index.Rmd

---
title: "Climate Emergency open data companion"
lang: "en-GB"
output:
  html_document:
    highlight: null
    mathjax: null
    theme: flatly
    css: styles.css
    self_contained: TRUE
    toc: true
    toc_depth: 2
    toc_float: true
    df_print: paged
    includes:
      in_header: index_head_includes.html
---
<main>
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo=FALSE, message=FALSE, warning=FALSE)
library(tidyverse) ; library(scales) ; library(ggsci) ; library(sf) ; library(ggrepel) ; library(lubridate) ; library(rvest) ; library(packcircles) ; library(treemapify) ; library(ggiraph) ; library(viridis) ; library(leaflet) ; library(leaflet.extras) ; library(htmlwidgets) ; library(htmltools) ; library(classInt)

# ggplot2 theme
theme_x <- function () { 
  theme_minimal(base_size = 14, base_family = "Open Sans") %+replace% 
    theme(
      panel.grid.major.x = element_blank(),
      panel.grid.minor = element_blank(),
      plot.title = element_text(size = 16, face = "bold", hjust = 0),
      plot.subtitle = element_text(hjust = 0, margin = margin(9, 0, 9, 0)),
      plot.caption = element_text(size = 12, color = "grey50", hjust = 1, margin = margin(t = 15)),
      axis.title = element_text(size = 10, hjust = 1),
      axis.text.x = element_text(angle = 90, hjust = 1, margin = margin(t = 0)),
      legend.position = "top", 
      legend.justification = "left"
    )
}

la <- st_read("data/geospatial/trafford_local_authority.geojson")

lsoa <- st_read("data/geospatial/lsoa.geojson") %>%
  filter(area_name == "Trafford") %>%
  st_transform(4326) 
```

Last updated: `r format(Sys.time(), '%d %B %Y')`

**As of February 2020, around 70% of the UK's councils have declared a <a href="https://en.wikipedia.org/wiki/Climate_emergency_declaration" target="_blank">'climate emergency'</a>. This datakit is designed to help councils address this emergency by making open data relating to climate change more discoverable.**

<br/>

```{r}
climate_emergency_councils <- st_read("data/councils/climate_emergency_councils.geojson", quiet = TRUE)

leaflet(height = 600, width = "100%", options = leafletOptions(zoomControl = FALSE)) %>% 
  setView(-3, 54.3, zoom = 6) %>% 
  addTiles(urlTemplate = "",
           attribution = '<a href="https://www.ons.gov.uk/methodology/geography/licences">Contains OS data © Crown copyright and database right (2020)</a> | Data: <a href="https://www.climateemergency.uk" target="_blank">climateemergency.uk</a>') %>%
  addPolygons(data = climate_emergency_councils, 
              fillColor = ifelse(climate_emergency_councils$status == "Declared", "#FFE800","#13B3E5"), 
              fillOpacity = 0.8, weight = 0.1, opacity = 1, color = "#212121",
              label = ~climate_emergency_councils$area_name,
              labelOptions = labelOptions(textsize = "13px", direction = "auto"),
              highlight = highlightOptions(color = "#212121", weight = 1, bringToFront = TRUE)) %>% 
  addFullscreenControl() %>% 
  addResetMapButton() %>% 
  addLegend(title = "UK councils declaring a climate emergency<p style = 'font-size:11px;'>6 February 2020<small>",
            colors = c("#FFE800","#13B3E5"),
            labels = c("Declared", "Undeclared"), opacity = 0.8,
            position = "topright") %>% 
  onRender(
  "function(el, t) {var myMap = this;myMap._container.style['background'] = '#ffffff';}",
        paste0("function(el, x) {$('head').append(","\'<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\'",");}"))
```

<br/>

**A variety of open datasets are presented with metadata and example visualisations. Structured data that has been pre-processed by the <a href="https://www.trafforddatalab.io/" target="_blank">Trafford Data Lab</a> are provided at local authority level where available. In some cases, the <a href="https://cran.r-project.org/" target="_blank">R</a> script used to pre-process the data is given rather than the data.**

**A companion <a href="https://trafforddatalab.shinyapps.io/climate_emergency" target="_blank">application</a> enables UK councils - and their residents - to build a slide pack of data visualisations relating to climate change for their local authority.**

***

## Carbon dioxide emissions
Carbon dioxide emissions broken down by housing, transport and industrial & commercial sectors. These estimates exclude large industrial sites, railways, motorways and land-use because local authorities have no influence on them. Carbon dioxide emissions are also available per capita (<em>kt</em>CO<sub>2</sub>/capita) in the source dataset.

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Data |
|---|---|---|---|---|---|---|
|Carbon dioxide emissions |2005-2021 |Local authority |<a href="https://www.gov.uk/government/collections/uk-local-authority-and-regional-carbon-dioxide-emissions-national-statistics" target="_blank">BEIS, DESNZ</a> | 2024-12-21 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="data/co2_emissions.csv" target="_blank">view</a> |
</div>

```{r fig.retina = 3, fig.cap="Carbon dioxide emissions by main sector in Trafford, 2021."}
co2_emissions <- read_csv("data/co2_emissions.csv") %>% 
  filter(area_name == "Trafford", period == "2021", sector != "LULUCF") %>%
  mutate(percent = round(value/sum(value)*100,0))

layout <- circleProgressiveLayout(co2_emissions, sizecol = "percent")
vertices <- circleLayoutVertices(layout, npoints = 100)

