A dataset containing 14,358 raw network segments representing intersected road routes
between African cities. Each segment is defined by start and end coordinates with
aggregate importance metrics. This dataset is provided to demonstrate how package
functions like consolidate_graph() and
simplify_network() can process messy segment data
into clean analytical networks like africa_network.
Usage
data(africa_segments)Format
A data frame with 14,358 rows and 7 columns:
- FX
Numeric. Start point longitude (range: -17.4 to 49.2).
- FY
Numeric. Start point latitude (range: -34.2 to 37.2).
- TX
Numeric. End point longitude (range: -17.0 to 49.1).
- TY
Numeric. End point latitude (range: -34.2 to 37.2).
- passes
Integer. Number of optimal inter-city routes passing through this segment. Range: 1 to 1,615, median: 46.
- gravity
Numeric. Sum of population gravity weights from routes using this segment. Computed as sum of (pop_origin * pop_destination / spherical_distance_km) / 1e9.
- gravity_rd
Numeric. Sum of road-distance-weighted gravity from routes. Computed as sum of (pop_origin * pop_destination / road_distance_m) / 1e9.
Source
Derived from OpenStreetMap routing data via OSRM, processed through route intersection and aggregation.
Dataset constructed for: Krantz, S. (2024). Optimal Investments in Africa's Road Network. Policy Research Working Paper 10893. World Bank. doi:10.1596/1813-9450-10893 . Replication materials: https://github.com/SebKrantz/OptimalAfricanRoads.
Details
This dataset represents an intermediate stage in network construction, after routes have been
intersected but before network simplification. The segments have been simplified using
linestrings_from_graph() to retain only start and end coordinates.
The segments can be used to demonstrate the flownet network processing workflow:
Convert segments to an sf LINESTRING object using
linestrings_from_graph()Apply
consolidate_graph()to merge nearby nodesApply
simplify_network()to remove intermediate nodes
The passes field indicates how many optimal city-to-city routes use each segment,
serving as a measure of segment importance in the network. Higher values indicate
segments that are critical for efficient inter-city connectivity.
Examples
data(africa_segments)
head(africa_segments)
#> FX FY TX TY passes gravity gravity_rd
#> 1 -17.44759 14.69386 -17.04861 14.70538 59 93.20669 67.218731
#> 2 -17.44759 14.69386 -16.70998 14.40844 6 59.98582 45.491608
#> 3 -17.04861 14.70538 -16.70998 14.40844 48 61.68941 42.121072
#> 4 -17.04861 14.70538 -16.96602 14.72614 11 31.51728 25.097660
#> 5 -17.03345 20.93362 -16.89503 21.28569 63 4.09779 2.981159
#> 6 -16.96602 14.72614 -16.63849 15.11222 10 20.13822 14.737626
# Summary statistics
summary(africa_segments[, c("passes", "gravity", "gravity_rd")])
#> passes gravity gravity_rd
#> Min. : 1.0 Min. : 0.0001 Min. : 0.0001
#> 1st Qu.: 14.0 1st Qu.: 3.9964 1st Qu.: 2.8740
#> Median : 46.0 Median : 16.5966 Median : 11.4518
#> Mean : 113.9 Mean : 76.2857 Mean : 53.9421
#> 3rd Qu.: 131.0 3rd Qu.: 64.4550 3rd Qu.: 43.4980
#> Max. :1615.0 Max. :4144.5363 Max. :3278.1380
# Segments with highest traffic
africa_segments[order(-africa_segments$passes), ][1:10, ]
#> FX FY TX TY passes gravity gravity_rd
#> 672 -0.43130 12.19363 -0.35795 12.18282 1615 654.6566 483.7826
#> 5829 -1.52978 12.36658 -1.52187 12.37092 1584 646.9655 478.4648
#> 5830 -1.52187 12.37092 -1.51004 12.37915 1584 646.9655 478.4648
#> 649 -1.36024 12.43948 -1.26867 12.48558 1581 649.2992 480.4201
#> 651 -1.26432 12.48681 -0.43130 12.19363 1574 645.7477 477.1712
#> 647 -1.44519 12.39922 -1.37384 12.43304 1570 646.5881 478.1731
#> 641 -1.53359 12.36966 -1.52978 12.36658 1536 584.3362 435.5978
#> 5713 -2.01904 12.19641 -1.75856 12.23416 1437 564.3777 415.2477
#> 5714 -1.75503 12.23701 -1.63531 12.32471 1437 564.3777 415.2477
#> 13627 35.69371 -0.15936 35.70070 -0.16562 1395 496.1546 353.6817
# \donttest{
# Convert to sf and plot
library(sf)
segments_sf <- linestrings_from_graph(africa_segments)
plot(segments_sf["passes"])
# }