-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathDHS_readData_MA.R
287 lines (248 loc) · 15.9 KB
/
DHS_readData_MA.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
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
source('DHS_functions.R')
DHS_checkPackages()
source('DHS_config.R')
## ---- Read in the GIS data (shapefiles)
## Lynda Radke has provided a Middle Arm shapefile
middle_arm.sp<- maptools:::readShapeSpatial("data/GIS/MA_WA_StudyArea.shp",
proj4string = CRS('+proj=utm +zone=52 +south +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +units=m +no_defs'),
repair=TRUE,force_ring=T,verbose=TRUE)
temp_data = middle_arm.sp@data %>% summarize(OBJECTID = 1, DH=1, Shape_Leng=mean(Shape_Leng), Shape_Area=mean(Shape_Area),Name='Middle Arm')
middle_arm.sp <- unionSpatialPolygons(middle_arm.sp ,ID=rep(1, length(middle_arm.sp$Zone_Name)))
middle_arm.sp = SpatialPolygonsDataFrame(middle_arm.sp, data=temp_data)
middle_arm.sp <- spTransform(middle_arm.sp, CRS('+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84'))
middle_arm.fort <- broom::tidy(middle_arm.sp,region='Name')
g = ggplot(middle_arm.fort, aes(y=lat, x=long, group=group)) + geom_polygon(color='black') #+ coord_map()
save(middle_arm.sp, file='data/processed/middle_arm.sp.RData')
middle_arm.df = broom::tidy(middle_arm.sp, region='Name')
save(middle_arm.df, file='data/processed/middle_arm.df.RData')
## The previous version had a map of the entire harbour that outlined the focal areas
## So we need to add in the East Arm
load(file='data/processed/east_arm.sp.RData')
DH.zone_MA.sp <- rbind(middle_arm.sp, east_arm.sp, makeUniqueIDs = TRUE)
save(DH.zone_MA.sp, file='data/processed/DH.zone_MA.sp.RData')
DH.zone_MA.df <- broom::tidy(DH.zone_MA.sp, region='Name')
save(DH.zone_MA.df, file='data/processed/DH.zone_MA.df.RData')
spatial <- read.csv('config/spatial.csv', strip.white=TRUE)
centroids = data.frame(ZoneName=DH.zone.sp$Name, gCentroid(DH.zone.sp,byid=TRUE))
spatial = spatial %>% left_join(centroids)
sf.poly_MA=st_as_sf(DH.zone_MA.sp)
spatial = spatial %>% filter(RegionName!='Outer', ZoneName != 'Shoal Bay', ZoneName != 'Elizabeth River') %>%
mutate(ZoneName=ifelse(ZoneName=='Outer Harbour', 'Outer Harbour\n and Shoal Bay',ZoneName)) %>%
mutate(Lab_lat = ifelse(ZoneName=='Blackmore River', -12.7,
ifelse(ZoneName=='West Arm', -12.65,
ifelse(ZoneName=='Middle Harbour', -12.4, Lab_lat))),
Lab_long = ifelse(ZoneName=='West Arm', 130.75,
ifelse(ZoneName=='Middle Harbour', 130.75, Lab_long)),
hjust = ifelse(ZoneName=='West Arm', 0.5,
ifelse(ZoneName=='Middle Harbour', 0.5, hjust)),
vjust = ifelse(ZoneName=='West Arm', 1,
ifelse(ZoneName=='Middle Harbour', 0, vjust))
)
g = ggplot() +
geom_sf(data=sf.poly_MA, alpha=0.8, aes(fill=Name), show.legend=FALSE) +
#scale_fill_manual(breaks=sf.poly$Name, values=c('grey40', 'grey90', 'grey40', 'grey90', 'grey40')) +
