/
merging-route-networks.Rmd
209 lines (175 loc) · 6.42 KB
/
merging-route-networks.Rmd
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
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
---
title: "Merging route networks"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Merging route networks}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
# # Uncomment to speed-up build
eval = FALSE,
comment = "#>",
echo = TRUE,
message = FALSE,
warning = FALSE
)
# devtools::load_all()
sf::sf_use_s2(FALSE)
```
```{r setup}
# library(stplanr)
devtools::load_all()
library(dplyr)
library(tmap)
library(ggplot2)
library(tmaptools)
rnet_x = sf::read_sf("https://github.com/ropensci/stplanr/releases/download/v1.0.2/rnet_x_ed.geojson")
rnet_y = sf::read_sf("https://github.com/ropensci/stplanr/releases/download/v1.0.2/rnet_y_ed.geojson")
# dups = duplicated(rnet_x$geometry)
# summary(dups)
# rnet_x = rnet_x |>
# filter(!dups)
# sf::write_sf(rnet_x, "~/github/ropensci/stplanr/rnet_x_ed.geojson", delete_dsn = TRUE)
```
# Target network preprocessing
We pre-processed the input simple geometry to make it even simpler as shown below.
```{r, out.width="50%", fig.width=8, fig.height=6, fig.show='hold'}
# tmap_mode("view")
# nrow(rnet_x)
# summary(sf::st_length(rnet_x))
plot(sf::st_geometry(rnet_x))
rnet_x = rnet_subset(rnet_x, rnet_y, dist = 20)
# nrow(rnet_x)
# plot(sf::st_geometry(rnet_x))
rnet_x = rnet_subset(rnet_x, rnet_y, dist = 20, min_length = 5)
# summary(sf::st_length(rnet_x))
# nrow(rnet_x)
# plot(sf::st_geometry(rnet_x))
rnet_x = rnet_subset(rnet_x, rnet_y, dist = 20, rm_disconnected = TRUE)
# nrow(rnet_x)
plot(sf::st_geometry(rnet_x))
```
The initial merged result was as follows (original data on left)
```{r}
funs = list(value = sum, Quietness = mean)
brks = c(0, 100, 500, 1000, 5000)
system.time({
rnet_merged = rnet_merge(rnet_x, rnet_y, dist = 10, segment_length = 20, funs = funs)
})
m1 = tm_shape(rnet_y) + tm_lines("value", palette = "viridis", lwd = 5, breaks = brks) +
tm_scale_bar()
m2 = tm_shape(rnet_merged) + tm_lines("value", palette = "viridis", lwd = 5, breaks = brks)
tmap_arrange(m1, m2, sync = TRUE, nrow = 1)
```
Speed-up the results by transforming to a projected coordinate system:
```{r}
rnet_x = sf::st_transform(rnet_x, 27700)
rnet_y = sf::st_transform(rnet_y, 27700)
```
```{r}
rnet_y_segmented = line_segment(rnet_y, segment_length = 20, use_rsgeo = TRUE)
system.time({
rnet_merged2 = rnet_merge(rnet_x, rnet_y, dist = 10, segment_length = 20, funs = funs)
})
```
Let's check the results:
```{r}
names(rnet_merged)
summary(rnet_merged$value)
summary(rnet_y$value)
sum(rnet_merged$value * sf::st_length(rnet_merged), na.rm = TRUE)
sum(rnet_y$value * sf::st_length(rnet_y), na.rm = TRUE)
```
We can more reduce the minimum segment length to ensure fewer NA values in the outputs:
```{r}
rnet_merged = rnet_merge(rnet_x, rnet_y, dist = 20, segment_length = 10, funs = funs)
m1 = tm_shape(rnet_y) + tm_lines("value", palette = "viridis", lwd = 5, breaks = brks)
m2 = tm_shape(rnet_merged) + tm_lines("value", palette = "viridis", lwd = 5, breaks = brks)
tmap_arrange(m1, m2, sync = TRUE, nrow = 1)
```
As shown in the results, some sideroad values have unrealistically high values:
![](https://user-images.githubusercontent.com/1825120/267946945-d89dfb99-fb60-4db5-ab39-168773ef01ad.png)
Let's see the results again:
```{r}
summary(rnet_merged$value)
summary(rnet_y$value)
sum(rnet_merged$value * sf::st_length(rnet_merged), na.rm = TRUE)
sum(rnet_y$value * sf::st_length(rnet_y), na.rm = TRUE)
```
The good news: the number of NAs is down to only 21 compared with the previous 100+.