ggplot(data = vertices) + 
  geom_polygon(aes(x, y, group = id, fill = factor(id)), color = "transparent", show.legend = FALSE) +
  geom_text(data = layout, aes(x, y), label = paste0(str_wrap(co2_emissions$sector, width = 15), "\n", co2_emissions$percent, "%"), 
            check_overlap = TRUE, color = "#000000", size = 2) + 
  scale_fill_manual(values = c("#cc1333","#5F4690","#1D6996","#38A6A5","#a1cce6","#ecc30b","#1a68cc")) + 
  labs(title = "Carbon dioxide emissions by main sector",
       subtitle = paste0("Trafford", ", 2021"),
       caption = "Excluding LULUCF\nSource: BEIS, DESNZ") +
  coord_equal() +
  theme_void() +
  theme(plot.title = element_text(size = 16, face = "bold", hjust = 0),
      plot.subtitle = element_text(hjust = 0, margin = margin(9, 0, 9, 0)),
      plot.caption = element_text(size = 12, color = "grey50", hjust = 1, margin = margin(t = 15)))
```

**Additional resources**    
The <a href="http://naei.defra.gov.uk/" target="_blank">National Atmospheric Emissions Inventory</a> (NAEI) website has an <a href="https://naei.beis.gov.uk/laghgapp/" target="_blank">interactive map</a> that allows you to explore CO<sub>2</sub> emissions by local authority area and sector.

***

## Carbon dioxide emissions from large industrial installations
Carbon dioxide emissions from large industrial installations are available across the UK from the <a href="http://naei.defra.gov.uk/" target="_blank">National Atmospheric Emissions Inventory</a>. Other greenhouse gases such as methane and nitrous oxide are also available.

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Data |
|---|---|---|---|---|---|---|
|CO<sub>2</sub> emissions from large industrial installations |2021 |Point locations |<a href="https://naei.beis.gov.uk/data/map-large-source" target="_blank">NAEI</a> | 2023-12-21 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="data/large_point_sources.csv" target="_blank">view</a> |
</div>

```{r}
large_point_sources <- read_csv("data/large_point_sources.csv") %>% 
  mutate(value = round(value,0)) %>% 
  filter(area_name == "Trafford") %>% 
  mutate(popup = str_c("<strong>", site, "</strong><br/>",
                       "<em>Operator: </em>", operator, "<br/>",
                       "<em>Sector: </em>", sector, "<br/>",
                       "<em>CO<sub>2</sub> emissions (2021): </em>", comma(value), " tonnes") %>%
           map(HTML))

leaflet(width = "100%") %>% 
  setView(lng = -2.330256, lat = 53.421829, zoom = 11) %>% 
      addTiles(urlTemplate = "https://cartodb-basemaps-{s}.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png",
               attribution = '&copy; <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a>, <a href="http://cartodb.com/attributions">CartoDB</a> | <a href="https://www.ons.gov.uk/methodology/geography/licences">Contains OS data © Crown copyright and database right (2023)</a> | Data: <a href="https://naei.beis.gov.uk/data/map-large-source" target="_blank">NAEI</a>') %>% 
  addPolygons(data = la, fillOpacity = 0, color = "#212121", weight = 2, opacity = 1) %>% 
  addCircles(data = large_point_sources, lng = ~lon, lat = ~lat, radius = ~sqrt(value), stroke = TRUE, weight = 1, color = "#FFFFFF", fillColor = "#212121", fillOpacity = 0.7, label = ~popup, labelOptions = labelOptions(textsize = "12px", direction = "auto")) %>%
  addControl(paste0("<strong> CO<sub>2</sub> emissions from large industrial installations</strong><br /><em>Trafford, 2021</em>"), position = 'topright')  %>% 
  onRender(paste0("function(el, x) {$('head').append(","\'<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\'",");}"))
```

***

## Industrial and commercial energy consumption
Estimates of total industrial & commercial energy consumption are available for 2005 to 2021.

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Data |
|---|---|---|---|---|---|---|
|Industrial and commercial energy consumption |2021 |Local authority |<a href="https://www.gov.uk/government/collections/total-final-energy-consumption-at-sub-national-level" target="_blank">DESNZ, BEIS</a> | 2023-12-21 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="data/non_domestic_energy_consumption.csv" target="_blank">view</a> |
</div>

```{r fig.retina = 3, fig.cap="Industrial and commercial fuel use in Trafford, 2021."}
industrial_commercial_energy_consumption <- read_csv("data/industrial_commercial_energy_consumption.csv") %>% 
  filter(area_name == "Trafford") %>% 
  mutate(value = round(value,0)) %>% 
  arrange(value) %>% 
  mutate(group = factor(group, levels = group))

ggplot(industrial_commercial_energy_consumption, aes(x = value, y = group)) +
  geom_segment(aes(x = 0, xend = value, y = group, yend = group), 
               linetype = "dotted", colour = "#212121", size = 0.5) + 
  geom_point(colour = "#dd1c77", size = 15) +
  geom_text(aes(label = comma(value, accuracy = 1)), fontface = "bold", colour = "white") +
  labs(x = "Thousands of tonnes of oil equivalent (ktoe)", y = NULL, 
       title = "Industrial and commercial fuel use",
       subtitle = paste0(distinct(industrial_commercial_energy_consumption, area_name), ", 2021"),
       caption = "Source: DESNZ, BEIS",
       fill = NULL) +
  theme_x() +
  theme(aspect.ratio = 0.5,
        panel.grid.major = element_blank(),
        axis.text.x = element_blank(),
        axis.text.y = element_text(hjust = 0))
```

***

## Road transport fuel consumption
Estimates of road transport fuel use by local authority are available for petrol (cars, motorcycles and LGVs) and diesel (cars, LGVs, HGVs and buses) consuming vehicles. Estimates by road type (motorways, A roads and minor roads) are also available in the source dataset.

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Data |
|---|---|---|---|---|---|---|
|Road transport energy consumption |2005-2021 |Local authority |<a href="https://www.gov.uk/government/collections/road-transport-consumption-at-regional-and-local-level" target="_blank">DESNZ, BEIS</a> | 2023-12-21 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="data/road_transport_energy_consumption.csv" target="_blank">view</a> |
</div>

```{r fig.retina = 3, fig.cap="Fuel use by vehicle type in Trafford, 2005-2021."}
road_transport_fuel_consumption <- read_csv("data/road_transport_fuel_consumption.csv") %>% 
  filter(area_name == "Trafford") %>% 
  mutate(group = ordered(group, levels = pull(arrange(filter(., period == "2021-01-01"), value),group)))

ggplot(road_transport_fuel_consumption,
       aes(x = period, y = value, colour = group, group = group)) +
  geom_line(size = 0.8, show.legend = FALSE) +
  geom_hline(yintercept = 0, size = 1, colour = "#212121") +
  scale_y_continuous(position = "right", labels = comma, expand = c(0.005, 0.005)) +
  scale_color_futurama() +
  facet_wrap(~group, nrow = 1) +
  labs(x = NULL, 
       y = "Thousand tonnes of oil equivalent (ktoe)",
       title = "Fuel use by vehicle type",
       subtitle = paste0(distinct(road_transport_fuel_consumption, area_name), ", 2005-2021"),
       caption = "Source: DESNZ, BEIS",
       colour = NULL) +
  theme_x() + 
  theme(strip.text = element_text(size = 9, face = "bold", hjust = 0, vjust = 1)) 
```

***

## Domestic energy consumption
Estimates of total domestic energy consumption area available from 2005 until 2021.

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Data |
|---|---|---|---|---|---|---|
|Domestic energy consumption |2021 |Local authority |<a href="https://www.gov.uk/government/collections/total-final-energy-consumption-at-sub-national-level" target="_blank">DESNZ, BEIS</a> | 2023-12-21 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="data/domestic_energy_consumption.csv" target="_blank">view</a> |
</div>

