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1-make_exposure.Rmd
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---
title: "Walking & cycling exposure in Walthamstow: 2013 v. 2021"
output:
html_document:
toc_float: true
toc: true
toc_depth: 4
collapsed: false
knit: (function(inputFile, encoding) {
rmarkdown::render(inputFile, encoding = encoding, output_dir = "../docs", output_file ="exposure.html") })
---
this document descibes the process of calcualting walking and cycling exposure for a number of typical routes within the London Borough of Waltham Forest.
### Preparation
Load libraries
```{r setup}
rm(list = ls())
## Load libraries
library('knitr')
library("googleway")
library("gepaf")
library("sp")
library('raster')
library('ggplot2')
library('geosphere')
library('sf')
library('reshape2')
library('leaflet')
library('mapview')
library('kableExtra')
## Script doesn't run in Rstudio using the 'Knit' button, but if put the below in the console it should do.
## rmarkdown::render('markdown_scripts/1-make_exposure.Rmd', output_dir = "../docs", output_file ="exposure.html")
```
### Data import
Import routes
```{r import_routes}
exposure_routes <- read.csv('../csv_inputs/exposure_journeys.csv',
stringsAsFactors = F)
exposure_routes[exposure_routes$via_array == '','via_array'] <- NA
```
### Import colours
```{r import_laei_colours}
source('https://raw.githubusercontent.com/KCL-ERG/colour_schemes/master/2013/pm25_laei2013_colours_breaks.R')
source('https://raw.githubusercontent.com/KCL-ERG/colour_schemes/master/2013/no2_laei2013_colours_breaks.R')
source('https://raw.githubusercontent.com/KCL-ERG/colour_schemes/master/2013/pm10_laei2013_colours_breaks.R')
```
### Route calculation, offest, and exposure calculation
Make a loop to calculate the routes and extract the concentrations. If seggregated==true, then the route is offset by the variable 'offset'
```{r start_loop, message=F, warning=F}
start_time <- Sys.time() + 1000
via_array <- NA
for (i in 1:nrow(exposure_routes)) {
# Journey parameters
start_lat <- exposure_routes[i,]$start_lat
start_lon <- exposure_routes[i,]$start_lon
if (!is.na(exposure_routes[i,]$via_array)) {via_array <- as.list(strsplit(as.character(exposure_routes[i,]$via_array), ";")[[1]]) } else {via_array <- NA}
end_lat <- exposure_routes[i,]$end_lat
end_lon <- exposure_routes[i,]$end_lon
mode <- as.character(exposure_routes[i,]$mode)
seggregated <- exposure_routes[i,]$seggregated
## take account of where in the road the journey is taking place
if (mode == 'walk' & seggregated == 'no') {offset <- 6}
if (mode == 'walk' & seggregated == 'yes') {offset <- 8}
if (mode == 'bicycle' & seggregated == 'no') {offset <- 4}
if (mode == 'bicycle' & seggregated == 'yes') {offset <- 6}
# EPSG strings we might need
latlong <- "+init=epsg:4326"
ukgrid <- "+init=epsg:27700"
google_projected <- "+init=epsg:3857 +proj=merc +a=6378137 +b=6378137 +lat_ts=0.0 +lon_0=0.0 +x_0=0.0 +y_0=0 +k=1.0 +units=m +nadgrids=@null +no_defs"
# Take the start/end data and get a route from Google
if ('walk' %in% mode) { source("../routing_functions/walking.R") }