geom_point(data=spatial, aes(y=y, x=x),show.legend=FALSE) +
geom_segment(data=spatial, aes(y=y, x=x, yend=Lab_lat, xend=Lab_long)) +
geom_text(data=spatial[,], aes(y=Lab_lat, x=Lab_long, label=ZoneName, hjust=hjust, vjust=vjust)) +
coord_sf() +
annotation_scale(location='bl') +
annotation_north_arrow(location='bl', which_north='true',
pad_x=unit(0.05,'npc'),pad_y=unit(0.05,'npc'),
style=north_arrow_fancy_orienteering) +
theme_bw() +
theme(axis.title.y=element_blank(), axis.title.x=element_blank())
g
ggsave('output/Map_MA1.pdf', g, width=7, height=8)
ggsave('output/Map_MA1.png', g, width=7, height=8, units='in', dpi=300)
## ----end
## ---- Read in the GIS data (shapefiles)
## There are some areas that have a hard seabed that are not ideal for sampling, these should be masked out
hard_seabed_MA.sp<- maptools:::readShapeSpatial("data/GIS/Hard_Seabed_MA.shp",
proj4string = CRS('+proj=utm +zone=52 +south +datum=WGS84 +units=m +no_defs'),repair=TRUE,force_ring=T,verbose=TRUE)
hard_seabed_MA.sp <- unionSpatialPolygons(hard_seabed_MA.sp ,ID=rep(1, length(hard_seabed_MA.sp$gridcode)))
hard_seabed_MA.sp <- spTransform(hard_seabed_MA.sp, CRS('+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84'))
hard_seabed_MA.fort <- broom::tidy(hard_seabed_MA.sp,region='Name')
g = ggplot(hard_seabed_MA.fort, aes(y=lat, x=long, group=group)) + geom_polygon(color='black') + coord_map()
save(hard_seabed_MA.sp, file='data/processed/hard_seabed_MA.sp.RData')
## Shipping Chanels Merged
shipping_MA.sp<- maptools:::readShapeSpatial("data/GIS/All_MiddleArm_Channels.shp",
proj4string = CRS('+proj=longlat +ellps=GRS80 +towgs84=0,0,0,0,0,0,0 +no_defs'),repair=TRUE,force_ring=T,verbose=TRUE)
shipping_MA.sp <- unionSpatialPolygons(shipping_MA.sp ,ID=rep(1, length(shipping_MA.sp$Id)))
shipping_MA.sp <- spTransform(shipping_MA.sp, CRS('+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84'))
shipping_MA.fort <- broom::tidy(shipping_MA.sp,region='Name')
g = ggplot(shipping_MA.fort, aes(y=lat, x=long, group=group)) + geom_polygon(color='black') + coord_map()
save(shipping_MA.sp, file='data/processed/shipping_MA.sp.RData')
## Exclusion Zones Merged
exclusion_MA.sp<- maptools:::readShapeSpatial("data/GIS/DLNG_Marine_Traffic_Exclusion_Zones.shp",
proj4string = CRS('+proj=utm +zone=52 +south +datum=WGS84 +units=m +no_defs'),repair=TRUE,force_ring=T,verbose=TRUE)
exclusion_MA.sp <- unionSpatialPolygons(exclusion_MA.sp ,ID=rep(1, length(exclusion_MA.sp$Id)))
exclusion_MA.sp <- spTransform(exclusion_MA.sp, CRS('+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84'))
exclusion_MA.fort <- broom::tidy(exclusion_MA.sp,region='Name')
g = ggplot(exclusion_MA.fort, aes(y=lat, x=long, group=group)) + geom_polygon(color='black') + coord_map()
save(exclusion_MA.sp, file='data/processed/exclusion_MA.sp.RData')