```{r fig.retina = 3, fig.cap="Domestic fuel use in Trafford, 2021."}
domestic_energy_consumption <- read_csv("data/domestic_energy_consumption.csv") %>% 
  filter(area_name == "Trafford") %>% 
  mutate(value = round(value,0)) %>% 
  arrange(value) %>% 
  mutate(group = factor(group, levels = group))

ggplot(domestic_energy_consumption, aes(x = value, y = group)) +
  geom_segment(aes(x = 0, xend = value, y = group, yend = group), 
               linetype = "dotted", colour = "#212121", size = 0.5) + 
  geom_point(colour = "#DDCC77", size = 15) +
  geom_text(aes(label = comma(value, accuracy = 1)), fontface = "bold", colour = "white") +
  labs(x = "Thousands of tonnes of oil equivalent (ktoe)", y = NULL, 
       title = "Domestic fuel use",
       subtitle = paste0(distinct(domestic_energy_consumption, area_name), ", 2021"),
       caption = "Source: DESNZ, BEIS",
       fill = NULL) +
  theme_x() +
  theme(aspect.ratio = 0.5,
        panel.grid.major = element_blank(),
        axis.text.x = element_blank(),
        axis.text.y = element_text(hjust = 0))
```

***

## Domestic property build period
Information about the age of domestic dwellings is available from the <a href="https://www.gov.uk/government/organisations/valuation-office-agency" target="_blank">Valuation Office Agency</a>. The age of a building influences energy consumption with older properties tending to be less energy efficient than newer buildings.

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Data |
|---|---|---|---|---|---|---|
|Domestic property build period |pre-1900 to 2023 |Local authority |<a href="https://www.gov.uk/government/collections/valuation-office-agency-council-tax-statistics" target="_blank">VOA</a> | 2023-12-21 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="data/domestic_property_build_period.csv" target="_blank">view</a> |
</div>

```{r fig.retina = 3, fig.cap="Domestic property build period in Trafford, pre-1900 to 2023."}
domestic_property_build_period <- read_csv("data/domestic_property_build_period.csv") %>% 
  filter(area_name == "Trafford") %>% 
  mutate(period = as.factor(period),
         period = fct_relevel(period, "pre-1900"))

ggplot(domestic_property_build_period, 
         aes(x = factor(period), y = value)) +
  geom_col(fill = "brown4") +
  geom_hline(yintercept = 0, size = 1, colour = "#212121") +
  scale_y_continuous(labels = comma) +
  labs(x = NULL, 
       y = "Dwellings",
       title = "Build period of domestic dwellings",
       subtitle = paste0(distinct(domestic_property_build_period, area_name), ", Up to March 2023"),
       caption = "Source: Valuation Office Agency")  +
  theme_x() +
  theme(axis.text.x = element_text(angle = 90, hjust = 1))
```

***

## EPCs by Energy Efficiency Rating 
An Energy Performance Certificate's (EPC) Energy Efficiency Rating gives a property an energy efficiency rating from A (most efficient) to G (least efficient) and is valid for 10 years.

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Data |
|---|---|---|---|---|---|---|
|EPCs by Energy Efficiency Rating |2013/Q4 to 2023/Q3 |Local authority |<a href="https://www.gov.uk/government/collections/energy-performance-of-buildings-certificates" target="_blank">MHCLG</a> | 2023-12-21 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="data/domestic_epcs_energy_efficiency.csv" target="_blank">view</a> |
</div>

```{r fig.retina = 3}
domestic_epcs_energy_efficiency <- read_csv("data/domestic_epcs_energy_efficiency.csv") %>%
  filter(area_name == "Trafford") %>% 
  mutate(percent = value / sum(value)) %>% 
  filter(efficiency %in% c("A", "B", "C")) %>% 
  summarise(abc = sum(percent))
```

`r percent(domestic_epcs_energy_efficiency$abc, accuracy = 0.1)` of homes in Trafford are well insulated with an Energy Performance Certificate (EPC) rating of A, B or C.

***

## EPCs by Environmental Impact Rating 
An Energy Performance Certificate's (EPC) Environmental Impact Rating is an estimate of the amount of CO<sub>2</sub> produced by a home. The most energy efficient homes fall within band A (lowest CO<sub>2</sub> emissions) and the least efficient in Band G (highest CO<sub>2</sub> emissions). Lodgements on the Energy Performance of Buildings Register are published on a quarterly basis.

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Data |
|---|---|---|---|---|---|---|
|EPCs by Environmental Impact Rating |2013/Q4 to 2023/Q3 |Local authority |<a href="https://www.gov.uk/government/statistical-data-sets/live-tables-on-energy-performance-of-buildings-certificates" target="_blank">MHCLG</a> | 2024-01-22 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="data/domestic_epcs_environmental_impact.csv" target="_blank">view</a> |
</div>

