06-ialeUK25-analysisSpatial.Rmd

# Analysis by Spatial Extent

Bar charts to examine how contributions to conferences vary by methods

```{r}
#spec(cpdata)

spatdata <- cpdata %>%
  select_if(is.numeric) %>%
  gather(key = Spatial, value = count, Micro:`Undefined Extent`) %>%
  filter(count > 0) %>%
  group_by(`Spatial`) %>%
  summarise_all(sum, na.rm=T) 

uspatdata <- cpdata %>%
  select_if(is.numeric) %>%
  gather(key = Spatial, value = count, Micro:Global) %>%
  filter(count > 0) %>%
  group_by(`Spatial`) %>%
  summarise_all(sum, na.rm=T) 
```

## Total Conference Contributions

Quick observations:

- Dominated by Local extent studies, but also many at national and regional extents
- Global and Continental (largest extent) have fewest studies (8 and 27 respectively)

```{r}

#ggplot(authCounts, aes(x=Spatial, y=count)) + geom_bar(stat="identity")

spatdata %>%
  select(Spatial, count) %>%
  mutate(prop = count/sum(count)) %>%
  mutate(prop = round(prop,3)) %>% 
  kable() %>%
  kable_styling() %>%
  scroll_box(width = "100%")
    

ggplot(spatdata, aes(x=Spatial, y=count)) + 
  geom_bar(stat="identity") + 
  geom_text(aes(x=Spatial, y=max(count), label = paste0(round(100*count / sum(count),1), "%"), vjust=-0.5))


uspatdata %>%
  select(Spatial, count) %>%
  mutate(prop = count/sum(count)) %>%
  mutate(prop = round(prop,3)) %>% 
  kable() %>%
  kable_styling() %>%
  scroll_box(width = "100%")
    

ggplot(uspatdata, aes(x=Spatial, y=count)) + 
  geom_bar(stat="identity") + 
  geom_text(aes(x=Spatial, y=max(count), label = paste0(round(100*count / sum(count),1), "%"), vjust=-0.5))

```

## Author Affiliation

Quick observations:

- global studies are have greater proportions of Business and NGO authorship
- all extents dominated by academic authors, except Global and National 
- All authorships represented at all extents except Private (in Micro and Global) and Government (in Global)

```{r}
authCounts <- spatdata %>%
  select(Spatial,Academic, Government,NGO,Business,Private) %>%
  mutate(sum = rowSums(.[2:6])) %>%   #calculate total for subsquent calcultation of proportion
  gather(key = Type, value = count, -Spatial, -sum) %>%
  mutate(prop = count / sum)  #calculate proportion

spatdata %>%
  select(Spatial,Academic, Government,NGO,Business,Private) %>%
  mutate(Total = rowSums(.[2:6])) %>%   #calculate total
  mutate_if(is.numeric, funs(prop = ./ Total)) %>%
  mutate_at(vars(ends_with("prop")), round, 3) %>% 
  select(-Total_prop) %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(width = "100%")

ggplot(authCounts, aes(x=Spatial, y=count, fill=Type)) + geom_bar(stat="identity", colour="white")

ggplot(authCounts, aes(x=Spatial, y=prop, fill=Type)) + geom_bar(stat="identity", colour="white")

```

## Landscape Type 

### Using all landscape types

Quick observations: 

- Global extent studies do not consider Lowland rural landscapes


```{r}
lspCounts <- spatdata %>%
  select(Spatial,`Upland rural`, `Lowland rural`, Urban,	Riverscape, Seascape, `Undefined LspType`,Other) %>%
  mutate(sum = rowSums(.[2:8])) %>%   #calculate total for subsquent calcultation of proportion
  gather(key = Type, value = count, -Spatial, -sum) %>%
  mutate(prop = count / sum)  #calculate proportion

spatdata %>%
  select(Spatial,`Upland rural`, `Lowland rural`, Urban,	Riverscape, Seascape, `Undefined LspType`,Other) %>%
  mutate(Total = rowSums(.[2:8])) %>%   #calculate total
  mutate_if(is.numeric, funs(prop = ./ Total)) %>%
  mutate_at(vars(ends_with("prop")), round, 3) %>% 
  select(-Total_prop) %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(width = "100%")

ggplot(lspCounts, aes(x=Spatial, y=count, fill=Type)) + geom_bar(stat="identity", colour="white")

ggplot(lspCounts, aes(x=Spatial, y=prop, fill=Type)) + geom_bar(stat="identity", colour="white")
```

### Without 'Undefined LspType' and 'Other' landscape types

Quick observations:

