-
Notifications
You must be signed in to change notification settings - Fork 0
/
Codes_for_visualization.Rmd
319 lines (262 loc) · 13.3 KB
/
Codes_for_visualization.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
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
---
title: "Living and Playing together - boat detection visualization"
author:
- email: [email protected]
name: Julie Vercelloni
date: "`r format(Sys.time(), '%d %B, %Y')`"
output: bookdown::html_document2
fontsize: 12pt
header-includes:
\usepackage{float} \floatplacement{figure}{H}
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,
message = F,
warning = F)
```
# Exploratory visualizations
```{r, eval=T, echo=T}
source("R/packages.R")
dat <- read.csv("boatClassifications_LAST.csv")%>%
mutate(boatclass = ifelse(class == 0, "stationary", "moving")) %>%
mutate(date1 = as.Date(date, format = "%d/%m/%Y")) %>%
mutate(month_num = format(date1, "%m"), year = format(date1, "%Y")) %>%
mutate(Site= ifelse(AOI=="peelIsland","Peel Island (Teerk Roo Ra)",
ifelse(AOI=="southBribie","Bribie Island (Yarun) South",
ifelse(AOI=="tangalooma","Moreton Island (Mulgumpin) Central West","NA")))) %>%
mutate(day = wday(date1, label=TRUE)) %>%
mutate(wend = ifelse(day %in% c("Sat", "Sun"), "Weekend", "Week"))
# Look at the overall number of images across sites
tal <- dat %>% group_by(date1, Site) %>%
filter(row_number() == 1) %>%
group_by(year, Site) %>% tally() %>% arrange(desc(n))
```
```{r fig1, fig.align = 'center', fig.width=9,fig.height=5, fig.cap="Number of analyzed images by the ML (machine learning) algorithms per area of interest."}
ggplot(tal, aes(x = year, y = n, group = Site, col = Site)) + geom_point(size = 2.2) + geom_line() +
theme_bw() + xlab("") + ylab("Number of analyzed images") +
theme(axis.text.x = element_text(size = 11), legend.position="bottom",
legend.title = element_text(colour = "black", size = 13, face = "bold"),
legend.key = element_blank(), legend.background = element_blank(),
legend.text = element_text(colour = "black", size = 11),
panel.grid.minor = element_blank(), plot.title = element_text(hjust = 0.5,vjust = 2, size = 15, face = "bold"),
axis.text.y = element_text(size = 11), axis.title.y = element_text(size = 13),
axis.title.x = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"), strip.background = element_rect(fill = "white"))+
scale_colour_manual("", values = wesanderson::wes_palette("GrandBudapest1", n = 3))
```
```{r, eval=T, echo=T}
dat_wend <- dat%>% group_by(date1, Site) %>%
filter(row_number() == 1) %>%
group_by(Site, month_num, year, wend) %>% tally()
```
```{r fig2, fig.align = 'center', fig.width=9,fig.height=5, fig.cap="Number of analyzed images by the ML algorithms splitted into week days and weekend."}
ggplot(dat_wend, aes(x = year, y= n, fill = wend)) +
geom_bar(stat="identity", width=.5, position = "dodge") + facet_wrap(~Site, scales = "free", ncol = 2) +
theme_bw() + xlab("") + ylab("Number of analyzed images") +
theme(axis.text.x = element_text(size = 11),legend.position="bottom",
legend.title = element_text(colour = "black", size = 13, face = "bold"),
legend.key = element_blank(), legend.background = element_blank(),
legend.text = element_text(colour = "black", size = 11),
panel.grid.minor = element_blank(), plot.title = element_text(hjust = 0.5, vjust = 2, size = 15, face = "bold"),
axis.text.y = element_text(size = 11), axis.title.y=element_text(size = 13), axis.title.x=element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"),strip.background = element_rect(fill = "white"))+
scale_fill_manual("",values = wesanderson::wes_palette("IsleofDogs1", n = 2))
```
```{r, eval=T, echo=T}
dat_sum <- dat %>% group_by(date1, Site, boatclass) %>% tally() %>%
rename(n_boat = n) %>%
mutate(year = format(date1, "%Y")) %>%
group_by(year, Site, boatclass) %>%
summarise(mean_boat = mean(n_boat), sd_boat = sd(n_boat), `Number of images` = n()) %>%
mutate(SE = sd_boat / sqrt(`Number of images`))
```
```{r fig3, fig.align = 'center', fig.width=14,fig.height=8, fig.cap="Averaged Nnumber of detected boats through time. The size of dots corresponds to the number of images analysed by the ML algorithms. Error bars show 95% confidence intervals."}
ggplot(dat_sum, aes(x=year, y=mean_boat, fill=boatclass, group = boatclass, col = boatclass)) +
geom_errorbar(aes(ymin=mean_boat - 1.96*SE, ymax=mean_boat + 1.96*SE), width=.2, show.legend = F) +