```{r fig.retina = 3, fig.cap="Cumulative domestic EPCs by Environmental Impact Rating in Trafford, 2008-2019."}
domestic_epcs_environmental_impact <- read_csv("data/domestic_epcs_environmental_impact.csv") %>% 
  filter(area_name == "Trafford") %>% 
  group_by(area_name, impact) %>% 
  summarise(value = sum(value)) %>% 
  mutate(value = value / sum(value))

ggplot(domestic_epcs_environmental_impact, aes(x = fct_rev(factor(impact)), y = value)) +
  geom_col(fill = "#bcbddc") +
  geom_hline(yintercept = 0, size = 1, colour = "#212121") +
  geom_text(aes(x = impact, y = value, label = percent(value, accuracy = 0.1)),
            colour = "#212121", size = 5, hjust = -0.1) +
  scale_y_continuous(expand = c(0.005, 0.005), limit = c(0, 0.4)) +
  labs(x = NULL, y = NULL,
       title = "Cumulative domestic EPCs by Environmental Impact Rating",
       subtitle = paste0(distinct(domestic_epcs_environmental_impact, area_name), ", 2008-2019"),
       caption = "Source: MHCLG") +
  coord_flip() +
  theme_x() +
  theme(panel.grid.major = element_blank(),
        plot.title = element_text(size = 16),
        axis.text.x = element_blank(),
        axis.text.y = element_text(size = 14, face = "bold"))
```

***

## Fuel poverty
A household is considered fuel poor if their income is below the poverty line (taking into account housing costs) and their home has a low energy efficiency.

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Data |
|---|---|---|---|---|---|---|
|Fuel poverty |2021 |England |<a href="https://www.gov.uk/government/statistics/sub-regional-fuel-poverty-data-2019" target="_blank">BEIS</a> | 2023-11-24 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="data/fuel_poverty.csv" target="_blank">view</a> |
</div>

```{r}
fuel_poverty <- left_join(lsoa %>% select(lsoa21cd), read_csv("data/fuel_poverty.csv") %>% 
                            filter(area_name == "Trafford"), by = "lsoa21cd") %>% 
  mutate(popup = str_c("<strong>", lsoa21nm, "</strong><br/>",
                       round(value,1), "%") %>%
           map(HTML))

breaks <- classIntervals(fuel_poverty$value, n = 5, style = "jenks")$brks
pal <- colorBin(palette = "YlGnBu", domain = NULL, bins = breaks, na.color = "#FFFFFF")

leaflet(fuel_poverty, width = "100%") %>% 
  addTiles(urlTemplate = "https://cartodb-basemaps-{s}.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png",
               attribution = '&copy; <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a>, <a href="http://cartodb.com/attributions">CartoDB</a> | <a href="https://www.ons.gov.uk/methodology/geography/licences">Contains OS data © Crown copyright and database right (2024)</a> | Data: <a href="https://www.gov.uk/government/statistics/sub-regional-fuel-poverty-data-2019" target="_blank">BEIS</a>') %>% 
  addPolygons(fillColor = ~pal(value), fillOpacity = 0.8, smoothFactor = 0.5, 
              stroke = TRUE, weight = 1, color = "#212121", opacity = 1, 
              label = ~popup, 
              labelOptions = labelOptions(style = list("font-weight" = "normal", padding = "3px 8px"), textsize = "12px", direction = "auto"),
              highlightOptions = highlightOptions(color = "white", weight = 2, bringToFront = TRUE)) %>% 
  addLegend(pal = pal, values = ~value, opacity = 0.7, title = NULL, position = "bottomright",
            labFormat = labelFormat(suffix = "%")) %>% 
  addControl(paste0("<strong>Proportion of households in fuel poverty</strong><br /><em>Trafford's LSOAs, 2021</em>"), position = 'topright') %>% 
  onRender(paste0("function(el, x) {$('head').append(","\'<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\'",");}"))
```

**Additional resources**    
Detailed fuel poverty data at a national level (England) are available from <a href="https://www.gov.uk/government/statistics/fuel-poverty-detailed-tables-2023-2022-data" target="_blank">BEIS</a>.

***

## Flood risk from rivers and/or the sea
The chance of flooding from rivers and/or the sea is available at 50m by 50m resolution. Each grid cell is allocated to one of four flood risk categories, taking into account flood defences and their condition. The risk of flooding from surface water is available from <a href="https://data.gov.uk/dataset/d5ca01ec-e535-4d3f-adc0-089b4f03687d/risk-of-flooding-from-surface-water-suitability" target="_blank">data.gov.uk</a>.

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Code |
|---|---|---|---|---|---|---|
|Flood risk |2019-12-20 (quarterly updates) |UK |<a href="https://data.gov.uk/dataset/risk-of-flooding-from-rivers-and-sea1" target="_blank">Environment Agency</a> | 2019-06-12 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="code/flood_risk.R" target="_blank">view</a> |
</div>

```{r fig.retina = 3, fig.cap="Risk of flooding from rivers and sea in Trafford, 2019-12-20."}
flood_risk <- st_read("data/flood_risk.geojson", quiet = TRUE)

ggplot() + 
  geom_sf(data = la, fill = "#E7E7E7", alpha = 1, colour = "NA") +
  geom_sf(data = flood_risk, aes(fill = prob_4band), colour = "NA") +
  scale_fill_manual(values = c("#eff3ff", "#bdd7e7", "#6baed6", "#2171b5"), 
                    labels = c("Very Low", "Low", "Medium", "High")) +
  labs(x = NULL, y = NULL,
       title = "Risk of Flooding from Rivers and Sea",
       subtitle = "Trafford, 2019-12-20",
       caption = "Contains Ordnance Survey data © Crown copyright and database right 2020\nSource: Environment Agency",
       fill = NULL) +
  guides(fill = guide_legend(reverse = TRUE)) +
  coord_sf(datum = NA) +
  theme_x() +
  theme(plot.caption = element_text(size = 8, hjust = 0),
        legend.position = "right",
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 8)) 
```

**Additional resources**    
The <a href="https://www.gov.uk/government/organisations/environment-agency" target="_blank">Environment Agency</a> has an <a href="https://flood-warning-information.service.gov.uk/long-term-flood-risk/map" target="_blank">interactive map</a> showing flood risk information.

***

## Air Quality Management Areas
<a href="https://uk-air.defra.gov.uk/aqma/" target="_blank">Air Quality Management Areas</a> (AQMA) are declared when a local authority expects that air quality will not meet the <a href="https://uk-air.defra.gov.uk/assets/documents/National_air_quality_objectives.pdf" target="_blank" aria-label="National Air Quality Objectives (PDF)">National Air Quality Objectives</a>.

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Code |
|---|---|---|---|---|---|---|
|Air Quality Management Areas |2020-07 |UK |<a href="https://uk-air.defra.gov.uk/aqma/maps" target="_blank">DEFRA</a> |2020-07 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="code/aqma.R" target="_blank">view</a> |
</div>

