-
Notifications
You must be signed in to change notification settings - Fork 0
/
02-ialeUK25-analysisAuthorAffilitation.Rmd
227 lines (145 loc) · 8.28 KB
/
02-ialeUK25-analysisAuthorAffilitation.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
# Analysis by Author Affiliation
Bar charts to examine how contribution to conferences have changed over time.
```{r}
#spec(cpdata)
affildata <- cpdata %>%
select_if(is.numeric) %>%
gather(key = Affiliation, value = count, Academic:Private) %>%
filter(count > 0) %>%
group_by(`Affiliation`) %>%
summarise_all(sum, na.rm=T)
```
## Total Conference Contributions
Quick observations:
- Academic contributors dominate, followed by Government (but as shown above, Government contributions have decreased recently, replaced by NGOs)
```{r}
print(affildata %>%
select(Affiliation, count) %>%
mutate(prop = count/sum(count)) %>%
mutate(prop = round(prop,3)))
ggplot(affildata, aes(x=Affiliation, y=count)) +
geom_bar(stat="identity") +
geom_text(aes(x=Affiliation, y=max(count), label = paste0(round(100*count / sum(count),1), "%"), vjust=-0.5))
```
## Landscape Type
Stacked bar plots of contributions (by types and author affiliation)
### Using all landscape types
Quick observations:
- Business not good at reporting landscape type!
- Private have greatest proportions of Seascape and Other
```{r}
lspCounts <- affildata %>%
select(`Affiliation`,`Upland rural`, `Lowland rural`, Urban, Riverscape, Seascape, `Undefined LspType`,Other) %>%
mutate(Asum = rowSums(.[2:8])) %>% #calculate total for subsquent calcultation of proportion
gather(key = Type, value = count, -`Affiliation`, -Asum) %>%
mutate(prop = count / Asum) #calculate proportion
print(affildata %>%
select(`Affiliation`,`Upland rural`, `Lowland rural`, Urban, Riverscape, Seascape, `Undefined LspType`,Other) %>%
mutate(sum = rowSums(.[2:8])) %>% #calculate total
mutate_if(is.numeric, funs(prop = ./ sum))
)
ggplot(lspCounts, aes(x=`Affiliation`, y=count, fill=Type)) + geom_bar(stat="identity", colour="white")
ggplot(lspCounts, aes(x=`Affiliation`, y=prop, fill=Type)) + geom_bar(stat="identity", colour="white")
```
### Without 'Undefined LspType' and 'Other' landscape types
Quick observations:
- Government has greatest proportion of Upland Rural
- Business has greatest Urban proportion and smallest Lowland Rural proportion
- Academic dominates total number of all landscape types (with possible exception of Upland Rural)
```{r}
lspCounts <- affildata %>%
select(`Affiliation`,`Upland rural`, `Lowland rural`, Urban, Riverscape, Seascape) %>%
mutate(Asum = rowSums(.[2:6])) %>% #calculate total for subsquent calcultation of proportion
gather(key = Type, value = count, -`Affiliation`, -Asum) %>%
mutate(prop = count / Asum) #calculate proportion
ggplot(lspCounts, aes(x=`Affiliation`, y=count, fill=Type)) + geom_bar(stat="identity", colour="white")
ggplot(lspCounts, aes(x=`Affiliation`, y=prop, fill=Type)) + geom_bar(stat="identity", colour="white")
```
## Species
Quick observations
- Academic seem to be majority by absolute number for all species
- Business and Private have greatest proportions of Generic Habitat
- NGOs have greatest proportion of Birds (RSPB?)
```{r}
speciesCounts <- affildata %>%
select(`Affiliation`,Mammals, Humans, Birds, Reptiles, Inverts, Plants, Amphibians, Fish, `Generic Habitat`,`Woodland Forests`) %>%
mutate(Asum = rowSums(.[2:11])) %>% #calculate total for subsquent calcultation of proportion
gather(key = Type, value = count, -`Affiliation`, -Asum) %>%
mutate(prop = count / Asum) #calculate proportion
ggplot(speciesCounts, aes(x=`Affiliation`, y=count, fill=Type)) + geom_bar(stat="identity", colour="white")
ggplot(speciesCounts, aes(x=`Affiliation`, y=prop, fill=Type)) + geom_bar(stat="identity", colour="white")
```
## Methods
Quick observations
- Academic seem to be majority by absolute number for all methods
- Business obviously lower proportion of empirical studies (expensive?), substituted by GIS and qualitative
- Government has smallest proportion of qualitative
- Private has greatest proprtion of theoretical, no RS and relatively little GIS (technical training?)
