Simplify major roads feature

aws-s3/scripts-ccao-data-raw-us-east-1/spatial-environment-major_road.R

@@ -0,0 +1,44 @@
+library(aws.s3)
+library(dplyr)
+library(geoarrow)
+library(osmdata)
+library(sf)
+source("utils.R")
+
+# This script queries OpenStreetMap for major roads in Cook County and
+# saves them as a spatial parquet
+AWS_S3_RAW_BUCKET <- Sys.getenv("AWS_S3_RAW_BUCKET")
+output_bucket <- file.path(AWS_S3_RAW_BUCKET, "spatial", "environment")
+current_year <- strftime(Sys.Date(), "%Y")
+
+##### Major roads #####
+# Query OpenStreetMap API for major roads in Cook
+# Create a sequence of years from 2014 to the current year
+years <- 2014:current_year
+
+# Iterate over the years
+for (year in years) {
+  remote_file <- file.path(
+    output_bucket, "major_road",
+    paste0("year=", year),
+    paste0("major_road-", year, ".parquet")
+  )
+
+  if (!aws.s3::object_exists(remote_file)) {
+    # Update the datetime in the opq function
+    osm_roads <- opq(bbox = "Cook County, IL",
+                     datetime = paste0(year, "-01-01T00:00:00Z"),
+                     timeout = 900) %>%
+      add_osm_feature(
+        key = "highway",
+        value = c("motorway", "trunk", "primary")
+      ) %>%
+      osmdata_sf() %>%
+      .$osm_lines %>%
+      select(osm_id, name) %>%
+      st_transform(4326) %>%
+      mutate(geometry_3435 = st_transform(geometry, 3435))
+
+    geoarrow::write_geoparquet(osm_roads, remote_file)
+  }
+}

aws-s3/scripts-ccao-data-warehouse-us-east-1/spatial-environment-major_road.R

@@ -1,38 +1,75 @@
+library(arrow)
 library(aws.s3)
 library(dplyr)
 library(geoarrow)
-library(glue)
-library(noctua)
 library(osmdata)
-library(purrr)
 library(sf)
 source("utils.R")
 
-# This script queries OpenStreetMap for major roads in Cook County and
-# saves them as a spatial parquet
+# This script is designed to ingest spatial data on major roads for each year
+# from 2014 to the present, simplify it for efficiency, and store a
+# deduplicated, aggregated version of this data in a warehouse bucket.
+#
+# We take an additive approach here to ensure distance to these roads is
+# consistent from earlier pin-level data. If there are new major roads in 2015
+# data, they will be added to existing 2014 major roads data, and that addition
+# will become our 2015 major roads data. If there are identical osm_id
+# observations between 2014 and 2015, we preserve the data from 2014.
+
+# Instantiate S3 bucket names
+AWS_S3_RAW_BUCKET <- Sys.getenv("AWS_S3_RAW_BUCKET")
 AWS_S3_WAREHOUSE_BUCKET <- Sys.getenv("AWS_S3_WAREHOUSE_BUCKET")
-output_bucket <- file.path(AWS_S3_WAREHOUSE_BUCKET, "spatial", "environment")
-current_year <- strftime(Sys.Date(), "%Y")
-
-##### Major roads #####
-# Query OpenStreetMap API for major roads in Cook
-remote_file <- file.path(
-  output_bucket, "major_road",
-  paste0("year=", current_year),
-  paste0("major_road-", current_year, ".parquet")
-)
-
-if (!aws.s3::object_exists(remote_file)) {
-  osm_roads <- opq(bbox = "Cook County, IL") %>%
-    add_osm_feature(
-      key = "highway",
-      value = c("motorway", "trunk", "primary")
-    ) %>%
-    osmdata_sf() %>%
-    .$osm_lines %>%
-    select(osm_id, name) %>%
-    st_transform(4326) %>%
-    mutate(geometry_3435 = st_transform(geometry, 3435))
-
-  geoarrow::write_geoparquet(osm_roads, remote_file)
+
+# Set up variables for iteration through years
+current_year <- as.integer(strftime(Sys.Date(), "%Y"))
+years <- 2014:current_year
+master_dataset <- NULL
+
+# Iterate over the years
+for (year in years) {
+  # Ingest path
+  ingest_file <- file.path(
+    AWS_S3_RAW_BUCKET, "spatial",
+    "environment", "major_road",
+    paste0("year=", year),
+    paste0("major_road-", year, ".parquet"))
+
+  # Simplify linestrings
+  current_data <- read_geoparquet_sf(ingest_file) %>%
+    mutate(geometry_3435 = st_simplify(geometry_3435, dTolerance = 10))
+
+  # Initiate master data set with first available year, add column for de-duping
+  if (is.null(master_dataset)) {
+    master_dataset <- current_data %>%
+      mutate(temporal = 0)
+
+    data_to_write <- current_data
+  } else {
+    # Create temporal column to preserve earliest data
+    combined_data <- bind_rows(master_dataset,
+                               current_data %>% mutate(temporal = 1))
+
+    # Arrange by osm_id and temporal, then deduplicate and preserve earlier data
+    data_to_write <- combined_data %>%
+      arrange(osm_id, temporal) %>%
+      group_by(osm_id) %>%
+      slice(1) %>%
+      ungroup() %>%
+      select(-temporal)
+
+    # Reset temporal tag for the next iteration
+    master_dataset <- data_to_write %>%
+      mutate(temporal = 0)
+  }
+
+  # Define the output file path for the data to write
+  output_file <- file.path(
+    AWS_S3_WAREHOUSE_BUCKET, "spatial",
+    "environment", "major_road",
+    paste0("year=", year),
+    paste0("major_road-", year, ".parquet")
+  )
+
+  geoarrow::write_geoparquet(data_to_write, output_file)
+
 }

dbt/models/spatial/docs.md

@@ -215,6 +215,11 @@ Major roads sourced from OpenStreetMap (OSM).
 Major roads include any OSM ways tagged with
 `highway/motorway`, `highway/trunk`, or `highway/primary`
 
+This data covers major roads data from 2014 onwards and uses an additive
+approach for data integration. Starting from 2014, each year's data builds 
+upon the previous year, with new major road additions being added to the 
+existing dataset.
+
 **Geometry:** `MULTILINESTRING`
 {% enddocs %}