```{r fig.retina = 3, fig.cap="Air Quality Management Area in Trafford, 2019."}
aqma <- st_read("data/aqma.geojson", quiet = TRUE)

ggplot() + 
  geom_sf(data = la, fill = "#E7E7E7", alpha = 1, colour = "NA") +
  geom_sf(data = aqma, aes(fill = prob_4band), fill = "#727C81", colour = "NA") +
  labs(x = NULL, y = NULL,
       title = "Air Quality Management Area",
       subtitle = "Trafford, 2020",
       caption = "Contains Ordnance Survey data © Crown copyright and database right 2020\nSource: DEFRA") +
  guides(fill = guide_legend(reverse = TRUE)) +
  coord_sf(datum = NA) +
  theme_x() +
  theme(plot.caption = element_text(size = 8, hjust = 0),
        legend.position = "right",
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 8)) 
```

**Additional resources**    
<a href="https://www.gov.uk/government/organisations/department-for-environment-food-rural-affairs" target="_blank">DEFRA</a> has an <a href="https://uk-air.defra.gov.uk/aqma/maps" target="_blank">AQMAs interactive map</a>.

***

## Background air pollution
Estimates for annual mean background concentrations of nitrogen oxide (NOx), nitrogen dioxide (NO<sub>2</sub>), coarse (PM<sub>10</sub>) and fine particulate matter (PM<sub>2.5</sub>) are available at 1km x 1km resolution. 

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Data |
|---|---|---|---|---|---|---|
|Background air pollution |20 |1 km x 1 km grid square centroids |<a href="https://uk-air.defra.gov.uk/data/pcm-data" target="_blank">DEFRA</a> | 2022 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="data/background_air_pollution.shp" target="_blank">view</a>  |
</div>

```{r fig.retina = 3, fig.cap="Modelled background PM2.5 concentrations in Trafford, 2022"}
background_air_pollution <- st_read("data/background_air_pollution.shp", quiet = TRUE) %>% 
  filter(area_name == "Trafford")

ggplot(data = background_air_pollution, 
       aes(fill = pm252022g, color = pm252022g)) +
  geom_sf(data = la, fill = NA, color = "#212121", size = 0.5) +
  geom_sf(color = NA, alpha = 0.8) +
  scale_fill_viridis_c(option = "magma", direction = -1,
                       guide = guide_colorbar(
                         barheight = unit(2, units = "mm"),
                         barwidth = unit(50, units = "mm"),
                         title.position = "left",
                         title.vjust = 1,
                         label.hjust = 0.5
                       )) +
  labs(title = expression(bold(paste("Modelled background ", PM[2.5], " concentrations"))),
       subtitle = "Trafford, 2022",
       caption = "Contains Ordnance Survey data © Crown copyright and database right 2024\nSource: DEFRA",
       fill = "Annual mean (μg/m3)") +
  coord_sf(datum = NA) +
  theme_x() +
  theme(plot.title = element_text(size = 16),
        plot.caption = element_text(size = 8, hjust = 0),
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 8)) 
```

**Additional resources**    
The <a href="https://uk-air.defra.gov.uk/data/gis-mapping" target="_blank">UK Ambient Air Quality Interactive Map</a> visualises background concentrations of air pollution.

***

## Roadside air pollution
There are several sources of continuous air quality monitoring data including: <a href="https://uk-air.defra.gov.uk/data/" target="_blank">DEFRA UK-AIR</a>; <a href="https://www.airqualityengland.co.uk/" target="_blank">Air Quality England</a>; <a href="http://www.scottishairquality.scot/" target="_blank">Air Quality in Scotland</a>; <a href="https://airquality.gov.wales/" target="_blank">Air Quality in Wales</a>; <a href="https://www.airqualityni.co.uk/" target="_blank">Northern Ireland Air</a> and <a href="https://openaq.org/#/locations?countries=GB" target="_blank">OpenAQ</a>. The legal limits for NO<sub>2</sub>, PM<sub>10</sub> and PM<sub>2.5</sub> concentrations are available from the <a href="https://ec.europa.eu/environment/air/quality/standards.htm" target="_blank">European Commission Air Quality Standards</a> page.

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Code |
|---|---|---|---|---|---|---|
|Roadside air pollution |Continuous |Monitoring station |<a href="https://www.airqualityengland.co.uk/" target="_blank">Ricardo Energy & Environment</a> | 2024-01-22 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="code/roadside_air_pollution.R" target="_blank">view</a> |
</div>
 
```{r fig.retina = 3, fig.cap="1-hour mean NO2 concentrations at Trafford A56 monitoring station during 2023."}
roadside_air_pollution <- read_csv("data/roadside_air_pollution.csv")

ggplot(roadside_air_pollution, aes(x = date_hour, y = value)) +
  geom_line(colour = "#CC6677", size = 0.3) +
  geom_hline(aes(yintercept = 200), linetype = "dotted", color = "#000000", size = 0.8) +
  geom_hline(yintercept = 0, size = 1, colour = "#212121") +
  scale_x_datetime(breaks = roadside_air_pollution$date_hour, date_labels = "%b-%y", date_breaks = "3 months") +
  scale_y_continuous(limits = c(0, 220), expand = c(0.005, 0.005)) +
  labs(title = expression(bold(paste("1-hour mean ", NO[2], " concentrations"))),
       subtitle = "Trafford A56 monitoring station, 2023",
       caption = "Source: Trafford Council / Ricardo EE",
       x = "",
       y = expression(paste("μg/m"^3))) +
  theme_x()
```

***

## Renewable electricity generation 
Data on renewable electricity installations, their capacity (MW) and generation (MWh).

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Data |
|---|---|---|---|---|---|---|
|Renewable electricity generation |2014-2022 |Local authority |<a href="https://www.gov.uk/government/statistics/regional-renewable-statistics" target="_blank">BEIS</a> | 2024-01-22 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="data/renewable_electricity_generation.csv" target="_blank">view</a> |
</div>