geom_point(aes(size = `Number of images`), alpha=.6, shape = 21, col = "black") + facet_wrap(~Site) + geom_line(show.legend = F) +
theme_bw() + xlab("") + ylab("Number of detected boats") +
theme(axis.text.x = element_text(size = 11, angle = 45, hjust = 1), legend.position="right",
legend.title = element_text(colour = "black", size = 13, face = "bold"),
legend.key = element_blank(), legend.background = element_blank(),
legend.text = element_text(colour = "black", size = 11),
panel.grid.minor = element_blank(), plot.title = element_text(hjust = 0.5, vjust = 2, size = 15, face="bold"),
axis.text.y = element_text(size = 11), axis.title.y = element_text(size = 13), axis.title.x=element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"), strip.background = element_rect(fill = "white"))+
scale_fill_manual("Category", values = c("navy", "red"))+
scale_colour_manual("", values = c("navy", "red"))
```
```{r, eval=T, echo=T}
dat_sum_end <- dat %>% group_by(date1, Site, boatclass,wend) %>% tally() %>%
rename(n_boat = n) %>%
mutate(year = format(date1, "%Y")) %>%
group_by(year, Site, boatclass, wend) %>%
summarise(mean_boat = mean(n_boat), sd_boat = sd(n_boat), `Number of images` = n()) %>%
mutate(SE = sd_boat / sqrt(`Number of images`))
```
```{r fig4, fig.align = 'center', fig.width=10,fig.height=6, fig.cap="Averaged number of detected boats during weekdays and weekend. The size of dots corresponds to the number of images analysed by the ML algorithms. The error bars show 95% confidence intervals."}
ggplot(dat_sum_end, aes(x=year, y=mean_boat, fill=boatclass, group = boatclass, col = boatclass)) +
geom_errorbar(aes(ymin=mean_boat - 1.96*SE, ymax=mean_boat + 1.96*SE), width=.2, show.legend = F) +
geom_point(aes(size = `Number of images`), alpha=.6, shape = 21, col = "black") +
facet_wrap(~Site + wend, ncol = 2) + geom_line(show.legend = F) +
theme_bw() + xlab("") + ylab("Number of detected boats") +
theme(axis.text.x = element_text(size = 11, angle = 45, hjust = 1), legend.position="right",
legend.title = element_text(colour = "black", size = 13, face = "bold"),
legend.key = element_blank(), legend.background = element_blank(),
legend.text = element_text(colour = "black", size = 11),
panel.grid.minor = element_blank(),
plot.title = element_text(hjust = 0.5, vjust = 2, size = 15, face = "bold"),
axis.text.y = element_text(size = 11), axis.title.y = element_text(size = 13),
axis.title.x = element_text(size = 13),
strip.text = element_text(size = 13, face = "bold"), strip.background = element_rect(fill = "white"))+
scale_fill_manual("Category", values = c("navy", "red"))+
scale_colour_manual("", values = c("navy", "red"))
```
```{r, eval=T, echo=T}
dat_grouped <- dat %>%
mutate(month_name = lubridate::month(as.numeric(month_num), label = TRUE, abbr = FALSE)) %>%
mutate(group_ID = paste(month_name,year, sep = " "))
tal1 <- dat_grouped %>% group_by(group_ID, year, month_name, boatclass, Site) %>% tally() %>% arrange(desc(n))
site_tal <- unique(tal1$Site)
p <- list()
for ( i in 1:length(site_tal)){
p[[i]] <- ggplot(tal1 %>% filter(Site == site_tal[i]), aes(x = month_name, y= n, fill = boatclass)) +
geom_bar(stat="identity", width=.5, position = "dodge", alpha=.6) + facet_wrap(~year, scales = "free", ncol = 2) +
theme_bw() + xlab("") + ylab("Number of detected boats") +
theme(axis.text.x = element_text(size=11, angle = 45, hjust = 1), legend.position="bottom",
legend.title = element_text(colour = "black", size = 13, face = "bold"),
legend.key = element_blank(),legend.background = element_blank(),
legend.text = element_text(colour = "black", size = 11),
panel.grid.minor = element_blank(),plot.title = element_text(hjust = 0.5,vjust = 2,size = 15, face="bold"),
axis.text.y = element_text(size=11),axis.title.y = element_text(size = 13),axis.title.x=element_text(size = 13),
strip.text = element_text(size=13, face = "bold"),strip.background = element_rect(fill = "white"))+
scale_fill_manual("Category",values = c("navy", "red")) + ggtitle(site_tal[i])
}
```
```{r fig5, fig.align = 'center', fig.width=12,fig.height=12, fig.cap="Number of detected boats through time per area of interest."}
p[[1]]
p[[2]]
p[[3]]
```
# Spatial mapping
The following codes produced maps of detected boats per area of interest. Maps display the geographic positions of boat detected by the machine learning algorithms within a month. Maps are saved in the main working directory as png and gif formats.