Bad news: sideroads have been assigned values from the main roads.
We can fix this with the `max_angle_diff` argument:
```{r}
rnet_merged = rnet_merge(rnet_x, rnet_y, dist = 20, segment_length = 10, funs = funs, max_angle_diff = 20)
m1 = tm_shape(rnet_y) + tm_lines("value", palette = "viridis", lwd = 5, breaks = brks)
m2 = tm_shape(rnet_merged) + tm_lines("value", palette = "viridis", lwd = 5, breaks = brks)
tmap_arrange(m1, m2, sync = TRUE, nrow = 1)
```
As shown in the results, the sideroad values are fixed:
![](https://user-images.githubusercontent.com/1825120/267955054-3bd393a7-1fdd-44a4-8717-7933199c6f37.png)
Let's see the results again:
```{r}
summary(rnet_merged$value)
summary(rnet_y$value)
sum(rnet_merged$value * sf::st_length(rnet_merged), na.rm = TRUE)
sum(rnet_y$value * sf::st_length(rnet_y), na.rm = TRUE)
```
It also works with charaster strings:
```{r}
rnet_y$char = paste0("road", sample(1:3, nrow(rnet_y), replace = TRUE))
most_common = function(x) {
ux = unique(x)
ux[which.max(tabulate(match(x, ux)))]
}
funs = list(char = most_common)
system.time({
rnet_merged = rnet_merge(rnet_x, rnet_y, dist = 10, segment_length = 20, funs = funs)
})
plot(rnet_y["char"])
plot(rnet_merged["char"])
```
Now let's testing on 3km dataset
```{r}
rnet_x = sf::read_sf("https://github.com/nptscot/networkmerge/releases/download/v0.1/os_3km.geojson")
rnet_y = sf::read_sf("https://github.com/nptscot/npt/releases/download/rnet_3km_buffer/rnet_3km_buffer.geojson")
```
Read columns from rnet_y to assign functions to them
```{r}
# Extract column names from the rnet_x data frame
name_list <- names(rnet_y)
name_list
# Initialize an empty list
funs <- list()
# Loop through each name and assign it a function based on specific conditions
for (name in name_list) {
if (name == "geometry") {
next # Skip the current iteration
} else if (name %in% c("Gradient", "Quietness")) {
funs[[name]] <- mean
} else {
funs[[name]] <- sum
}
}
```
```{r, eval = FALSE}
brks = c(0, 100, 500, 1000, 5000,10000)
colors <- c("green", "yellow", "blue", "purple", "red")
rnet_merged = rnet_merge(rnet_x, rnet_y, dist = 20, segment_length = 10, funs = funs, max_angle_diff = 20)
# st_write(rnet_merged, "data-raw/3km_exmaple_merged.geojson", driver = "GeoJSON")
rnet_merged <- st_make_valid(rnet_merged)
m1 = tm_shape(rnet_y) + tm_lines("all_fastest_bicycle", palette = "viridis", lwd = 5, breaks = brks)
m2 = tm_shape(rnet_merged) + tm_lines("all_fastest_bicycle", palette = "viridis", lwd = 5, breaks = brks)
tmap_arrange(m1, m2, sync = TRUE, nrow = 1)
```
Read 3km_exmaple_merged from github
```{r}
exmaple_3km = sf::read_sf("https://github.com/nptscot/networkmerge/releases/download/v0.1/3km_exmaple_merged.geojson")
names(rnet_y)
summary(rnet_y$all_fastest_bicycle)
summary(exmaple_3km$all_fastest_bicycle)
sum(exmaple_3km$all_fastest_bicycle * sf::st_length(exmaple_3km), na.rm = TRUE)
sum(rnet_y$all_fastest_bicycle * sf::st_length(rnet_y), na.rm = TRUE)
```