## ----end
# The above wont be masked out until after the modelling stage. The reason for this is that the rest of the
## polygons are used to define the barrier models, howver these exclusions are not barriers to physical processes -
## rather they are exclusion zones for sampling. Therefore, they will only be considered in the determination
## of sampling domains in the DHS_sampling.R routines.
## Munksgaard data ==========================================================
## ---- Read in the site data
sites_MA = read_csv('data/MiddleArm_Final_Appendix Darwin Harbour sediment study 2019_location.csv', trim_ws=TRUE,
col_types = cols(Sample=readr::col_factor(NULL),
Date=readr::col_date(format='%d/%m/%y'),
Area=readr::col_factor(NULL)))
head(sites_MA)
save(sites_MA, file='data/processed/sites_MA.RData')
g=ggplot(middle_arm.fort) +
geom_polygon(color='black',aes(y=lat, x=long))+
geom_point(data=sites_MA, aes(y=Latitude, x=Longitude), color='red') +
coord_map()
##g
chem_MA = readr::read_csv('data/primary/MiddleArm_Final_Appendix Darwin Harbour sediment study 2019.csv', trim_ws=TRUE,
col_types = cols(Sample=readr::col_factor(NULL)))
chem_MA
## some of the fields have < entries and so are considered character vectors.
## the > values indicate that the values are less than the limit of detection.
## I will replace them with half the next smallest value for that chemical.
chem_MA = chem_MA %>%
dplyr::select(-Date, -Area) %>%
mutate_if(is.character, function(x) {
xx = x[x!='<'];
Min=min(as.numeric(xx), na.rm=TRUE);
x[x=='<'] = 0.5*Min;
as.numeric(x)
})
sediment.data_MA = chem_MA
save(sediment.data_MA, file='data/processed/sediment.data_MA.RData')
## ----end
## Designated data ==========================================================
## ---- Read in the site data
designated_MA = read_csv('data/MIddleArm_DesignatedSite_Locations.csv', trim_ws=TRUE)
head(designated_MA)
g=ggplot(middle_arm.fort) +
geom_polygon(color='black',aes(y=lat, x=long))+
geom_point(data=designated_MA, aes(y=Latitude, x=Longitude), color='red') +
coord_map()
##g
priority_sites_MA = designated_MA
## ----end
## ---- Site maps
g=ggplot() +
geom_sf(data=sf.poly_MA, fill='grey99') +
coord_sf() + #(xlim=range(c(sediment.data_MA$Longitude,outerharbour.fort$long)),ylim=range(c(sediment.data_MA$Latitude,outerharbour.fort$lat))) +
geom_sf(data=sf.poly_MA %>% filter(Name=='Middle Arm'), alpha=0.8) +
geom_point(data=sediment.data_MA, aes(y=Latitude, x=Longitude)) +
geom_point(data=priority_sites_MA, aes(y=Latitude, x=Longitude), color='red', shape=16) +
annotation_scale(location='bl') +
annotation_north_arrow(location='bl', which_north='true',
pad_x=unit(0.05,'npc'),pad_y=unit(0.05,'npc'),
style=north_arrow_fancy_orienteering) +
theme_bw() +
theme(axis.title.y=element_blank(), axis.title.x=element_blank())
g
ggsave('output/Map2_MA.pdf', g, width=7, height=6)
ggsave('output/Map2_MA.png', g, width=7, height=6, units='in', dpi=300)
g=ggplot() +
geom_sf(data=sf.poly_MA %>% filter(Name=='Middle Arm'), fill='#336A98') +
#geom_sf(data=sf.poly_MA, fill='grey99') +
geom_sf(data=st_as_sf(hard_seabed_MA.sp), fill='white', color='white') +
geom_sf(data=st_as_sf(shipping_MA.sp), fill='white', color='white') +
geom_sf(data=st_as_sf(exclusion_MA.sp), fill='white', color='white') +
