---
title: "Average Walk Time Analysis"
---
Analysis of walking times to nearest schools across the ACT.
```{r setup}
#| echo: false
#| message: false
#| warning: false
library(sf)
library(leaflet)
library(dplyr)
library(ggplot2)
library(plotly)
library(htmltools)
# Load walk time data
walk_2001 <- st_read(here::here("data", "walk_analysis", "school_walk_times_2001.gpkg"), quiet = TRUE)
walk_2024 <- st_read(here::here("data", "walk_analysis", "school_walk_times_2024.gpkg"), quiet = TRUE)
primary_2001 <- walk_2001 |> filter(school_type == "primary")
primary_2024 <- walk_2024 |> filter(school_type == "primary")
high_2001 <- walk_2001 |> filter(school_type == "high")
high_2024 <- walk_2024 |> filter(school_type == "high")
```
```{r}
#| echo: false
# Create KPI callout function
create_kpi_callout <- function(title, value, subtitle, color = "#2166AC") {
div(
style = paste0("background-color: ", color, "; color: white; padding: 20px;
border-radius: 8px; text-align: center; margin: 10px;"),
h3(title, style = "margin: 0; font-size: 1.2em;"),
h1(paste0(value, " mins"), style = "margin: 5px 0; font-size: 2.5em; font-weight: bold;"),
p(subtitle, style = "margin: 0; font-size: 0.9em; opacity: 0.9;")
)
}
# Get specific stats for KPIs
primary_2001_avg <- round(sum(primary_2001$mean_walk_time_min *
primary_2001$population) / sum(primary_2001$population), digits = 1)
primary_2024_avg <- round(sum(primary_2024$mean_walk_time_min *
primary_2024$population) / sum(primary_2024$population), digits = 1)
high_2001_avg <- round(sum(high_2001$mean_walk_time_min *
high_2001$population) / sum(high_2001$population), digits = 1)
high_2024_avg <- round(sum(high_2024$mean_walk_time_min *
high_2024$population) / sum(high_2024$population), digits = 1)
```
Walk times were computed by placing 10 random locations in each sa2, and then calculating the average walk time to the closest primary or high school. Then the populations was evenly weighted to each of the random locations in the SA2 and a weighted average walk time was estimated.
# Primary Schools
## Walk Time KPIs
::: {.columns}
::: {.column width="50%"}
```{r}
#| echo: false
# Create 2001 Primary KPI
create_kpi_callout(
title = "Primary Schools 2001",
value = primary_2001_avg,
subtitle = "Population-weighted average walk time",
color = "#1e3a8a"
)
```
:::
::: {.column width="50%"}
```{r}
#| echo: false
# Calculate change for color coding
primary_change <- primary_2024_avg - primary_2001_avg
change_color <- if(primary_change > 0) "#dc2626" else "#16a34a" # Red if worse, green if better
# Create 2024 Primary KPI with change info
create_kpi_callout(
title = "Primary Schools 2024",
value = primary_2024_avg,
subtitle = paste0("Change: ", ifelse(primary_change >= 0, "+", ""), round(primary_change, 1), " mins vs 2001"),
color = change_color
)
```
:::
:::
## Primary School Walk Time Maps
### 2024
```{r}
create_walk_time_map <- function(data, year, school_type) {
# Calculate color palette - using plasma reversed so shorter times are yellow/bright
pal <- colorNumeric("plasma", data$mean_walk_time_min, na.color = "#cccccc", reverse = TRUE)
leaflet(data) |>
addProviderTiles(providers$CartoDB.Positron) |>
addPolygons(
fillColor = ~pal(mean_walk_time_min),
fillOpacity = ~ifelse(population == 0, 0, 0.7), # Transparent if no population
color = "#444444",
weight = 1,
label = ~paste0(
sa2_name, "<br>",
"Walk Time: ", round(mean_walk_time_min, 1), " mins<br>",
"Population (5-14): ", population, "<br>",
"Year: ", year
) |> lapply(htmltools::HTML),
labelOptions = labelOptions(sticky = TRUE)
) |>
addLegend(
pal = pal,
values = ~mean_walk_time_min,
title = paste("Walk Time (mins) <br>", school_type, year),
position = "bottomright"
) |>
setView(lng = 149.12, lat = -35.30, zoom = 10)
}
create_walk_time_map(primary_2024, 2024, "Primary")
```
### 2001
```{r}
#| echo: false
create_walk_time_map(primary_2001, 2001, "Primary")
```
# High Schools
## Walk Time KPIs
::: {.columns}
::: {.column width="50%"}
```{r}
#| echo: false
# Create 2001 Primary KPI
create_kpi_callout(
title = "High Schools 2001",
value = high_2001_avg,
subtitle = "Population-weighted average walk time",
color = "#1e3a8a"
)
```
:::
::: {.column width="50%"}
```{r}
#| echo: false
# Calculate change for color coding
high_change <- high_2024_avg - high_2001_avg
change_color <- if(high_change > 0) "#dc2626" else "#16a34a" # Red if worse, green if better
# Create 2024 high KPI with change info
create_kpi_callout(
title = "Primary Schools 2024",
value = high_2024_avg,
subtitle = paste0("Change: ", ifelse(high_change >= 0, "+", ""), round(high_change, 1), " mins vs 2001"),
color = change_color
)
```
:::
:::
## High School Walk Time Maps
### 2024
```{r}
create_walk_time_map(high_2024, 2024, "High")
```
### 2001
```{r}
#| echo: false
create_walk_time_map(high_2001, 2001, "High")
```