```{r fig.retina = 3, fig.cap="Renewable electricity generation by source in Trafford, 2022."}
renewable_electricity_generation <- read_csv("data/renewable_electricity_generation.csv") %>% 
  filter(area_name == "Trafford") %>%
  mutate(info = str_c(group, ": ",  comma(value))) 

ggplot(renewable_electricity_generation %>% filter(value != 0),
         aes(area = value, fill = fct_reorder(group, value, .desc = TRUE), subgroup = group, label = info)) +
  geom_treemap(colour = "#212121") +
  geom_treemap_text(colour = "#FFFFFF", place = "bottomleft", reflow = TRUE, 
                    padding.x = grid::unit(1.5, "mm"),
                    padding.y = grid::unit(2, "mm"),
                    size = 14) +
  scale_fill_simpsons() +
  labs(x = NULL, y = NULL, 
       title = "Renewable electricity generation by source",
       subtitle = paste0(distinct(renewable_electricity_generation, area_name), ", 2022"),
       caption = paste0(paste0(renewable_electricity_generation %>% filter(is.na(value)) %>% pull(group),collapse = ", "), " generation\n was suppressed due to disclosure control.\nSource: BEIS"),
       fill = "MWh") +
  theme_x() +
  theme(legend.position = "right") 
```

***

## Onshore wind
The location of operational onshore (and offshore wind) turbines is recorded on the Renewable Energy Planning Database (REPD).

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Data |
|---|---|---|---|---|---|---|
|Onshore wind |2023-10 |Point |<a href="https://www.gov.uk/government/publications/renewable-energy-planning-database-monthly-extract" target="_blank">BEIS</a> | 2023-01-22 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="data/onshore_wind.csv" target="_blank">view</a> |
</div>

```{r}
onshore_wind <- read_csv("data/onshore_wind.csv") %>% 
  mutate(operator = iconv(operator, from = "latin1", to = "UTF-8"),
         popup = str_c("<strong>", name, "</strong><br/>",
                       "<em>Local authority: </em>", area_name, "<br/>",
                       "<em>Turbines: </em>", turbines, "<br/>",
                       "<em>Capacity: </em>", capacity, " MW") %>%
           map(HTML))

leaflet(width = "100%") %>% 
  setView(-2.28417866956407, 53.5151885751656, zoom = 9) %>% 
  addTiles(urlTemplate = "https://cartodb-basemaps-{s}.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png",
           attribution = '&copy; <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a>, <a href="http://cartodb.com/attributions">CartoDB</a> | <a href="https://www.ons.gov.uk/methodology/geography/licences">Contains OS data © Crown copyright and database right (2020)</a> | Data: <a href="https://www.gov.uk/government/publications/renewable-energy-planning-database-monthly-extract" target="_blank">BEIS</a>') %>% 
  addMarkers(data = onshore_wind, icon = makeIcon(iconUrl = "img/wind_turbine.png", iconWidth = 30, iconHeight = 30), label = ~popup, labelOptions = labelOptions(textsize = "12px", direction = "auto")) %>% 
  addFullscreenControl() %>% 
  addControl(paste0("<strong>Onshore wind turbines</strong>"), position = 'topright')  %>% 
  onRender(paste0("function(el, x) {$('head').append(","\'<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\'",");}"))
```

***

## Non-domestic Renewable Heat Incentive
The <a href="https://www.ofgem.gov.uk/environmental-programmes/non-domestic-rhi" target="_blank">Non-domestic Renewable Heat Incentive</a> is a UK Government scheme launched in November 2011 to encourage industry, businesses and public sector organisations to switch to renewable heating technologies. Participants are paid a tariff per unit of energy produced as recorded by an on-site meter over 20 years.

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Data |
|---|---|---|---|---|---|---|
|Non-domestic Renewable Heat Incentive |2011-11 to 2023-03 |Local authority |<a href="https://www.gov.uk/government/collections/renewable-heat-incentive-statistics" target="_blank">Ofgem</a> | 2024-01-22 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="data/non-domestic_rhi.csv" target="_blank">view</a> |
</div>

There have been `r pull(filter(read_csv("data/non-domestic_rhi.csv"),area_name == "Trafford"), value)` non-domestic Renewable Heat Incentive applications accredited in Trafford since the scheme was launched in 2011.

***

## Domestic Renewable Heat Incentive
The <a href="https://www.ofgem.gov.uk/environmental-programmes/domestic-rhi" target="_blank">Domestic Renewable Heat Incentive</a> is a UK Government scheme launched in April 2014 that encourages households to switch from gas and oil-fired boilers to low carbon heating technologies like heat pumps, biomass boilers and solar thermal panels. Financial support is linked to the production of renewable heat with payments received over seven years.

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Data |
|---|---|---|---|---|---|---|
|Domestic Renewable Heat Incentive |2014-04 to 2023-03 |Local authority |<a href="https://www.gov.uk/government/collections/renewable-heat-incentive-statistics" target="_blank">Ofgem</a> | 2024-01-22 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="data/domestic_rhi.csv" target="_blank">view</a> |
</div>

A total of `r pull(filter(read_csv("data/domestic_rhi.csv"),area_name == "Trafford"), value)` applications have been accredited for payment in Trafford between April 2014 and March 2023.

***

## Recycling
The percentage of household waste sent for reuse, recycling or composting. This local authority level measure is used as the basis of UK reporting for the <a href="https://ec.europa.eu/environment/waste/framework" target="_blank">EU Waste Framework Directive</a> which specifies a target to recycle, reuse or compost 50% of the UK's household waste by December 2020.

Data for England, Wales and Northern Ireland are available by financial year and by calendar year in Scotland. 

|Indicator |Period |Geography |Source |Updated |Licence |Data |
|---|---|---|---|---|---|---|
|Recycling |2015/16 - 2021/22 |Local authority |<a href="https://www.gov.uk/government/statistical-data-sets/env18-local-authority-collected-waste-annual-results-tables" target="_blank">Defra</a>; <a href="https://statswales.gov.wales/Catalogue/Environment-and-Countryside/Waste-Management/Local-Authority-Municipal-Waste" target="_blank">StatsWales</a>; <a href="https://www.sepa.org.uk/environment/waste/waste-data/waste-data-reporting/household-waste-data" target="_blank">SEPA</a>; <a href="https://www.daera-ni.gov.uk/publications/northern-ireland-local-authority-collected-municipal-waste-management-statistics-time-series-data" target="_blank">DAERA</a> | 2023-12-06 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="data/recycling.csv" target="_blank">view</a> |