geom_sf(data=sf.poly_MA %>% filter(Name=='Middle Arm'), fill=NA, color='black') +
coord_sf() + #(xlim=range(c(sediment.data_MA$Longitude,outerharbour.fort$long)),ylim=range(c(sediment.data_MA$Latitude,outerharbour.fort$lat))) +
geom_point(data=sediment.data_MA, aes(y=Latitude, x=Longitude)) +
geom_point(data=priority_sites_MA, aes(y=Latitude, x=Longitude), color='red', shape=16) +
annotation_scale(location='bl') +
annotation_north_arrow(location='bl', which_north='true',
pad_x=unit(0.05,'npc'),pad_y=unit(0.05,'npc'),
style=north_arrow_fancy_orienteering) +
theme_bw() +
theme(axis.title.y=element_blank(), axis.title.x=element_blank())
g
ggsave('output/Map3_MA.pdf', g, width=7, height=6)
ggsave('output/Map3_MA.png', g, width=7, height=6, units='in', dpi=300)
## ----end
## ---- Hydrodynamic data
bedShear_50p <- raster('data/Hydrodynamics/bedShear_50p.tif', crs=CRS('+proj=utm +zone=52 +south +datum=WGS84 +units=m +no_defs'))
bedShear_75p <- raster('data/Hydrodynamics/bedShear_75p.tif', crs=CRS('+proj=utm +zone=52 +south +datum=WGS84 +units=m +no_defs'))
velocity_50p <- raster('data/Hydrodynamics/velocity_50p.tif', crs=CRS('+proj=utm +zone=52 +south +datum=WGS84 +units=m +no_defs'))
velocity_75p <- raster('data/Hydrodynamics/velocity_75p.tif', crs=CRS('+proj=utm +zone=52 +south +datum=WGS84 +units=m +no_defs'))
hydro = brick(bedShear_50p,bedShear_75p,velocity_50p,velocity_75p)
##change the projection into +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84
hydro = projectRaster(hydro,
crs='+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84')
## now we need to apply a mask to narrow this to the focal area
##outer_east.sp = rbind(east_arm.sp,outerharbour.sp,makeUniqueIDs = TRUE)
hydro.middle_arm.sp = crop(mask(hydro, middle_arm.sp), middle_arm.sp)
hydro.middle_arm.df = rasterToPoints(hydro.middle_arm.sp) %>% as.data.frame
save(hydro.middle_arm.sp, file='data/processed/hydro.middle_arm.sp.RData')
save(hydro.middle_arm.df, file='data/processed/hydro.middle_arm.df.RData')
plot_func = function (df, name) {
ggplot() +
geom_tile(data=df, aes(y=y, x=x, fill=Value)) +
coord_sf(crs='+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84') +
scale_fill_gradientn(name,colors=tim.colors(12)) +
theme_bw()+
theme(axis.title.y=element_blank(), axis.title.x=element_blank())
}
dat = hydro.middle_arm.df %>% gather(key=Variable, value=Value,-x,-y)
dat = dat %>% group_by(Variable) %>% nest() %>%
mutate(g=map2(data, Variable, plot_func))
grid.arrange(grobs=dat$g)
ggsave(filename='output/hydro_middle_arm.pdf', grid.arrange(grobs=dat$g), width=10, height=7)
ggsave(filename='output/hydro_middle_arm.png', grid.arrange(grobs=dat$g), width=10, height=7, units='in', dpi=300)
## ----end
## ---- Wave data
beagle_10_00_Ubot <- raster('data/Waves/beagle_10_00_Ubot.tif', crs=CRS('+proj=utm +zone=52 +south +datum=WGS84 +units=m +no_defs'))
beagle_10_90_Ubot <- raster('data/Waves/beagle_10_90_Ubot.tif', crs=CRS('+proj=utm +zone=52 +south +datum=WGS84 +units=m +no_defs'))
beagle_10_140_Ubot <- raster('data/Waves/beagle_10_140_Ubot.tif', crs=CRS('+proj=utm +zone=52 +south +datum=WGS84 +units=m +no_defs'))
beagle_10_270_Ubot <- raster('data/Waves/beagle_10_270_Ubot.tif', crs=CRS('+proj=utm +zone=52 +south +datum=WGS84 +units=m +no_defs'))
