Merge pull request #111 from wri/dev

Basline indicators - Biodiversity - San jose #103

Author: S-AI-F

baseline-indicators/README.md

@@ -19,13 +19,22 @@ The baseline indicators attempt to provide insightful metrics at the city scale
 
 ## Biodiversity
 
+| Indicator name | Description |Used datasets | Years |
+| ------- | ------- | ------- | ------- | 
+| Number of reported birds | Number of reported birds within the administrative boundaries of the selected city (both metropolitan and municipality levels) | `GBIF`, `geBoundaries` | 2020 |
+| Number of reported birds by order | Number of reported birds broken down by orders within the administrative boundaries of the selected city (both metropolitan and municipality levels) | `GBIF`, `geBoundaries` | 2020 |
+| Number of reported birds by family | Number of reported birds broken down by family within the administrative boundaries of the selected city (both metropolitan and municipality levels) | `GBIF`, `geBoundaries` | 2020 |
+| Number of reported birds by genus | Number of reported birds broken down by genus within the administrative boundaries of the selected city (both metropolitan and municipality levels) | `GBIF`, `geBoundaries` | 2020 |
+| Percent of protected area | For each municipality, percent of land protected as a protected area as defined by IUCN or CBD. | `WDPA`, `geBoundaries` | 2020 |
+
 ## GHG emissions
 
 
 # Key results
 
 | Theme | City | Report |
 | --- | --- |--- |
-| Green space | San-Jose | [Link to the report](https://cities-urbanshift.s3.eu-west-3.amazonaws.com/reports/UrbanShift-Greenspace-Indicators.html) |
-| Green space | Kigali | [Link to the report](https://cities-urbanshift.s3.eu-west-3.amazonaws.com/reports/UrbanShift-Greenspace-Indicators-Kigali.html) |
-| Green space | Freetown | [Link to the report](https://cities-urbanshift.s3.eu-west-3.amazonaws.com/reports/UrbanShift-Greenspace-Indicators-Freetown.html) |
+| Green space | San-Jose | [San-Jose Greenspace basline idicators report](https://cities-urbanshift.s3.eu-west-3.amazonaws.com/reports/UrbanShift-Greenspace-Indicators.html) |
+| Green space | Kigali | [Kigali Greenspace basline idicators report](https://cities-urbanshift.s3.eu-west-3.amazonaws.com/reports/UrbanShift-Greenspace-Indicators-Kigali.html) |
+| Green space | Freetown | [Freetown Greenspace basline idicators report](https://cities-urbanshift.s3.eu-west-3.amazonaws.com/reports/UrbanShift-Greenspace-Indicators-Freetown.html) |
+| Biodiversity | San-Jose | [San-Jose Biodiversity basline idicators report](https://cities-urbanshift.s3.eu-west-3.amazonaws.com/baseline-indicators/biodiversity/reports/UrbanShift-Biodiversity-SanJose.html) |

baseline-indicators/biodiversity/data/boundaries-CRI-San_Jose-ADM2.geojson

@@ -1,5 +1,5 @@
 ---
-title: "UrbanShift Greenspace Indicators"
+title: "UrbanShift Biodiversity Indicators"
 author: "Saif Shabou"
 date: "`r format (Sys.time(), '%d %B %Y')`"
 output: 
@@ -76,7 +76,6 @@ These assessments are intended to provide information to evaluate patterns withi
 
 ## Data sources
 
-### Global Biodiversity Information Facility (GBIF)
 
 - **Global Biodiversity Information Facility (GBIF)**: [The Global Biodiversity Information Facility (GBIF)](https://www.gbif.org/en/) is an international network and data infrastructure funded by the world's governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth. 
 
@@ -86,10 +85,22 @@ These assessments are intended to provide information to evaluate patterns withi
 
 ## Biodiversity indicators
 
+| Indicator name | Description |Used datasets | Years |
+| ------- | ------- | ------- | ------- | 
+| Number of reported birds | Number of reported birds within the administrative boundaries of the selected city (both metropolitan and municipality levels) | `GBIF`, `geBoundaries` | 2020 |
+| Number of reported birds by order | Number of reported birds broken down by orders within the administrative boundaries of the selected city (both metropolitan and municipality levels) | `GBIF`, `geBoundaries` | 2020 |
+| Number of reported birds by family | Number of reported birds broken down by family within the administrative boundaries of the selected city (both metropolitan and municipality levels) | `GBIF`, `geBoundaries` | 2020 |
+| Number of reported birds by genus | Number of reported birds broken down by genus within the administrative boundaries of the selected city (both metropolitan and municipality levels) | `GBIF`, `geBoundaries` | 2020 |
+| Percent of protected area | For each municipality, percent of land protected as a protected area as defined by IUCN or CBD. | `WDPA`, `geBoundaries` | 2020 |
+
 # Case study: San Jose
 
