(osmnxr)

osmnxr summarises a street network with the geometric and topological measures used in urban morphology (Boeing 2025). We use a bundled real network — the centre of Olinda, Brazil — so everything here runs offline.

g <- ox_example("olinda")
g

Basic statistics

ox_basic_stats(g)

The pieces of this summary are standard urban indicators:

n_nodes / n_edges — intersections and street segments. Intersection density (nodes per km²) is the most common measure of network "grain".
mean_length — average street segment length, a proxy for block size.
total_length — total street length; divide by area for street density.
circuity — how much streets deviate from straight lines.

area_km2 <- as.numeric(sf::st_area(sf::st_convex_hull(sf::st_union(g$nodes)))) / 1e6
n_intersections <- sum(g$nodes$osmid %in% c(g$edges$u, g$edges$v))
n_intersections / area_km2 # intersections per km^2

Circuity

Circuity is total street length over straight-line distance between segment endpoints. A value near 1 means straight streets; higher means more winding:

ox_circuity(g)

Centrality: finding chokepoints

Betweenness centrality counts the share of shortest paths passing through each node. Its maximum highlights structural chokepoints — "a bridge connecting a city's halves" (Boeing & Ha 2024) — that are single points of failure for mobility and resilience.

ct <- ox_centrality(g, type = "betweenness", normalized = TRUE)
ct[order(-ct$betweenness), ][1:5, ]

Map it: the darkest, largest nodes carry the most through-traffic.

nodes <- g$nodes
nodes$betweenness <- ct$betweenness[match(nodes$osmid, ct$osmid)]
plot(g, col = "grey80", lwd = 0.6)
plot(nodes["betweenness"], pch = 19,
     cex = 0.4 + 4 * nodes$betweenness / max(nodes$betweenness),
     pal = function(n) hcl.colors(n, "YlOrRd", rev = TRUE), add = TRUE)

The high-betweenness nodes trace the through-routes that hold the network together — exactly the junctions a planner would protect or reinforce.

References

Boeing, G. (2025). Modeling and analyzing urban networks and amenities with OSMnx. Geographical Analysis.

Boeing, G., & Ha, J. (2024). Resilient by design: simulating street network disruptions across every urban area in the world. Transportation Research Part A.