if ('bicycle' %in% mode) { source("../routing_functions/cycling.R") }
rm(start_lat, start_lon, end_lat, end_lon)
if ('walk' %in% mode) {
result <- walk_result
rm(walk_result)
duration <- walk_duration
rm(walk_duration)
} else {
result <- cycle_result
rm(cycle_result)
duration <- cycle_duration
rm(cycle_duration)
}
##### In here is where we calculate the offset for where the journey is taking place compared to the centre of the road
##### https://stackoverflow.com/questions/50275195/draw-a-parallel-line-in-r-offset-from-a-line
#### Only want to do this if seggregated == yes
coordinates(result) <- ~lon + lat
proj4string(result) = CRS(latlong)
result <- spTransform(result, ukgrid)
segment.shift <- function(x, y, d){
#
# calculate vector
v <- c(x[2] - x[1],y[2] - y[1])
#
# normalize vector
v <- v/sqrt((v[1]**2 + v[2]**2))
#
# perpendicular unit vector
vnp <- c( -v[2], v[1] )
return(list(x = c( x[1] + d*vnp[1], x[2] + d*vnp[1]),
y = c( y[1] + d*vnp[2], y[2] + d*vnp[2])))
}
x <- result@coords[,1]
y <- result@coords[,2]
xn <- numeric( (length(x) - 1) * 2 )
yn <- numeric( (length(y) - 1) * 2 )
for ( p in 1:(length(x) - 1) ) {
xs <- c(x[p], x[p+1])
ys <- c(y[p], y[p+1])
new.s <- segment.shift( xs, ys, offset)
xn[(p-1)*2+1] <- new.s$x[1] ; xn[(p-1)*2+2] <- new.s$x[2]
yn[(p-1)*2+1] <- new.s$y[1] ; yn[(p-1)*2+2] <- new.s$y[2]
}
new_result <- data.frame(x = xn,
y = yn)
coordinates(new_result) <- ~x+y
proj4string(new_result) = CRS(ukgrid)
new_result <- spTransform(new_result, latlong)
new_result <- data.frame(new_result)
new_result$id <- unique(result$id)
new_result$line <- unique(result$line)
new_result$mode <- unique(result$mode)
result <- new_result
rm(new_result, new.s, offset, x, xn, xs, y, yn, ys, segment.shift)
names(result)[1:2] <- c('lon', 'lat')
##### End of the help from stackoverflow
coordinates(result) <- ~lon + lat
proj4string(result) = CRS(latlong)
result <- spTransform(result, ukgrid)
## This makes a spatialline from the points for the result
x <- lapply(split(result, result$id), function(x) Lines(list(Line(coordinates(x))), result$id[1L]))
lines <- SpatialLines(x)
data <- data.frame(id = unique(result$id))
rownames(data) <- data$id
result <- SpatialLinesDataFrame(lines, data)
proj4string(result) = CRS(ukgrid)
rm(x, lines, data)
## How many minutes long was the bicycle and walk journeys
timeslots <- data.frame(id = unique(result$id), time = seq.POSIXt(start_time, start_time + duration, "min"))
## Now want to split the line into a point per minute
result <- st_as_sf(result)
result$id <- exposure_routes[i,]$journey_id
if (i == 1) {line_result <- result} else {line_result <- rbind(line_result, result)}
result <- st_line_sample(result, n = nrow(timeslots), type = 'regular')
result <- suppressWarnings(st_set_crs(result, 27700))
result <- data.frame(as(result, 'Spatial'))
## Now some harmonisation stuff to get the car, walk and bus all the same format so can join them together
result$id <- exposure_routes[i,]$journey_id
result$mode <- mode
result$line <- NA
result <- cbind(result, timeslots)
