03-ialeUK25-analysisLandscapeType.Rmd

# Analysis by Landscape Type

Bar charts to examine how contribution to conferences have changed over time.

## Total Conference Contributions

```{r}
#spec(cpdata)

lspdata <- cpdata %>%
  select_if(is.numeric) %>%
  gather(key = LspType, value = count, `Upland rural`:Other) %>%
  filter(count > 0) %>%
  group_by(`LspType`) %>%
  summarise_all(sum, na.rm=T) 
```

Quick observations:

- Lowland rural dominate, followed by 'undefined' and Upland rural

```{r}

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

ggplot(lspdata, aes(x=LspType, y=count)) + 
  geom_bar(stat="identity") + 
  geom_text(aes(x=LspType, y=max(count), label = paste0(round(100*count / sum(count),1), "%"), vjust=-0.25))

```

## Author Affiliation

Quick observations:

- Academic are majority of all landscape types, with possible exception of Upland rural (Government?)
- 

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

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

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

```

## Species
Quick observations

- Animal types quite evenly distributed across Lowland rural
- Humans are large contributor to seascape studies (possibly by absolute number as well as relative)
- Generic habitat is large contributor across all landscape types

```{r}

sppCounts <- lspdata %>%
  select(LspType,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, -LspType, -sum) %>%
  mutate(prop = count / sum)  #calculate proportion

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

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

```

## Methods

Quick observations

- 'Undefined landscape' studies are largely theoretical
- Lowland rural largely studies using empirical and quantitative methods
- Seascape studies have largest proportion of qualitative methods


```{r}

methodsCounts <- lspdata %>%
  select(LspType,Empirical, Theoretical, Qualitative, Quantitative, GIS, `Remote sensing`) %>%
  mutate(sum = rowSums(.[2:7])) %>%   #calculate total for subsquent calcultation of proportion
  gather(key = Type, value = count, -LspType, -sum) %>%
  mutate(prop = count / sum)  #calculate proportion

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

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

```

## Spatial Extent

Quick observations

- Urban landscape studies are dominated by Local scale analysis
- Upland rural have larger proportion of national studies than Lowland rural

```{r}

spatialCounts <- lspdata %>%
  select(LspType,Micro, Mini, Local, Regional, National, Continental, Global,`Undefined Extent`) %>%
  mutate(sum = rowSums(.[2:9])) %>%   #calculate total for subsquent calcultation of proportion
  gather(key = Type, value = count, -LspType, -sum) %>%
  mutate(prop = count / sum)  #calculate proportion

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

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

```

## Temporal Extent

Quick observations

- Upland have greatest proportion of Decadal studies
- Urban have greatest proportion of Monthly studies

```{r}

temporalCounts <- lspdata %>%
  select(LspType,Hours, Days, Weeks, Months, Years, Decades, Centuries, Longer, `Undefined Temporal`) %>%
  mutate(sum = rowSums(.[2:10])) %>%   #calculate total for subsquent calcultation of proportion
  gather(key = Type, value = count, -LspType, -sum) %>%
  mutate(prop = count / sum)  #calculate proportion

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

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

```

## Concepts

Quick observations

- Upland have greatest proportion of LUCC studies
- Seascape have greatest proportion of climate change studies

```{r}

conceptCounts <- lspdata %>%
  select(`LspType`,`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])) %>%   #calculate total for subsquent calcultation of proportion
  gather(key = Type, value = count, -`LspType`, -sum) %>%
  mutate(prop = count / sum)  #calculate proportion

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

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

```

## Other Concepts

Quick observations

- Socio-economic dimensions are most widely examined in Urban landscapes and Seascapes
- Unsurprisingly, Green Infrastructure has greatest proportion in Urban landscapes and catchment-based approaches in Riverscapes
- Little study of cultural landscapes in Urban areas

```{r}

othCCounts <- lspdata %>%
  select(`LspType`,`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])) %>%   #calculate total for subsquent calcultation of proportion
  gather(key = Type, value = count, -`LspType`, -sum) %>%
  mutate(prop = count / sum)  #calculate proportion

ggplot(othCCounts, aes(x=`LspType`, y=count, fill=Type)) + geom_bar(stat="identity", colour="white")

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

```