syntethic census from routing to POIs

Author: temospena

pois.qmd

@@ -54,6 +54,65 @@ city_limit = census |> st_union() |> st_as_sf()
 plot(city_limit)
 ```
 
+::: callout-note
+## Note
+
+If you don't have census data at a reasonable level (census blocks), you can recreate random points in your city.
+See the code bellow 🔎
+:::
+
+```{r}
+#| code-fold: true
+#| code-summary: "Generate synthetic census-like points for a City"
+# 0. Load libraries
+library(sf)
+library(dplyr)
+
+# 1. Read city polygon (replace with your file path)
+city_limit <- st_read("data/city_limit.gpkg") |> st_transform(4326)
+
+# 2. Set parameters
+set.seed(42) # for reproducibility
+total_pop <- 545000 # your city population
+avg_residents <- 300 # a census block average
+n_points <- round(total_pop / avg_residents)
+
+# 3. Generate random points within the city boundary
+census_syn <- st_sample(city_limit, size = n_points, type = "random") |>
+  st_as_sf() |>
+  mutate(id = 1:n_points)
+
+# # 4. Compute distance from city center (for density weighting) - OPTIONAL
+# center <- st_centroid(city_limit)
+# dist_center <- as.numeric(st_distance(census_syn, center))  # in meters
+# 
+# # 5. Create weights so closer points get higher population - OPTIONAL
+# # Inverse distance weighting (add +1 to avoid division by zero)
+# weights <- 1 / (dist_center + 1)
+
+# assign random populations that sum to total_pop
+weights <- runif(n_points)
+
+# Normalize weights to sum to 1 and assign residents
+residents <- round(weights / sum(weights) * total_pop)
+
+# 6. Adjust rounding so the total sums exactly to total_pop
+diff <- total_pop - sum(residents)
+if (diff != 0) {
+  residents[1:abs(diff)] <- residents[1:abs(diff)] + sign(diff)
+}
+
+# 7. Add residents to points
+census_syn$residents <- residents
+sum(census_syn$residents) # should be total_pop
+
+# 8. Quick visualization
+mapview::mapview(census_syn, zcol = "residents")
+
+# 9. Save for later
+st_write(census_syn, "data/synthetic_census_points.gpkg", delete_dsn = TRUE)
+```
+
 ## Points of Interest
 
 ### OpenStreetMap

routing.qmd

@@ -45,65 +45,6 @@ city_center$lon = st_coordinates(city_center)[,1]
 city_center$lat = st_coordinates(city_center)[,2]
 ```
 
-::: callout-note
-## Note
-
-If you don't have census data at a reasonable level (census blocks), you can recreate random points in your city.
-See the code bellow 🔎
-:::
-
-```{r}
-#| code-fold: true
-#| code-summary: "Generate synthetic census-like points for a City"
-# 0. Load libraries
-library(sf)
-library(dplyr)
-
-# 1. Read city polygon (replace with your file path)
-city_limit <- st_read("data/city_limit.gpkg") |> st_transform(4326)
-
-# 2. Set parameters
-set.seed(42) # for reproducibility
-total_pop <- 545000 # your city population
-avg_residents <- 300 # a census block average
-n_points <- round(total_pop / avg_residents)
-
-# 3. Generate random points within the city boundary
-census_syn <- st_sample(city_limit, size = n_points, type = "random") |>
-  st_as_sf() |>
-  mutate(id = 1:n_points)
-
-# # 4. Compute distance from city center (for density weighting) - OPTIONAL
-# center <- st_centroid(city_limit)
-# dist_center <- as.numeric(st_distance(census_syn, center))  # in meters
-# 
-# # 5. Create weights so closer points get higher population - OPTIONAL
-# # Inverse distance weighting (add +1 to avoid division by zero)
-# weights <- 1 / (dist_center + 1)
-
-# assign random populations that sum to total_pop
-weights <- runif(n_points)
-
-# Normalize weights to sum to 1 and assign residents
-residents <- round(weights / sum(weights) * total_pop)
-
-# 6. Adjust rounding so the total sums exactly to total_pop
-diff <- total_pop - sum(residents)
-if (diff != 0) {
-  residents[1:abs(diff)] <- residents[1:abs(diff)] + sign(diff)
-}
-
-# 7. Add residents to points
-census_syn$residents <- residents
-sum(census_syn$residents) # should be total_pop
-
-# 8. Quick visualization
-mapview::mapview(census_syn, zcol = "residents")
-
-# 9. Save to GeoPackage
-st_write(census_syn, "data/synthetic_census_points.gpkg", delete_dsn = TRUE)
-```
-
 ## Travel Time Matrix {#ttm}
 
 ```{r}