names(result) <- c('easting', 'northing', 'id', 'mode', 'line', 'id_2', 'time')
result <- result[,c('id', 'mode', 'line', 'time', 'easting', 'northing')]
result$year <- exposure_routes[i,]$year
coordinates(result) <- ~easting + northing
proj4string(result) <- CRS(ukgrid)
# Now get our pollutant files
## 2013 concentration maps
if (exposure_routes[i,]$year == 2013) {
wf_2013_no2 <- raster('../air_quality_inputs/no2_2013.asc')
crs(wf_2013_no2) <- CRS(ukgrid)
wf_2013_nox <- raster('../air_quality_inputs/nox_2013.asc')
crs(wf_2013_nox) <- CRS(ukgrid)
wf_2013_pm10 <- raster('../air_quality_inputs/pm10_2013.asc')
crs(wf_2013_pm10) <- CRS(ukgrid)
wf_2013_pm25 <- raster('../air_quality_inputs/pm25_2013.asc')
crs(wf_2013_pm25) <- CRS(ukgrid)
# Extract concentrations
no2 <- result
no2@data$pollutant <- 'no2'
no2@data$concentration <- extract(wf_2013_no2, no2@coords, method = 'bilinear')
pm25 <- result
pm25@data$pollutant <- 'pm25'
pm25@data$concentration <- extract(wf_2013_pm25, pm25@coords, method = 'bilinear')
pm10 <- result
pm10@data$pollutant <- 'pm10'
pm10@data$concentration <- extract(wf_2013_pm10, pm10@coords, method = 'bilinear')
nox <- result
nox@data$pollutant <- 'nox'
nox@data$concentration <- extract(wf_2013_nox, nox@coords, method = 'bilinear')
result <- rbind.SpatialPointsDataFrame(no2, pm25, nox, pm10)
rm(wf_2013_no2, wf_2013_nox, wf_2013_pm10, wf_2013_pm25,nox, no2, pm25, pm10)
}
## 2020 concentration maps
if (exposure_routes[i,]$year == 2020) {
wf_2020_no2 <- raster('../air_quality_inputs/no2_2020.asc')
crs(wf_2020_no2) <- CRS(ukgrid)
wf_2020_nox <- raster('../air_quality_inputs/nox_2020.asc')
crs(wf_2020_nox) <- CRS(ukgrid)
wf_2020_pm10 <- raster('../air_quality_inputs/pm10_2020.asc')
crs(wf_2020_pm10) <- CRS(ukgrid)
wf_2020_pm25 <- raster('../air_quality_inputs/pm25_2020.asc')
crs(wf_2020_pm25) <- CRS(ukgrid)
# Extract concentrations
no2 <- result
no2@data$pollutant <- 'no2'
no2@data$concentration <- extract(wf_2020_no2, no2@coords, method = 'bilinear')
pm25 <- result
pm25@data$pollutant <- 'pm25'
pm25@data$concentration <- extract(wf_2020_pm25, pm25@coords, method = 'bilinear')
pm10 <- result
pm10@data$pollutant <- 'pm10'
pm10@data$concentration <- extract(wf_2020_pm10, pm10@coords, method = 'bilinear')
nox <- result
nox@data$pollutant <- 'nox'
nox@data$concentration <- extract(wf_2020_nox, nox@coords, method = 'bilinear')
result <- rbind.SpatialPointsDataFrame(no2, pm25, nox, pm10)
rm(wf_2020_no2, wf_2020_nox, wf_2020_pm10, wf_2020_pm25,nox, no2, pm25, pm10)
}
if (i == 1) {final_result <- result} else {final_result <- rbind.SpatialPointsDataFrame(final_result, result)}
rm(timeslots, duration, result, via_array)
}
```
Bind the results we've made together with the original parameter data, and delete some variables that aren't needed anymore
```{r bind_results}
final_result$time <- NULL
final_result$line <- NULL
final_result <- merge(final_result, exposure_routes[,c('journey_id', 'name', 'seggregated')], by.x = 'id', by.y = 'journey_id')
rm(i, google_projected, latlong, mode, seggregated, ukgrid)
```
### Results
The summary of the routes and years are shown.
