/
vector-and-raster-rnets.R
187 lines (136 loc) · 7.86 KB
/
vector-and-raster-rnets.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
library(dplyr)
library(sf)
library(ggplot2)
library(pct)
library(raster)
library(mapview)
###This gets LSOA data (there is no MSOA route network data available for schools)
rnet_commute = pct::get_pct_rnet(region = "west-yorkshire",geography = "lsoa") %>% sf::st_transform(27700)
rnet_schools = pct::get_pct_rnet(region = "west-yorkshire", purpose = "school",geography = "lsoa") %>% sf::st_transform(27700)
schools = sf::read_sf("https://github.com/npct/pct-outputs-national/raw/master/school/lsoa/d_all.geojson") %>% sf::st_transform(27700)
wyorks = c("Leeds", "Bradford", "Kirklees", "Wakefield", "Calderdale")
schools = schools %>% filter(lad_name %in% wyorks)
z = pct::get_pct_zones("west-yorkshire", geography = "lsoa")
breaks = c(-1, 10, 50, 100, 500, 1000, 5000)
colors = sf::sf.colors(n = length(breaks) - 1)
rnet_commute$color = cut(x = rnet_commute$dutch_slc, breaks = breaks, labels = colors)
rnet_commute$color = as.character(rnet_commute$color)
# summary(rnet_schools$dutch_slc)
rnet_schools$color = cut(x = rnet_schools$dutch_slc, breaks = breaks, labels = colors)
rnet_schools$color = as.character(rnet_schools$color)
sec_schools = filter(schools, phase == "Secondary")
#
# plot(rnet_commute$geometry, col = rnet_commute$color)
# plot(rnet_schools$geometry)
# plot(rnet_schools$geometry, col = rnet_schools$color)
# plot(schools$geometry, add = TRUE)
#########Create a combined route network (both commute and schools)########
commute_less = rnet_commute[,c(1,6,8,9)]
schools_less = rnet_schools[,c(1,5,6,7)]
combine = rbind(commute_less,schools_less)
summary(combine)
###Creating the route network###
rnet_combined = stplanr::overline2(x = combine, attrib = "dutch_slc")
rnet_combined$color = cut(x = rnet_combined$dutch_slc, breaks = breaks, labels = colors)
rnet_combined$color = as.character(rnet_combined$color)
# plot(rnet_combined$geometry, col = rnet_combined$color)
##Create a combined route network in which the schools component has double weighting
schools_doubled = mutate(schools_less, dutch_slc = dutch_slc*2)
weighted_combine = rbind(commute_less,schools_doubled)
rnet_weighted = stplanr::overline2(x = weighted_combine, attrib = "dutch_slc")
rnet_weighted$color = cut(x = rnet_weighted$dutch_slc, breaks = breaks, labels = colors)
rnet_weighted$color = as.character(rnet_weighted$color)
#########Rasterisation (1000m resolution)###########
# rasterisation of schools route network
r = raster::raster(rnet_commute, resolution = 1000)
# Note: the next line is sloooow, even for small region of IoW and with low spatial resolution (1 km)
rnet_schools_raster = raster::rasterize(x = rnet_schools, y = r, field = "dutch_slc", fun = mean)
# # test with terra
# devtools::install_github("rspatial/terra")
mapview::mapview(rnet_schools_raster)
# mapview::mapview(sec_schools) +
# mapview::mapview(z)
saveRDS(rnet_schools_raster, file = "wyorks_schools_raster.rds")
# rasterisation of commute route network
rnet_commute_raster = raster::rasterize(x = rnet_commute, y = r, field = "dutch_slc", fun = mean)
mapview::mapview(rnet_commute_raster) +
###Create 1km raster for the combined route network, fun=length###
rnet_combined_raster = raster::rasterize(x = rnet_combined, y = r, field = "dutch_slc", fun = length)
mapview::mapview(rnet_combined_raster)
# ideas/next steps (could become github issues)
# do the same for commute layer
# try a different function. Mean doesn't work well - it produces the highest values in squares that only contain one road (eg near Ilkley). Could include length or length * n_cycling or number of segments or number of segments * mean
