Fig 1.
Schematic of selected connectivity measures.
(a) Edge-related measures include sinuosity, which incorporates the sum of the end-to-end length for each edge, and dendricity metrics, which incorporate a classification of all edges into one of four network-theoretic types. Solid lines indicate streets. (b) Node-related measures are based on the nodal degree, indicated by colored circles, and circuity, which relates to the shortest path lengths and the direct-path distances between one node and all its neighbors within some radius. Green lines indicate streets, while gray lines indicate straight-line distances to nearby nodes within the 500 m radius shown in white.
Table 1.
SNDi corresponds to the PCA1 component.
Fig 2.
Three prototypical street-network styles.
(A) The grid paradigm has the highest connectivity and an SNDi of -1.01. (B) The medieval paradigm is relatively highly connected (SNDi of 0.66) but far from a grid. (C) The culs-de-sac paradigm is the most sprawling, with SNDi of 7.9.
Fig 3.
Examples of high and low street-network sprawl.
Street network diagrams from sample neighborhoods in two countries, restricted to the 30-arc second grid cell. A version with satellite and Google Streetview imagery integrated is available online. The upper and lower rows show examples at the 5th and 95th global percentiles of SNDi respectively. The S1 File contains a large set of examples.
Fig 4.
Empirical street-network types.
The eight types are identified through k-means cluster analysis of 30-arc second grid cells, and ordered by increasing SNDi. For each type, streets in an example grid cell near the type’s centroid are shown, along with a plot of the type-level distributions (colored lines) and means (colored circles) of SNDi across grid cells. The caption indicates the (latitude, longitude) of each example grid cell as well as the country where it is located. Service roads and paths that are closed to cars (most visible in Type H) are depicted with thinner grey lines.
Table 2.
Regressions of vehicle ownership and walking.
Coefficients shown are standardized (beta) coefficients, with robust standard errors in parentheses. All p-values are less than 0.1%. Constant terms are not shown. All coefficients are statistically significant at conventional levels. All models include fixed effects for country (United States, France, New Zealand and Japan). For data sources and units of analysis, see the S1 File.
Fig 5.
Distribution of empirical types by vehicle ownership and walking, United States.
Vehicle ownership and commute mode share are aggregated to grid cells, based on an area-weighted average of census block groups that intersect a given grid cell. The lower two deciles are not defined for fraction commute by walking; because ∼30% of the grid cells have a zero walk share, the 1st and 2nd deciles are combined with the 3rd decile.
Fig 6.
Urban street-network sprawl (SNDi) by country, 2018.
The inset charts the within-country distribution of SNDi for the largest countries by population. The distributions are grid cells weighted by the number of nodes in each cell; see Fig F in S1 File for unweighted distributions and distributions for additional countries.
Table 3.
Countries listed by street-network connectivity.
The top ten and bottom ten countries with ≥10M population and ≥100k street nodes, ordered by SNDi. Countries with populations >20M are listed in bold. A fuller list is in Table F in S1 File.
Table 4.
Cities listed by street-network connectivity.
The top ten and bottom ten cities with ≥3,000 street nodes, ordered by SNDi. A fuller list is in Table G in S1 File.
Fig 7.
Distribution of empirical types.
Distributions are shown for the largest ten countries (by population) and cities (by length of edges, restricted to one per country). Other countries and cities are shown in Figs H, I and J in S1 File. City extents are defined using [15].