```{r aggregate_results}
summary_results <- aggregate(concentration ~ name + mode + year + pollutant + seggregated, data=final_result, FUN=mean)
names(summary_results) <- c('Route', 'Mode', 'Year', 'Pollutant', 'Seggregated', 'Mean Exposure (ug/m3)')
summary_results[order(summary_results$Route, summary_results$Pollutant, summary_results$Year),] %>% kable(row.names = F) %>% kable_styling()
```
```{r make_points_to_sf}
final_result <- st_as_sf(final_result)
```
```{r add_lines_to_summary}
summary_results <- merge(summary_results, exposure_routes[,c('journey_id', 'name')], by.x = 'Route', by.y = 'name')
summary_results <- merge(summary_results, line_result, by.x = 'journey_id', by.y ='id')
summary_results <- st_sf(summary_results[,-c(8)],st_sfc(summary_results$geometry))
```
Now make some plots of the results
```{r make_plots}
plot <- list()
for (i in 1:length(unique(final_result$name))) {
route_name <- unique(final_result$name)[i]
plot[[i]] <- ggplot(final_result[final_result$name == route_name & final_result$pollutant %in% c('no2', 'pm25'),],
aes(x = as.factor(year), y = concentration, fill = seggregated)) +
geom_boxplot() +
facet_wrap(name~pollutant, ncol = 4, scales = 'free_y') +
xlab('Year') +
theme(plot.subtitle = element_text(vjust = 1),
plot.caption = element_text(vjust = 1),
panel.grid.major = element_line(colour = "gray92"),
panel.grid.minor = element_line(colour = "gray92"),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14, colour = "black"),
axis.text.y = element_text(size = 14),
plot.title = element_text(size = 14),
legend.title = element_text(size = 13),
panel.background = element_rect(fill = NA,
colour = "black", linetype = "solid"),
plot.background = element_rect(colour = NA),
strip.text.x = element_text(size = 12),
strip.background = element_blank())
}
```
Example 1 - Chingford to Leyton (Concentration plots)
```{r chingford_to_leyon_concs}
plot[[1]]
```
Example 1 - Chingford to Leyton (Spatial plots)
```{r chingford_to_leyton_spatial}
final_result <- st_set_crs(final_result, 27700)
final_result <- st_transform(final_result, 4326)
example_2013 <- final_result[final_result$pollutant == 'no2' & final_result$name == 'Chingford to Leyton' & final_result$year == 2013,]
example_2020 <- final_result[final_result$pollutant == 'no2' & final_result$name == 'Chingford to Leyton' & final_result$year == 2020 & final_result$seggregated == 'yes',]
qpal <- colorQuantile(no2_laei2013_colours, c(example_2013$concentration, example_2020$concentration), n = 16)
qpal <- colorNumeric(
palette = no2_laei2013_colours,
domain = c(example_2013$concentration, example_2020$concentration))
leaflet(options = leafletOptions(attributionControl=F))%>%
addProviderTiles(providers$Esri.WorldTopoMap)%>%
addFeatures(example_2013,
group = '2013',
fillColor = qpal(example_2013$concentration),
color="transparent",
fillOpacity = 0.7,
popup = paste0(as.character(round(example_2013$concentration,0)),' ?g/m<sup>3</sup>')) %>%
addFeatures(example_2020,
group = '2020',
fillColor = qpal(example_2020$concentration),
color="transparent",
fillOpacity = 0.7,
popup = paste0(as.character(round(example_2020$concentration,0)), ' ?g/m<sup>3</sup>')) %>%
addLayersControl(baseGroups = c('2013', '2020')) %>%
addLegend(pal=qpal,values=c(example_2013$concentration, example_2020$concentration),title="NO2 (?g/m<sup>3</sup>)") %>%
addControl('<strong>Exposure to NO<sub>2</sub> while cycling between Chingford <br> & Leyton has dropped by 22% between 2013<br>and 2020 due to improvements in air<br> quality, and the introduction of seggregated <br> cycle lanes.</strong>', position = "topleft")
```
Now make a static output of the same.