# Idea: find cells that are above a threshold value in both schools and commute layers
# Filter out single or double isolated pixels to identify routes where there is overlap
# https://cran.r-project.org/web/packages/imager/vignettes/pixsets.html
# Is this possible? Check methods
# Update school origin-destination data
# Data at higher resolution?
# New primary schools? Ask Martin
##################Rasterisation (100m and 200m resolution)##########################
###Create 200m raster for the combined route network, fun=length###
r200 = raster::raster(rnet_commute, resolution = 200)
rnet_combined_raster200 = raster::rasterize(x = rnet_combined, y = r200, field = "dutch_slc", fun = length)
mapview::mapview(rnet_combined_raster200)
###Create 200m raster for the combined route network, fun=mean###
raster200m_combined_mean = raster::rasterize(x = rnet_combined, y = r200, field = "dutch_slc", fun = mean)
mapview::mapview(raster200m_combined_mean)
##Calculate length*mean for a 200m raster###
wyorks200m_combined_lxm = overlay(rnet_combined_raster200,raster200m_combined_mean,fun=function(r1,r2){return(r1*r2)})
mapview::mapview(wyorks200m_combined_lxm)
###Create 100m raster for the combined route network, fun=length###
r100 = raster::raster(rnet_commute, resolution = 100)
wyorks100m_combined_length = raster::rasterize(x = rnet_combined, y = r100, field = "dutch_slc", fun = length)
mapview::mapview(wyorks100m_combined_length)
###Create 100m raster for the combined route network, fun=mean###
wyorks100m_combined_mean = raster::rasterize(x = rnet_combined, y = r100, field = "dutch_slc", fun = mean)
mapview::mapview(wyorks100m_combined_mean)
##Calculate length*mean for a 100m raster###
wyorks100m_combined_lxm = raster::rasterize(x = rnet_combined, y = r100, field = "dutch_slc", fun = function(x,...){return(length(x)*mean(x))})
#wyorks100m_combined_lxm = overlay(wyorks100m_combined_length,wyorks100m_combined_mean,fun=function(r1,r2){return(r1*r2)})
mapview::mapview(wyorks100m_combined_lxm)
#writeRaster(wyorks200m_combined_lxm,"wyorks200m_combined_lxm.tiff")
##############Vector rnet maps###############
##Plot the combined vector map just for central Leeds###
ggplot() + geom_sf(data = rnet_combined$geometry, col = rnet_combined$color) + coord_sf(xlim = c(427000,431000),ylim = c(432000,436000),expand = FALSE) + theme_bw()
ggplot() + geom_sf(data = rnet_commute$geometry, col = rnet_commute$color) + coord_sf(xlim = c(427000,431000),ylim = c(432000,436000),expand = FALSE) + theme_bw()
ggplot() + geom_sf(data = rnet_schools$geometry, col = rnet_schools$color) + coord_sf(xlim = c(427000,431000),ylim = c(432000,436000),expand = FALSE) + theme_bw()
# with tmap ---------------------------------------------------------------
library(tmap)
# pre-processing
names(rnet_combined)
names(rnet_commute)
names(rnet_schools)
# leeds = ukboundaries::leeds %>% st_transform(27700)
# leeds_centre = leeds %>% st_centroid()
library(osmdata)
leeds_rail_station = opq("leeds") %>%
add_osm_feature("railway", "station") %>%
osmdata_sf()
leeds_centre = leeds_rail_station$osm_polygons %>% st_transform(27700)
leeds = leeds_centre %>% st_buffer(5000)
rnet1 = rnet_commute[leeds,] %>% dplyr::select(dutch_slc) %>% mutate(layer = "commute")
rnet2 = rnet_schools[leeds,] %>% dplyr::select(dutch_slc) %>% mutate(layer = "school")
rnet3 = rnet_combined[leeds,] %>% dplyr::select(dutch_slc) %>% mutate(layer = "combined")
rnet4 = rnet_weighted[leeds,] %>% dplyr::select(dutch_slc) %>% mutate(layer = "weighted")
rnet_all = rbind(rnet1, rnet2, rnet3, rnet4)
rnet_all$layer = factor(x = rnet_all$layer, levels = c("commute", "school", "combined", "weighted"))
levels(rnet_all$layer)
p4 = tm_shape(rnet_all) +
tm_lines(lwd = "dutch_slc", legend.lwd.show = F, scale = 9) +
tm_facets(by = "layer",nrow = 1,ncol = 4)
#tmap_save(p4,"faceted-map-leeds.png", height = 757, width = 2341)
#Saved from GUI as faceted_to_scale.png
#Check image attributes
library(png)
img1_path <- "./faceted_to_scale.png"
img1 <- readPNG(img1_path, native = TRUE, info = TRUE)
attr(img1, "info")
magick::image_read("./faceted_to_scale.png")
img2_path <- "./faceted-map-leeds.png"
img2 <- readPNG(img2_path, native = TRUE, info = TRUE)
attr(img2, "info")
magick::image_read("./faceted-map-leeds.png")
# ###Putting schools on a route network map
# plot(rnet_schools$geometry,col = rnet_schools$color)
# plot(schools,add = TRUE)