```{r}
methodsCounts <- affildata %>%
select(`Affiliation`,Empirical, Theoretical, Qualitative, Quantitative, GIS, `Remote sensing`) %>%
mutate(Asum = rowSums(.[2:7])) %>% #calculate total for subsquent calcultation of proportion
gather(key = Type, value = count, -`Affiliation`, -Asum) %>%
mutate(prop = count / Asum) #calculate proportion
ggplot(methodsCounts, aes(x=`Affiliation`, y=count, fill=Type)) + geom_bar(stat="identity", colour="white")
ggplot(methodsCounts, aes(x=`Affiliation`, y=prop, fill=Type)) + geom_bar(stat="identity", colour="white")
```
## Spatial Extent
Quick observations
- Academic seem to be majority by absolute number for all extents
- Business have largest proportion of Global and National studies, with smallest proprtion of Local studies
- Private has larest proportion of Local and Mini studies (cost-related and given no RS and few GIS studies?)
- Academic: decreasing proportion Local -> Regional -> National -> Global
- Government: greater proportion of National than Regional
```{r}
spatialCounts <- affildata %>%
select(`Affiliation`,Micro, Mini, Local, Regional, National, Continental, Global,`Undefined Extent`) %>%
mutate(Asum = rowSums(.[2:9])) %>% #calculate total for subsquent calcultation of proportion
gather(key = Type, value = count, -`Affiliation`, -Asum) %>%
mutate(prop = count / Asum) #calculate proportion
ggplot(spatialCounts, aes(x=`Affiliation`, y=count, fill=Type)) + geom_bar(stat="identity", colour="white")
ggplot(spatialCounts, aes(x=`Affiliation`, y=prop, fill=Type)) + geom_bar(stat="identity", colour="white")
```
## Temporal Extent
Quick observations
- Vast majority of all affiliations did not list temporal extent of the study
Academic seem to be majority by absolute number for all methods
- Not much more of interest here...
```{r}
temporalCounts <- affildata %>%
select(`Affiliation`,Hours, Days, Weeks, Months, Years, Decades, Centuries, Longer, `Undefined Temporal`) %>%
mutate(Asum = rowSums(.[2:10])) %>% #calculate total for subsquent calcultation of proportion
gather(key = Type, value = count, -`Affiliation`, -Asum) %>%
mutate(prop = count / Asum) #calculate proportion
ggplot(temporalCounts, aes(x=`Affiliation`, y=count, fill=Type)) + geom_bar(stat="identity", colour="white")
ggplot(temporalCounts, aes(x=`Affiliation`, y=prop, fill=Type)) + geom_bar(stat="identity", colour="white")
```
## Concepts
Quick observations
- Academic seem to be majority by absolute number for all extents
- Business have greatest proportions of climate change and ecosystem services, less interested in history and legacy
- All other affiliations reasonably similar in terms of proportions
```{r}
conceptCounts <- affildata %>%
select(`Affiliation`,`PPS of landscapes`,
`Connectivity and fragmentation`, `Scale and scaling`,`Spatial analysis and modeling`,LUCC,`History and legacy`,`Climate change interactions`,`Ecosystem services`,`Landscape sustainability`,`Accuracy and uncertainty`) %>%
mutate(Asum = rowSums(.[2:11])) %>% #calculate total for subsquent calcultation of proportion
gather(key = Type, value = count, -`Affiliation`, -Asum) %>%
mutate(prop = count / Asum) #calculate proportion
ggplot(conceptCounts, aes(x=`Affiliation`, y=count, fill=Type)) + geom_bar(stat="identity", colour="white")
ggplot(conceptCounts, aes(x=`Affiliation`, y=prop, fill=Type)) + geom_bar(stat="identity", colour="white")
```
## Other Concepts
Quick observations
- Academic seem to be majority by absolute number for all extents
- Government and NGOs have greater proportion of Management and Conservation than Academic
- Private low on biodiversity but higher on cultural landscapes, landscape assessment and planning
```{r}
otherCounts <- affildata %>%
select(`Affiliation`,`Green Infrastructure`,`Planning and Architecture`,`Management and Conservation`,`Cultural Landscapes`,`Socio-economic Dimensions`,Biodiversity,`Landscape Assessment`,`Catchment Based Approach`,`Invasives Pests Diseases`) %>%
mutate(Asum = rowSums(.[2:10])) %>% #calculate total for subsquent calcultation of proportion
gather(key = Type, value = count, -`Affiliation`, -Asum) %>%
mutate(prop = count / Asum) #calculate proportion
ggplot(otherCounts, aes(x=`Affiliation`, y=count, fill=Type)) + geom_bar(stat="identity", colour="white")
ggplot(otherCounts, aes(x=`Affiliation`, y=prop, fill=Type)) + geom_bar(stat="identity", colour="white")
```