```{r fig.retina = 3, fig.cap="Re-use, recycling or composting of household waste in Trafford, 2015/16 - 2021/22."}
recycling <- read_csv("data/recycling.csv") %>% 
  filter(area_name == "Trafford")

ggplot(recycling, aes(x = period, y = value, group = 1)) +
  geom_line(colour = "#117733", size = 1) +
  geom_hline(yintercept = 0, size = 1, colour = "#212121") +
  scale_y_continuous(position = "right", labels = function(x) paste0(x, "%")) +
  labs(x = NULL, y = NULL,
       title = "Reuse, recycling or composting of household waste",
       subtitle = paste0(distinct(recycling, area_name), ", 2015/16 - 2021/22"),
       caption = "Source: DEFRA") +
  theme_x() +
  theme(plot.title = element_text(size = 18))
```

*** 

## Ultra low emission vehicles
Ultra low emission vehicles (ULEVs) emit less than 75g of CO<sub>2</sub> per km and typically refer to battery electric, plug-in hybrid electric and fuel cell electric vehicles.

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Data |
|---|---|---|---|---|---|---|
|Ultra low emission vehicles |2011-2022 |Local authority |<a href="https://www.gov.uk/government/statistical-data-sets/all-vehicles-veh01" target="_blank">Department for Transport </a> | 2023-12-13 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="data/ulev.csv" target="_blank">view</a> |
</div>

```{r fig.retina = 3, fig.cap="ULEVs as a proportion of all licensed vehicles in Trafford, 2011-2022."}
ulev <- read_csv("data/ulev.csv") %>% 
  filter(area_name == "Trafford") %>% 
  spread(indicator, value) %>%
  drop_na() %>% 
  mutate(percent = `Ultra low emission vehicles`/`All licensed vehicles`)

ggplot(ulev, aes(x = period, y = percent)) +
  geom_col(fill = "#ffa600") +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  scale_y_continuous(labels = percent_format(accuracy = 0.1)) +
  scale_x_continuous(breaks=seq(2011,2022, by = 1)) +
  labs(x = NULL, y = NULL, 
       title = "ULEVs as a proportion of all licensed vehicles",
       subtitle = paste0(distinct(ulev, area_name), ", 2011-2022"),
       caption = "Source: DfT and DVLA") +
  theme_x() +
  theme(axis.title.y = element_text(hjust = 0))
```

***

## Electric cars and vans
The number of licensed electric cars and vans (battery electric, plug-in hybrid electric, or range-extended electric) registered by the keeper's local authority is available by calendar year.

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Data |
|---|---|---|---|---|---|---|
|Licensed electric vehicles |2011-2022 |Local authority |<a href="https://www.gov.uk/government/statistical-data-sets/all-vehicles-veh01" target="_blank">Department for Transport </a> | 2023-12-13 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="data/ev.csv" target="_blank">view</a> |
</div>

```{r fig.retina = 3, fig.cap="EVs as a proportion of all licensed cars and vans in Trafford, 2011-2018."}
ev <- read_csv("data/ev.csv") %>% 
  filter(area_name == "Trafford") %>% 
  spread(indicator, value) %>%
  drop_na() %>% 
  mutate(percent = `Electric cars and vans`/`All cars and vans`)

ggplot(ev, aes(x = period, y = percent)) +
  geom_col(fill = "#332288") +
  geom_hline(yintercept = 0, size = 1, colour = "#333333") +
  scale_y_continuous(labels = percent_format(accuracy = 0.1)) +
  scale_x_continuous(breaks=seq(2011,2022, by = 1)) +
  labs(x = NULL, y = NULL, 
       title = "EVs as a proportion of all licensed cars and vans",
       subtitle = paste0(distinct(ev, area_name), ", 2011-2022"),
       caption = "Source: DfT and DVLA") +
  theme_x() +
  theme(axis.title.y = element_text(hjust = 0))
```

***

## Electric vehicle charging locations
Infomation about electric car charging points are available from <a href="https://www.openchargemap.io" target="_blank">Open Charge Map</a>. The data are crowd-sourced so the accuracy of the data cannot be verified. 
 	
<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Code |
|---|---|---|---|---|---|---|
|Electric vehicle charging locations |Continuous  |Point |<a href="https://www.openchargemap.io/site/about" target="_blank">Open Charge Map</a> |2024-01-22 |<a href="https://creativecommons.org/licenses/by-sa/4.0/" target="_blank">Creative Commons Attribution-ShareAlike 4.0 International</a> |<a href="code/electric_vehicle_charging_locations.R" target="_blank">view</a> |
</div>

```{r fig.retina = 3}
electric_vehicle_charging_locations <- read_csv("data/electric_vehicle_charging_locations.csv") %>% 
  mutate(site = str_replace_all(name, "'", ""),
         popup = str_c("<strong>", name, "</strong><br/>",
                       "<em>Operator: </em>", operator, "<br/>",
                       "<em>Points: </em>", points) %>%
           map(HTML))