```{r, eval=T, echo=T}
#Filter group_ID with less than 50 boats for spatial visualization
tal <- dat_grouped %>% group_by(group_ID, Site) %>% tally() %>% arrange(n) %>% filter(n>50)
dat_grouped <- dat_grouped %>% filter(group_ID %in% tal$group_ID)
# Loop over the period (month per year) to create one map per period
tag.map.title <- tags$style(HTML("
.leaflet-control.map-title {
transform: translate(-50%,20%);
position: fixed !important;
left: 50%;
text-align: left;
font-size: 20px;
color: black;
font-weight: bold
}
"))
pal <- colorFactor(c("navy", "red"), domain = c("stationary","moving"))
# Site 1
dat_site <- dat_grouped %>% filter(Site == site_tal[1])
# Transform in spatial dataframe
dat_sf <- st_as_sf(dat_site, coords = c("longitude", "latitude"), crs = 4326)
yy <- unique(dat_sf$group_ID)
for (i in 1:length(yy)){
title <- tags$div(
tag.map.title, HTML(yy[i])
)
m <- leaflet(dat_sf %>% filter(group_ID == yy[i] )) %>%
setView(lng = mean(dat_site$longitude), lat = mean(dat_site$latitude), zoom = 11) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addCircleMarkers(
radius = ~ifelse(boatclass == "moving", 3, 2),
color = ~pal(boatclass),
stroke = FALSE, fillOpacity = 0.8
) %>%
addLegend(pal = pal, values = c("stationary","moving"),
title = "Boat detection") %>%
addControl(title, position = "topleft", className="map-title")
## This is the png creation part
saveWidget(m, 'temp.html', selfcontained = FALSE)
webshot('temp.html', file=sprintf('Site1%02d.png', i),
cliprect = 'viewport')
}
# Get the GIF
png.files <- sprintf("Site1%02d.png", 1:length(yy))
GIF.convert <- function(x, output = paste0(site_tal[1],".gif"))
{
image_read(x) %>%
image_animate(fps = 1) %>%
image_write(output)
}
GIF.convert(png.files)
# Site 2
dat_site <- dat_grouped %>% filter(Site == site_tal[2]) %>%
filter(! class == 1) # moving boat detected only in May 2018
# Transform in spatial dataframe
dat_sf <- st_as_sf(dat_site, coords = c("longitude", "latitude"), crs = 4326)
yy <- unique(dat_sf$group_ID)
for (i in 1:length(yy)){
title <- tags$div(
tag.map.title, HTML(yy[i])
)
m <- leaflet(dat_sf %>% filter(group_ID == yy[i])) %>%
setView(lng = mean(dat_site$longitude), lat = mean(dat_site$latitude), zoom = 12) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addCircleMarkers(
radius = ~2,
color = ~ "red",
stroke = FALSE, fillOpacity = 0.8
) %>%
addLegend(pal = pal, values = c("stationary","moving"),
title = "Boat detection") %>%
addControl(title, position = "topleft", className="map-title")
## This is the png creation part
saveWidget(m, 'temp.html', selfcontained = FALSE)
webshot('temp.html', file= sprintf('Site2%02d.png', i),
cliprect = 'viewport')
}
# Get the GIF
png.files <- sprintf("Site2%02d.png", 1:length(yy))
GIF.convert <- function(x, output = paste0(site_tal[2],".gif"))
{
image_read(x) %>%
image_animate(fps = 1) %>%
image_write(output)
}
GIF.convert(png.files)
# Site 3
dat_site <- dat_grouped %>% filter(Site == site_tal[3])
# Transform in spatial dataframe
dat_sf <- st_as_sf(dat_site, coords = c("longitude", "latitude"), crs = 4326)
yy <- unique(dat_sf$group_ID)
for (i in 1:length(yy)){
title <- tags$div(
tag.map.title, HTML(yy[i])
)
m <- leaflet(dat_sf %>% filter(group_ID == yy[i] )) %>%
setView(lng = mean(dat_site$longitude), lat = mean(dat_site$latitude), zoom = 11) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addCircleMarkers(
radius = ~ifelse(boatclass == "moving", 3, 2),
color = ~pal(boatclass),
stroke = FALSE, fillOpacity = 0.8
) %>%
addLegend(pal = pal, values = c("stationary","moving"),
title = "Boat detection") %>%
addControl(title, position = "topleft", className="map-title")
## This is the png creation part
saveWidget(m, 'temp.html', selfcontained = FALSE)
webshot('temp.html', file=sprintf('Site3%02d.png', i),
cliprect = 'viewport')
}
# Get the GIF
png.files <- sprintf("Site3%02d.png", 1:length(yy))
GIF.convert <- function(x, output = paste0(site_tal[3],".gif"))
{
image_read(x) %>%
image_animate(fps = 1) %>%
image_write(output)
}
GIF.convert(png.files)
```