- National extent studies have largest proportion of Upland Rural landscape studies
- Micro, Mini and Regional extent studies dominated by Lowland rural
- 'Undefined extent' has largest proportion of riverscape studies

```{r}

lspCounts <- spatdata %>%
  select(Spatial,`Upland rural`, `Lowland rural`, Urban,	Riverscape, Seascape) %>%
  mutate(sum = rowSums(.[2:6])) %>%   #calculate total for subsquent calcultation of proportion
  gather(key = Type, value = count, -Spatial, -sum) %>%
  mutate(prop = count / sum)  #calculate proportion

spatdata %>%
  select(Spatial,`Upland rural`, `Lowland rural`, Urban,	Riverscape, Seascape) %>%
  mutate(Total = rowSums(.[2:6])) %>%   #calculate total
  mutate_if(is.numeric, funs(prop = ./ Total)) %>%
  mutate_at(vars(ends_with("prop")), round, 3) %>% 
  select(-Total_prop) %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(width = "100%")

ggplot(lspCounts, aes(x=Spatial, y=count, fill=Type)) + geom_bar(stat="identity", colour="white")

ggplot(lspCounts, aes(x=Spatial, y=prop, fill=Type)) + geom_bar(stat="identity", colour="white")

```

## Species

Quick observations

- Global studies are again qualitatively different from other scale studies - no amphibiams, birds, reptiles, woodland studies but largest proportions of Fish and generic Habitat studies
- Smallest extents (mini and micro) have largest proportions of Plant and Inverts studies

```{r}

speciesCounts <- spatdata %>%
  select(Spatial, Mammals, Humans, Birds, Reptiles, Inverts, Plants, Amphibians, Fish, `Generic Habitat`,`Woodland Forests`) %>%
  mutate(sum = rowSums(.[2:11])) %>%   #calculate total for subsquent calcultation of proportion
  gather(key = Type, value = count, -Spatial, -sum) %>%
  mutate(prop = count / sum)  #calculate proportion

spatdata %>%
  select(Spatial, Mammals, Humans, Birds, Reptiles, Inverts, Plants, Amphibians, Fish, `Generic Habitat`,`Woodland Forests`) %>%
  mutate(Total = rowSums(.[2:11])) %>%   #calculate total for subsquent calcultation of proportion
  mutate_if(is.numeric, funs(prop = ./ Total)) %>%
  mutate_at(vars(ends_with("prop")), round, 3) %>% 
  select(-Total_prop) %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(width = "100%")

ggplot(speciesCounts, aes(x=Spatial, y=count, fill=Type)) + geom_bar(stat="identity", colour="white")

ggplot(speciesCounts, aes(x=Spatial, y=prop, fill=Type)) + geom_bar(stat="identity", colour="white")

```


## Methods

Quick observations:

- Global, Continental and Undefined Extent have the greatest proportions of Theoretical,  Qualitative and Remote Sensing studies
- Smallest extents (mini and micro) have the largest proportions of Empirical studies (and local have largest absolute number)
- Regional and Local studies have largest number and proportions of GIS studies (and GIS used least at extremes of extents, i.e. mini, micro and global)

```{r}
methodsCounts <- spatdata %>%
  select(Spatial, Empirical, Theoretical, Qualitative, Quantitative, GIS, `Remote sensing`) %>%
  mutate(sum = rowSums(.[2:7])) %>%
  gather(key = Type, value = count, -Spatial, -sum) %>%
  mutate(prop = count / sum) 

spatdata %>%
  select(Spatial, Empirical, Theoretical, Qualitative, Quantitative, GIS, `Remote sensing`) %>%
  mutate(Total = rowSums(.[2:7])) %>%
  mutate_if(is.numeric, funs(prop = ./ Total)) %>%
  mutate_at(vars(ends_with("prop")), round, 3) %>% 
  select(-Total_prop) %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(width = "100%")

ggplot(methodsCounts, aes(x=Spatial, y=count, fill=Type)) + geom_bar(stat="identity", colour="white")

ggplot(methodsCounts, aes(x=Spatial, y=prop, fill=Type)) + geom_bar(stat="identity", colour="white")

