Final R project

Introduction

The Seattle Light Rail system plays a major role in transportation by connecting commuters to neighborhoods, attractions, and the airport. This project uses network analysis to explore which stations are most central to the system’s connectivity. By identifying key stations and visualizing their connections, I can better understand the strengths of the system and areas where it could be improved.

Setup and Data Description

library(tidyverse)

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library(igraph)

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library(scales)

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# Read in the data
stations <- read_csv("Seattle_Transportation_Plan_Transit_Element.csv")

## Rows: 58 Columns: 16
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): STATUS, NAME, STATION, SEGMENT, BOARD, STATION_DE, REGIONAL_T, EDI...
## dbl  (6): OBJECTID_1, LINK_TYPE, CONTRACT, New, x, y
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# Heres the link for my file i downloaded it as a csv #https://data.seattle.gov/dataset/Light-Rail-Stations/nng6-bzbm/about_data


nodes <- stations %>%
  distinct(NAME, x, y) %>%
  rename(name = NAME)

# Create edge list
ordered_stations <- nodes$name[order(nodes$y, decreasing = TRUE)]
edges <- data.frame(
  from = head(ordered_stations, -1),
  to = tail(ordered_stations, -1)
)

# Build the graph
g <- graph_from_data_frame(edges, vertices = nodes, directed = FALSE)

layout_coords <- layout_with_kk(g)
deg <- degree(g)
sizes <- rescale(deg, to = c(6, 18))
top_stations <- names(sort(deg, decreasing = TRUE))[1:5]
colors <- ifelse(V(g)$name %in% top_stations, "red", "skyblue")
labels <- ifelse(V(g)$name %in% top_stations, V(g)$name, "")

stations <- read_csv("Seattle_Transportation_Plan_Transit_Element.csv")

## Rows: 58 Columns: 16
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): STATUS, NAME, STATION, SEGMENT, BOARD, STATION_DE, REGIONAL_T, EDI...
## dbl  (6): OBJECTID_1, LINK_TYPE, CONTRACT, New, x, y
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## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

head(stations)

## # A tibble: 6 × 16
##   OBJECTID_1 STATUS  NAME  STATION SEGMENT BOARD STATION_DE LINK_TYPE REGIONAL_T
##        <dbl> <chr>   <chr> <chr>   <chr>   <chr> <chr>          <dbl> <chr>     
## 1          1 Existi… NE 1… NE 145… Lynnwo… Appr… Board App…         0 <NA>      
## 2          2 Future  NE 1… NE 130… Lynnwo… Prop… Proposed …         0 <NA>      
## 3          3 Future  Shor… Shorel… Lynnwo… Appr… Board App…         0 <NA>      
## 4          4 Future  Moun… Mountl… Lynnwo… Appr… Board App…         0 <NA>      
## 5          5 Future  Lynn… Lynnwo… Lynnwo… Appr… Board App…         0 <NA>      
## 6          6 Future  220t… 220th … Lynnwo… Prop… Proposed …         0 <NA>      
## # ℹ 7 more variables: CONTRACT <dbl>, EDITDATE <chr>, WEBSITE <chr>,
## #   EDIT_FLAG <chr>, New <dbl>, x <dbl>, y <dbl>

library(scales) 

layout_coords <- layout_with_kk(g)  

deg <- degree(g)


deg[is.na(deg)] <- 0

sizes <- rescale(deg, to = c(6, 18))  


top_stations <- names(sort(deg, decreasing = TRUE))[1:5]


colors <- ifelse(V(g)$name %in% top_stations, "red", "skyblue")


labels <- ifelse(V(g)$name %in% top_stations, V(g)$name, "")

# Plot
plot(g,
     layout = layout_coords,
     vertex.size = sizes,
     vertex.color = colors,
     vertex.label = labels,
     vertex.label.cex = 0.85,
     vertex.label.color = "black",
     vertex.frame.color = "gray50",
     edge.color = "gray80",
     edge.width = 0.6,
     main = "Seattle Light Rail Network: Key Stations Highlighted")

# Layout 1
plot(g,
     layout = layout_with_graphopt(g),
     vertex.size = sizes,
     vertex.color = colors,
     vertex.label = labels,
     vertex.label.cex = 0.75,
     vertex.label.color = "black",
     edge.color = "gray85",
     main = "Seattle Light Rail: Centrality Structure")

plot(g,
     layout = layout_in_circle(g),
     vertex.color = "blue",
     vertex.frame.color = "gray",
     vertex.label = V(g)$name,
     vertex.label.cex = 0.7,
     vertex.label.color = "black",
     vertex.size = 8,
     edge.color = "pink",
     edge.width = 1.2,
     main = "Seattle Light Rail: Station-to-Station Connections")

# Layout 2
plot(g,
     layout = layout_with_fr(g),
     vertex.size = sizes,
     vertex.color = colors,
     vertex.label = labels,
     vertex.label.cex = 0.75,
     vertex.label.color = "black",
     edge.color = "white",
     main = "Seattle Light Rail: Force-Directed Layout")

# Layout 3
top10 <- names(sort(deg, decreasing = TRUE))[1:10]
V(g)$color <- ifelse(V(g)$name %in% top10, "pink", "blue")
V(g)$size <- ifelse(V(g)$name %in% top10, 15, 5)
V(g)$label <- ifelse(V(g)$name %in% top10, V(g)$name, NA)

plot(g,
     layout = layout_with_fr(g),
     vertex.color = V(g)$color,
     vertex.size = V(g)$size,
     vertex.label = V(g)$label,
     vertex.label.cex = 0.8,
     vertex.label.color = "black",
     main = "Top 10 Most Central Stations")

Conclusion

This project used network analysis to explore the structure and connectivity of Seattle’s Light Rail system. By visualizing the network and applying centrality measures, I was able to identify the most influential stations in terms of their direct connections to others. Stations like Westlake, University Street, and SeaTac/Airport emerged as key hubs that serve as the backbone of the transit system.

The multiple network layouts helped me view the system from different perspectives, geographic, structural, and centrality-based. The subgraph focusing on the top 10 stations provided a focused visualization of the busiest nodes and their immediate neighbors.

Final R project

Noor Samra

2025-06-07

Introduction

Setup and Data Description

Conclusion