```{r static_chingford_to_leyton}
no2_2013_chingford_leyton <- leaflet(options = leafletOptions(attributionControl=F)) %>%
addProviderTiles(providers$Esri.WorldTopoMap)%>%
addFeatures(example_2013,
group = '2013',
fillColor = qpal(example_2013$concentration),
color="transparent",
fillOpacity = 0.9,
popup = paste0(as.character(round(example_2013$concentration,0)),' ?g/m<sup>3</sup>')) %>%
addLegend(pal=qpal,values=c(example_2013$concentration, example_2020$concentration),title="NO2 (µg/m<sup>3</sup>)")
mapshot(no2_2013_chingford_leyton, file = "../map_outputs/no2_2013_chingford_leyton.png",
remove_controls = c("zoomControl", "layersControl", "homeButton", "scaleBar"))
no2_2020_chingford_leyton <- leaflet(options = leafletOptions(attributionControl=F)) %>%
addProviderTiles(providers$Esri.WorldTopoMap)%>%
addFeatures(example_2020,
group = '2020',
fillColor = qpal(example_2020$concentration),
color="transparent",
fillOpacity = 0.9,
popup = paste0(as.character(round(example_2020$concentration,0)),' ?g/m<sup>3</sup>')) %>%
addLegend(pal=qpal,values=c(example_2013$concentration, example_2020$concentration),title="NO2 (µg/m<sup>3</sup>)")
mapshot(no2_2020_chingford_leyton, file = "../map_outputs/no2_2020_chingford_leyton.png",
remove_controls = c("zoomControl", "layersControl", "homeButton", "scaleBar"))
```
Example 2 - Leyton to Blackhorse (Spatial plots)
```{r leyton_to_blackhorse_spatial}
example_2013 <- final_result[final_result$pollutant == 'no2' & final_result$name == 'Leyton to Blackhorse' & final_result$year == 2013,]
example_2020 <- final_result[final_result$pollutant == 'no2' & final_result$name == 'Leyton to Blackhorse' & final_result$year == 2020,]
qpal <- colorQuantile(no2_laei2013_colours, c(example_2013$concentration, example_2020$concentration), n = 16)
qpal <- colorNumeric(
palette = no2_laei2013_colours,
domain = c(example_2013$concentration, example_2020$concentration))
leaflet(options = leafletOptions(attributionControl=F))%>%
addProviderTiles(providers$Esri.WorldTopoMap)%>%
addFeatures(example_2013,
group = '2013',
fillColor = qpal(example_2013$concentration),
color="transparent",
fillOpacity = 0.7,
popup = paste0(as.character(round(example_2013$concentration,0)),' µg/m<sup>3</sup>')) %>%
addFeatures(example_2020,
group = '2020',
fillColor = qpal(example_2020$concentration),
color="transparent",
fillOpacity = 0.7,
popup = paste0(as.character(round(example_2020$concentration,0)), ' µg/m<sup>3</sup>')) %>%
addLayersControl(baseGroups = c('2013', '2020')) %>%
addLegend(pal=qpal,values=c(example_2013$concentration, example_2020$concentration),title="NO2 (µg/m<sup>3</sup>)") %>%
addControl('<strong>Exposure to NO<sub>2</sub> while cycling between Leyton <br> & Blackhorse has dropped by 21% between 2013<br>and 2020 due to improvements in air<br> quality, and the introduction of seggregated <br> cycle lanes.</strong>', position = "bottomleft")
```
Now make a static output of the same.
```{r static_leyton_to_blackhorse}
no2_2013_leyton_blackhorse <- leaflet(options = leafletOptions(attributionControl=F)) %>%
addProviderTiles(providers$Esri.WorldTopoMap)%>%
addFeatures(example_2013,
group = '2013',
fillColor = qpal(example_2013$concentration),
color="transparent",
fillOpacity = 0.9,
popup = paste0(as.character(round(example_2013$concentration,0)),' ?g/m<sup>3</sup>')) %>%
addLegend(pal=qpal,values=c(example_2013$concentration, example_2020$concentration),title="NO2 (µg/m<sup>3</sup>)")
mapshot(no2_2013_leyton_blackhorse, file = "../map_outputs/no2_2013_leyton_blackhorse.png",
remove_controls = c("zoomControl", "layersControl", "homeButton", "scaleBar"))
no2_2020_leyton_blackhorse <- leaflet(options = leafletOptions(attributionControl=F)) %>%
addProviderTiles(providers$Esri.WorldTopoMap)%>%
addFeatures(example_2020,
group = '2020',
fillColor = qpal(example_2020$concentration),
color="transparent",
fillOpacity = 0.9,