beagle_10_315_Ubot <- raster('data/Waves/beagle_10_315_Ubot.tif', crs=CRS('+proj=utm +zone=52 +south +datum=WGS84 +units=m +no_defs'))
waveShear_10_00 <- raster('data/Waves/waveShear_10_00.tif', crs=CRS('+proj=utm +zone=52 +south +datum=WGS84 +units=m +no_defs'))
waveShear_10_90 <- raster('data/Waves/waveShear_10_90.tif', crs=CRS('+proj=utm +zone=52 +south +datum=WGS84 +units=m +no_defs'))
waveShear_10_140 <- raster('data/Waves/waveShear_10_140.tif', crs=CRS('+proj=utm +zone=52 +south +datum=WGS84 +units=m +no_defs'))
waveShear_10_270 <- raster('data/Waves/waveShear_10_270.tif', crs=CRS('+proj=utm +zone=52 +south +datum=WGS84 +units=m +no_defs'))
waveShear_10_315 <- raster('data/Waves/waveShear_10_315.tif', crs=CRS('+proj=utm +zone=52 +south +datum=WGS84 +units=m +no_defs'))
waves_ubot = brick(beagle_10_00_Ubot,beagle_10_90_Ubot,beagle_10_140_Ubot,beagle_10_270_Ubot,beagle_10_315_Ubot)
waves = brick(waveShear_10_00, waveShear_10_90, waveShear_10_140, waveShear_10_270, waveShear_10_315)
##change the projection into +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84
waves_ubot = projectRaster(waves_ubot,
crs='+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84')
waves = projectRaster(waves,
crs='+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84')
## now we need to apply a mask to narrow this to the focal area
waves_ubot.middle_arm.sp = b = crop(mask(waves_ubot, middle_arm.sp), middle_arm.sp)
waves_ubot.middle_arm.df = rasterToPoints(waves_ubot.middle_arm.sp) %>% as.data.frame
save(waves_ubot.middle_arm.sp, file='data/processed/waves_ubot.middle_arm.sp.RData')
save(waves_ubot.middle_arm.df, file='data/processed/waves_ubot.middle_arm.df.RData')
waves.middle_arm.sp = b = crop(mask(waves, middle_arm.sp), middle_arm.sp)
waves.middle_arm.df = rasterToPoints(waves.middle_arm.sp) %>% as.data.frame
save(waves.middle_arm.sp, file='data/processed/waves.middle_arm.sp.RData')
save(waves.middle_arm.df, file='data/processed/waves.middle_arm.df.RData')
dat = waves_ubot.middle_arm.df %>% gather(key=Variable, value=Value,-x,-y)
dat = dat %>% group_by(Variable) %>% nest() %>%
mutate(g=map2(data, Variable, plot_func))
grid.arrange(grobs=dat$g)
ggsave(filename='output/waves_ubot_middle_arm.pdf', grid.arrange(grobs=dat$g), width=11, height=10.5)
ggsave(filename='output/waves_ubot_middle_arm.png', grid.arrange(grobs=dat$g), width=11, height=10.5, units='in', dpi=300)
## ----end
## ---- Grain size (Mainly inner harbour)
grainsize = read_csv('data/primary/grainsize.csv', trim_ws=TRUE)
head(grainsize)
nms=names(grainsize)[-1]
grainsize = grainsize %>% gather(key=Size,value=Value, -SampleName) %>%
mutate(Size=gsub('\\(','\n(',Size),
Size=gsub('%','',Size),
Size=factor(Size, levels=unique(Size)))
grainsize %>% group_by(Size) %>% summarise(mean(Value, na.rm=TRUE))
## merge with sites and split into Inner and Outer harbour
grainsize = grainsize %>% left_join(sites_MA %>% dplyr::select(SampleName=Sample, Area)) %>% filter(!is.na(Area))
g=ggplot() +
geom_boxplot(data=grainsize, aes(y=Value, x=Size)) +
scale_y_continuous('% Abundance in sediments') +
scale_x_discrete('Grain size category')+
theme_bw()
ggsave(filename='output/particle_sizes_MA.pdf', g, width=8, height=3)
ggsave(filename='output/particle_sizes_MA.png', g, width=6, height=2, units='in', dpi=300)
#geom_density(data=grainsize, aes(x=Value, fill=Size))
## ----end