leaflet(width = "100%") %>% 
  setView(lng = -2.330256, lat = 53.421829, zoom = 11) %>% 
  addTiles(urlTemplate = "https://cartodb-basemaps-{s}.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png",
           attribution = '&copy; <a href="http://www.openstreetmap.org/copyright">OpenStreetMap</a>, <a href="http://cartodb.com/attributions">CartoDB</a> | <a href="https://www.ons.gov.uk/methodology/geography/licences">Contains OS data © Crown copyright and database right (2020)</a> | Data: <a href="https://map.openchargemap.io" target="_blank">Open Charge Map</a>') %>% 
  addPolygons(data = la, fillOpacity = 0, color = "#212121", weight = 2, opacity = 1) %>% 
  addMarkers(data = electric_vehicle_charging_locations, icon = makeIcon(iconUrl = "img/ev_charging_location.png", iconWidth = 20, iconHeight = 20), label = ~popup, labelOptions = labelOptions(textsize = "12px", direction = "auto")) %>% 
  addControl(paste0("<strong>Electric vehicle charging locations</strong><br /><em>Trafford, January 2024</em>"), position = 'topright')  %>% 
  onRender(paste0("function(el, x) {$('head').append(","\'<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\'",");}"))
```

**Additional resources**    
<a href="https://map.openchargemap.io/#/search" target="_blank">Open Charge Map</a> allows you to explore electric vehicle charging locations across the UK.

***

## Cycling
Local authority cycling rates are published by the Department of Transport from the <a href="https://www.sportengland.org/research/active-lives-survey/" target="_blank">Active Lives Survey</a>.

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Data |
|---|---|---|---|---|---|---|
|Cycling |2021-11 - 2022-11 |Local authority |<a href="https://www.gov.uk/government/statistical-data-sets/walking-and-cycling-statistics-cw" target="_blank">Department for Transport</a> | 2019-07-31 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="data/cycling.csv" target="_blank">view</a> |
</div>

The Active Lives Survey estimate that in 2022 the proportion of adults who cycled at least once a week was `r round(pull(filter(read_csv("data/cycling.csv"),area_name == "Trafford", frequency == "At least once per week"), value), 1)`%. Other cycling frequencies are shown below:

|How often? |% |
|:---|:-----|
|At least once a week |`r round(pull(filter(read_csv("data/cycling.csv"),area_name == "Trafford", frequency == "At least once per week"), value), 1)` |
|At least once per month |`r round(pull(filter(read_csv("data/cycling.csv"),area_name == "Trafford", frequency == "At least once per month"), value), 1)` |

***

## Walking

Local authority walking rates are published by the Department of Transport from the <a href="https://www.sportengland.org/research/active-lives-survey/" target="_blank">Active Lives Survey</a>.

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Data |
|---|---|---|---|---|---|---|
|Walking |2021-11 - 2022-11 |Local authority |<a href="https://www.gov.uk/government/statistical-data-sets/walking-and-cycling-statistics-cw" target="_blank">Department for Transport</a> | 2019-07-31 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="data/walking.csv" target="_blank">view</a> |
</div>

The Active Lives Survey estimate that in 2022 the proportion of adults who walked at least once a week was `r round(pull(filter(read_csv("data/walking.csv"),area_name == "Trafford", frequency == "Once per week"), value), 1)`%. Other walking frequencies are shown below:

|How often? |% |
|:---|:-----|
|At least five times a week |`r round(pull(filter(read_csv("data/walking.csv"),area_name == "Trafford", frequency == "At least 5 times per week"), value), 1)` |
|At least three times a week |`r round(pull(filter(read_csv("data/walking.csv"),area_name == "Trafford", frequency == "At least 3 times per week"), value), 1)` |
|At least once a week |`r round(pull(filter(read_csv("data/walking.csv"),area_name == "Trafford", frequency == "At least once per week"), value), 1)` |
|At least once per month |`r round(pull(filter(read_csv("data/walking.csv"),area_name == "Trafford", frequency == "At least once per month"), value), 1)` |

***

## Green belt
The percentage of land in each local authority area that is designated as Green Belt land by the local planning authority is published annually. Some local authorities are not listed in the dataset because they do not have any designated Green Belt land.

Please note: Greenwich is shown as ‘-’ in the raw data because it has less than five hectares of designated Green Belt land. This has been dropped from the processed data because it is not a numeric value.

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Data |
|---|---|---|---|---|---|---|
|Green belt |2023-03-31  |England |<a href="https://www.gov.uk/government/collections/green-belt-statistics" target="_blank">Ministry of Housing, Communities & Local Government</a> |2023-12-15 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="data/green_belt.csv" target="_blank">view</a> |
</div>

The extent of the designated Green Belt in Trafford at 31 March 2023 was estimated at `r comma(pull(read_csv("data/green_belt.csv") %>% filter(area_name == "Trafford", indicator == "Green Belt area"), value))` hectares, or approximately  `r round(pull(read_csv("data/green_belt.csv") %>% filter(area_name == "Trafford", indicator == "Green Belt area"), value)/pull(read_csv("data/green_belt.csv") %>% filter(area_name == "Trafford", indicator == "Total area"), value)*100, 1)`% of the land area of Trafford.     

**Additional resources**    
Green Belt polygon boundaries for local authorities (2023) can be directly downloaded from <a href="https://www.planning.data.gov.uk/dataset/green-belt" target="_blank">data.gov.uk</a>.

***

## Green spaces
Parks, playing fields, gardens and other green spaces that are likely to be publicly accessible are available in the <a href="https://www.ordnancesurvey.co.uk" target="_blank">Ordnance Survey's</a> OS Open Greenspace product.

<div class = "metadata">
|Indicator |Period |Geography |Source |Updated |Licence |Code |
|---|---|---|---|---|---|---|
|Green spaces |2019-04  |Great Britain |<a href="https://www.ordnancesurvey.co.uk/business-and-government/products/os-open-greenspace.html" target="_blank">Ordnance Survey</a> |2019-0 |<a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/" target="_blank">OGL v3.0</a> |<a href="code/greenspaces.R" target="_blank">view</a> |
</div>

```{r fig.retina = 3, fig.cap="Green spaces in Trafford, 2019"}
greenspaces <- st_read("data/greenspaces.geojson", quiet = TRUE)

ggplot() + 
  geom_sf(data = la, fill = "#E7E7E7", alpha = 1, colour = "NA") +
  geom_sf(data = greenspaces, aes(fill = site_type), colour = "NA") +
  scale_fill_manual(values = c("#66c2a5", "#fc8d62", "#8da0cb", "#e78ac3", "#a6d854", "#ffd92f", "#e5c494"), 
                    labels = c("Allotments Or Community Growing Spaces",
                               "Golf Course", "Play Space", "Playing Field",
                               "Public Park Or Garden", 
                               "Religious Ground and Cemeteries", "Sports")) +
  labs(x = NULL, y = NULL,
       title = "Green spaces",
       subtitle = "Trafford, 2019",
       caption = "Contains Ordnance Survey data © Crown copyright and database right 2019",
       fill = NULL) +
  coord_sf(datum = NA) +
  theme_x() +
  theme(plot.caption = element_text(size = 8),
        legend.position = "right",
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 8)) 
```

**Additional resources**    
The <a href="http://naei.defra.gov.uk/" target="_blank">Ordnance Survey's</a> <a href="https://osmaps.ordnancesurvey.co.uk" target="_blank">interactive OS Maps tool</a> allows you to explore green spaces in the UK interactively.


<br /><br /><br /><br />
<a rel="license" href="http://creativecommons.org/licenses/by-nc/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc/4.0/88x31.png" /><br />All content is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.</a> <a href="https://github.com/traffordDataLab/climate_emergency" target="_blank" aria-label="Visit our climate emergency repo on GitHub">
<span class="fa fa-github" style="font-size:24px; float:right; margin-top: -3px;"></span><span class="hideContent">GitHub</span>
</a></div></main>
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