popup = paste0(as.character(round(example_2020$concentration,0)),' ?g/m<sup>3</sup>')) %>%
addLegend(pal=qpal,values=c(example_2013$concentration, example_2020$concentration),title="NO2 (µg/m<sup>3</sup>)")
mapshot(no2_2020_leyton_blackhorse, file = "../map_outputs/no2_2020_leyton_blackhorse.png",
remove_controls = c("zoomControl", "layersControl", "homeButton", "scaleBar"))
```
PM2.5 from Chingford to Leyton
```{r pm25_leyton_to_blackhorse}
example_2013 <- final_result[final_result$pollutant == 'pm25' & final_result$name == 'Leyton to Blackhorse' & final_result$year == 2013,]
example_2020 <- final_result[final_result$pollutant == 'pm25' & final_result$name == 'Leyton to Blackhorse' & final_result$year == 2020,]
qpal <- colorQuantile(pm25_laei2013_colours, c(example_2013$concentration, example_2020$concentration), n = 11)
qpal <- colorNumeric(
palette = pm25_laei2013_colours,
domain = c(example_2013$concentration, example_2020$concentration))
pm25_2013_chingford_leyton <- leaflet(options = leafletOptions(attributionControl=F)) %>%
addProviderTiles(providers$Esri.WorldTopoMap)%>%
addFeatures(example_2013,
group = '2013',
fillColor = qpal(example_2013$concentration),
color="transparent",
fillOpacity = 0.9,
popup = paste0(as.character(round(example_2013$concentration,0)),' µg/m<sup>3</sup>')) %>%
addLegend(pal=qpal,values=c(example_2013$concentration, example_2020$concentration),title="PM2.5 (µg/m<sup>3</sup>)")
mapshot(pm25_2013_chingford_leyton, file = "../map_outputs/pm25_2013_leyton_blackhorse.png",
remove_controls = c("zoomControl", "layersControl", "homeButton", "scaleBar"))
pm25_2020_chingford_leyton <- leaflet(options = leafletOptions(attributionControl=F)) %>%
addProviderTiles(providers$Esri.WorldTopoMap)%>%
addFeatures(example_2020,
group = '2020',
fillColor = qpal(example_2020$concentration),
color="transparent",
fillOpacity = 0.9,
popup = paste0(as.character(round(example_2020$concentration,0)),' µg/m<sup>3</sup>')) %>%
addLegend(pal=qpal,values=c(example_2013$concentration, example_2020$concentration),title="PM2.5 (µg/m<sup>3</sup>)")
mapshot(pm25_2020_chingford_leyton, file = "../map_outputs/pm25_2020_leyton_blackhorse.png",
remove_controls = c("zoomControl", "layersControl", "homeButton", "scaleBar"))
```
All routes
```{r map_of_routes}
line_result <- st_set_crs(line_result, 27700)
line_result <- st_transform(line_result, 4326)
leaflet(options = leafletOptions(attributionControl=F))%>%
#addProviderTiles(providers$Esri.WorldTopoMap)%>%
#addProviderTiles(providers$Esri.NatGeoWorldMap) %>%
addTiles() %>%
addFeatures(line_result[line_result$id %in% exposure_routes[exposure_routes$mode == 'bicycle',]$journey_id,],
group = 'Bike Routes',
color = 'red',
popup = as.character(exposure_routes[exposure_routes$journey_id %in% line_result[line_result$id %in% exposure_routes[exposure_routes$mode == 'bicycle',]$journey_id,]$id,]$name),
opacity = 0.3) %>%
addFeatures(line_result[line_result$id %in% exposure_routes[exposure_routes$mode == 'walk',]$journey_id,],
group = 'Walk Routes',
color = 'blue',
popup = as.character(exposure_routes[exposure_routes$journey_id %in% line_result[line_result$id %in% exposure_routes[exposure_routes$mode == 'walk',]$journey_id,]$id,]$name),
opacity = 0.3) %>%
addLayersControl(baseGroups = c('Bike Routes', 'Walk Routes'))
```
Chingford Station to Leyton Station
```{r static_map_of_chingford_to_leyton}
temp_static <- leaflet(options = leafletOptions(attributionControl=F)) %>%
addProviderTiles(providers$Esri.WorldTopoMap)%>%
addFeatures(line_result[line_result$id == 1,])
mapshot(temp_static, file = "../map_outputs/route_chingford_leyton.png",
remove_controls = c("zoomControl", "layersControl", "homeButton", "scaleBar"))
```
```{r write_out_result}
st_write(final_result, '../geojson_outputs/exposure_routes.geojson', delete_dsn=TRUE)
```
[Result available]('../geojson_outputs/exposure_routes.geojson')