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[Robinlovelace]

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[ccamara]

Thanks for this, too, @Robinlovelace !

[Robinlovelace]

Thanks @ccamara an idea place for any questions about the book + reproducible examples, hope it will be of use+interest to supplement Discord and other communication places.

[rafaelmaikel]

[Robinlovelace]

Hi @rafaelmaikel thanks for sharing the code. Could you share some example input datasets? Also, it will make it way easier if you format the code in your issue, see details here: https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax

[rafaelmaikel]

Hi Robin, I will send you the datasets that I used, they are public and can be also found here "https://transparencia.metrosp.com.br/dataset/pesquisa-origem-e-destino/resource/4362eaa3-c0aa-410a-a32b-37355c091075"
Matriz_Tab30_OD-2017_METRO_2024-07.xlsx

Code

# Function to load necessary packages
load_package <- function(package) {
  if (!requireNamespace(package, quietly = TRUE)) {
    install.packages(package)
  }
  library(package, character.only = TRUE)
}

# Load necessary packages
packages <- c("foreign", "haven", "sf", "dplyr", "ggplot2", "od", "readxl", "tidyr")
lapply(packages, load_package)

# File paths
sav_path <- "C:/Users/Administrador/Documents/Trabalho/OD-2017-v1_METRO_2024-07 (3)/OD-2017-v1_METRO_2024-07 (2)/OD-2017-v1_METRO_2024-07.sav"
shp_zona_path <- "C:/Users/Administrador/Documents/Trabalho/OD-2017-v1_METRO_2024-07 (3)/Zonas-OD-2017_METRO_2024-07/Zonas-OD-2017_METRO_2024-07.shp"
xlsx_path <- "C:/Users/Administrador/Documents/Trabalho/Matriz_Tab30_OD-2017_METRO_2024-07.xlsx"

# Read files
sav_data <- read_sav(sav_path)
zonas_od <- st_read(shp_zona_path)
od_matrix <- readxl::read_xlsx(xlsx_path)

# Define and apply CRS
utm_crs <- 32723
zonas_od <- st_transform(zonas_od, crs = utm_crs)

# Simplify geometry by removing the third dimension
zonas_od <- st_zm(zonas_od, drop = TRUE, what = "ZM")

# Check and repair geometries if necessary
zonas_od <- st_make_valid(zonas_od)

# Obtain and extract coordinates of the centroids
centroids <- st_centroid(zonas_od)
coords <- st_coordinates(centroids)

# Define origin and destination zones for filtering
origins <- c(121, 122, 123, 124, 125)
destinations <- c(121, 122, 123, 124, 125, 126, 127, 116, 109, 110, 142, 141, 140, 139)

# Filter the OD matrix to include only the desired origins and destinations
filtered_od_matrix <- od_matrix %>%
  filter(.[[1]] %in% origins) %>%  # Filter rows (origins)
  select(c(1, which(names(od_matrix) %in% destinations)))  # Filter columns (destinations)

# Convert the filtered OD matrix to long format
od_long <- filtered_od_matrix %>%
  pivot_longer(
    cols = -1,  # All columns except the first
    names_to = "dest",
    values_to = "flow"
  ) %>%
  rename(orig = 1) %>%
  filter(flow > 0)  # Filter only OD pairs with positive flow

# Create OD pairs from the coordinates
p <- cbind(
  coords[match(od_long$orig, zonas_od$NumeroZona), ], 
  coords[match(od_long$dest, zonas_od$NumeroZona), ]
)

# Check if the coordinates were found correctly
if (nrow(p) > 0) {
  od_pairs <- coords_to_od(p, interzone_only = TRUE)  # Check the coords_to_od function
  
  # Convert OD pairs into geographic lines
  od_lines <- odc_to_sf(od_pairs)  # Check the odc_to_sf function
  
  # Add real flows
  od_lines$num_viagens <- od_long$flow
  
  # Visualize with ggplot2
  ggplot() +
    geom_sf(data = zonas_od, fill = "lightgrey", color = "black") +
    geom_sf(data = od_lines, aes(color = num_viagens), size = 2) +
    scale_color_continuous(name = "Passenger Flow") +
    labs(title = "Passenger Flow Between Zones",
         x = "Longitude", y = "Latitude") +
    theme_minimal()
} else {
  print("No OD pairs were found with the specified coordinates.")
}

Discussion

I need a more advanced analysis where we will join the origin points within the multipolygon and analyze their relationship with some destinations. The origins we are interested in are in zones 121, 122, 123, 124, and 125, while the destinations include 120, 121, 122, 123, 124, 125, 126, 127, 116, 109, 110, 143, 142, 141, 140, and 139. It's crucial to be able to identify which zone we're dealing with in zonas_od$NumeroZona.

Could you please review the code and suggest improvements or point out any potential issues, particularly with handling the spatial data and filtering the origin-destination pairs?

[rafaelmaikel]

GitHub does not support shp and sav files, can I send you elsewhere?

[Robinlovelace]

You could upload as a .zip

[rafaelmaikel]

OD-2017-v1_METRO_2024-07 (3).zip