```



## Temporal Extent 

### With undefined

Quick observations:


```{r echo=F}
temporalCounts <- spatdata %>%
  select(Spatial, Hours, Days, Weeks, Months, Years, Decades, Centuries, Longer, `Undefined Temporal`
) %>%
  mutate(sum = rowSums(.[2:10])) %>%
  gather(key = Type, value = count, -Spatial, -sum) %>%
  mutate(prop = count / sum) 

spatdata %>%
  select(Spatial, Hours, Days, Weeks, Months, Years, Decades, Centuries, Longer, `Undefined Temporal`
) %>%
  mutate(Total = rowSums(.[2:10])) %>%
  mutate_if(is.numeric, funs(prop = ./ Total)) %>%
  mutate_at(vars(ends_with("prop")), round, 3) %>% 
  select(-Total_prop) %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(width = "100%")

ggplot(temporalCounts, aes(x=Spatial, y=count, fill=Type)) + geom_bar(stat="identity", colour="white")

ggplot(temporalCounts, aes(x=Spatial, y=prop, fill=Type)) + geom_bar(stat="identity", colour="white")

```

### Without Undefined

- Again Global is qualitatively different (only Decades and Years studies)
- Continental has nothing shorter than Years
- National and Regional extents have large proportions of Decadal studies
- Micro and Mini extents have largest proprtions of Monthly and Weekly studies

```{r echo=F}
temporalCounts <- spatdata %>%
  select(Spatial, Hours, Days, Weeks, Months, Years, Decades, Centuries, Longer 
) %>%
  mutate(sum = rowSums(.[2:9])) %>%
  gather(key = Type, value = count, -Spatial, -sum) %>%
  mutate(prop = count / sum) 

spatdata %>%
  select(Spatial, Hours, Days, Weeks, Months, Years, Decades, Centuries, Longer 
) %>%
  mutate(Total = rowSums(.[2:9])) %>%
  mutate_if(is.numeric, funs(prop = ./ Total)) %>%
  mutate_at(vars(ends_with("prop")), round, 3) %>% 
  select(-Total_prop) %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(width = "100%")

ggplot(temporalCounts, aes(x=Spatial, y=count, fill=Type)) + geom_bar(stat="identity", colour="white")

ggplot(temporalCounts, aes(x=Spatial, y=prop, fill=Type)) + geom_bar(stat="identity", colour="white")

```

## Concepts

Quick observations:

- Global extents have largest proportions of Ecosystem Services and Climate Change studies
- Continental and National extents have largest proportions of LUCC studies
- Micro extent have largest proportion of Pattern-Process-Scale studies

```{r}
conceptCounts <- spatdata %>%
  select(Spatial, `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(sum = rowSums(.[2:11])) %>%
  gather(key = Type, value = count, -Spatial, -sum) %>%
  mutate(prop = count / sum) 


spatdata %>%
  select(Spatial, `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(Total = rowSums(.[2:11])) %>%
  mutate_if(is.numeric, funs(prop = ./ Total)) %>%
  mutate_at(vars(ends_with("prop")), round, 3) %>% 
  select(-Total_prop) %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(width = "100%")

ggplot(conceptCounts, aes(x=Spatial, y=count, fill=Type)) + geom_bar(stat="identity", colour="white")

ggplot(conceptCounts, aes(x=Spatial, y=prop, fill=Type)) + geom_bar(stat="identity", colour="white")

```


## Other Concepts

Quick observations:

- Smallest extents (micro and mini) have smallest proportions of Socio-Economic Dimensions studies and largest proportions of Biodiversity studies
- Consistent proportions of Management and Conservation studies across all extents


```{r}
otherCounts <- spatdata %>%
  select(Spatial, `Green Infrastructure`,`Planning and Architecture`,`Management and Conservation`,`Cultural Landscapes`,`Socio-economic Dimensions`,Biodiversity,`Landscape Assessment`,`Catchment Based Approach`,`Invasives Pests Diseases`
) %>%
  mutate(sum = rowSums(.[2:10])) %>%
  gather(key = Type, value = count, -Spatial, -sum) %>%
  mutate(prop = count / sum) 

spatdata %>%
  select(Spatial, `Green Infrastructure`,`Planning and Architecture`,`Management and Conservation`,`Cultural Landscapes`,`Socio-economic Dimensions`,Biodiversity,`Landscape Assessment`,`Catchment Based Approach`,`Invasives Pests Diseases`
) %>%
  mutate(Total = rowSums(.[2:10])) %>%
  mutate_if(is.numeric, funs(prop = ./ Total)) %>%
  mutate_at(vars(ends_with("prop")), round, 3) %>% 
  select(-Total_prop) %>%
  kable() %>%
  kable_styling() %>%
  scroll_box(width = "100%")

ggplot(otherCounts, aes(x=Spatial, y=count, fill=Type)) + geom_bar(stat="identity", colour="white")

ggplot(otherCounts, aes(x=Spatial, y=prop, fill=Type)) + geom_bar(stat="identity", colour="white")

```