 ## Birds
 
+### Metropolitan level
+
+- **Location of reported birds:**
+
 ```{r Birds_map, warning=FALSE, message=FALSE, echo = FALSE,  eval = TRUE}
 # read Boundary
 boundary = st_read("https://cities-urbanshift.s3.eu-west-3.amazonaws.com/data/boundaries/CRI-San_Jose-boundary.geojson",
@@ -109,7 +120,11 @@ label_birds2020 <- sprintf(
 leaflet(data = boundary, height = 500, width = "100%") %>% 
   addTiles() %>%
   addProviderTiles("OpenStreetMap.France", group = "OSM") %>%
+  addProviderTiles(providers$CartoDB.DarkMatter , group = "CartoDB") %>% 
   # addProviderTiles(providers$Stamen.TonerLite, group = "Toner Lite") %>%
+  # setView(lat = boundary_centroid_lat,
+  #         lng = boundary_centroid_lon,
+  #         zoom = 9) %>% 
   # Add boundaries
   addPolygons(data = boundary,
               group = "Administrative boundaries",
@@ -121,6 +136,12 @@ leaflet(data = boundary, height = 500, width = "100%") %>%
                 dashArray = "",
                 fillOpacity = 0.3,
                 bringToFront = FALSE)) %>%
+  addMarkers(birds2020$lon, 
+             birds2020$lat,
+             popup = ~as.character(birds2020$family),
+             label = label_birds2020,
+             clusterOptions = markerClusterOptions(),
+             group = "Birds clusters") %>% 
   # add birds layer
   addCircleMarkers(birds2020$lon, birds2020$lat, 
                    radius = 3,
@@ -130,5 +151,263 @@ leaflet(data = boundary, height = 500, width = "100%") %>%
                    weight = 0.8,
                    fillOpacity = 0.6,
                    popup = ~as.character(birds2020$family),
-                   label = label_birds2020) 
+                   label = label_birds2020,
+                   group = "Birds observations") %>% 
+  # Layers control
+  addLayersControl(
+    baseGroups = c("OSM","CartoDB"),
+    overlayGroups = c("Birds clusters","Birds observations"),
+    options = layersControlOptions(collapsed = FALSE)
+  ) %>% 
+    hideGroup("Birds observations")
+```
+
+- **Number of reported birds by order:**
+
+```{r Birds_stat_sanjose_orders, warning=FALSE, message=FALSE, echo = FALSE,  eval = TRUE}
+# orders ----
+birds2020_stats_orders = birds2020 %>% 
+  as.data.frame() %>% 
+  group_by(order) %>%
+  summarise(nb_species = n()) %>% 
+  arrange(desc(nb_species)) %>% 
+  dplyr::select(`order name` = order,
+         `Number of species`= nb_species)
+
+# plot chart
+birds2020_stats_orders %>% 
+  plot_ly(height = 500, width = 900) %>% 
+  add_trace(x = ~factor(`order name`), 
+            y = ~`Number of species`, 
+            marker = list(color = col_BoldRiverBlue),
+            type = "bar",
+            orientation = "v")  %>% 
+  layout(title = "Number of birds by order (2020)",
+         xaxis = list(title = '', categoryorder = "array",categoryarray = ~`Number of species`),
+         yaxis = list(title = 'Number of observations in 2020'))
+```
+
+- **Number of reported birds by family:**
+
+```{r Birds_stat_sanjose_family, warning=FALSE, message=FALSE, echo = FALSE,  eval = TRUE}
+# families ----
+
+birds2020_stats_families = birds2020 %>% 
+  as.data.frame() %>% 
+  group_by(family) %>%
+  summarise(nb_species = n()) %>% 
+  arrange(desc(nb_species)) %>% 
+  dplyr::select(`family name` = family,
+                `Number of species`= nb_species)
+
+# plot chart
+birds2020_stats_families %>% 
+  plot_ly(height = 500, width = 900) %>% 
+  add_trace(x = ~factor(`family name`), 
+            y = ~`Number of species`, 
+            marker = list(color = col_BoldRiverBlue),
+            type = "bar",
+            orientation = "v")  %>% 
+  layout(title = "Number of birds by family (2020)",
+         xaxis = list(title = '', categoryorder = "array",categoryarray = ~`Number of species`),
+         yaxis = list(title = 'Number of observations in 2020'))
+         
+```
+
+- **Number of reported birds by genus:**
+
+```{r Birds_stat_sanjose_genus, warning=FALSE, message=FALSE, echo = FALSE,  eval = TRUE}
+# genus ----
+
+birds2020_stats_genus = birds2020 %>% 
+  as.data.frame() %>% 
+  group_by(genus) %>%
+  summarise(nb_species = n()) %>% 
+  arrange(desc(nb_species)) %>% 
+  dplyr::select(`genus name` = genus,
+                `Number of species`= nb_species)
+
+# plot table
+birds2020_stats_genus %>% 
+  arrange(desc(`Number of species`)) %>% 
+  kable() %>%
+  kable_styling(bootstrap_options = c("striped", "hover"), full_width = F, font_size = 13)%>% 
+  scroll_box(width = "100%", height = "400px")
+         
+```
+
+### Municipality level
+
+```{r Birds_map_adm2, warning=FALSE, message=FALSE, echo = FALSE,  eval = TRUE}
+boundary_municipality = st_read("https://cities-urbanshift.s3.eu-west-3.amazonaws.com/baseline-indicators/biodiversity/data/boundaries-CRI-San_Jose-ADM2.geojson",
+                                quiet = TRUE)
+
+# find birds observations within municipalities polygons
+birds_in_geo_adm2 <- st_join(x = birds2020, 
+                             y = boundary_municipality, 
+                             join = st_within)
+
+# agregate number of birds observations by municipality 
+birds_in_geo_adm2_stat = birds_in_geo_adm2 %>% 
+  as.data.frame() %>% 
+  count(shapeName.1) %>% 
+  dplyr::select(shapeName.1,
+         nb_species = n)  
+  
+# join with geo
+birds_adm2_stat_geo = boundary_municipality %>% 
+  left_join(birds_in_geo_adm2_stat,
+            by = c("shapeName.1"))
+
+# prepare map
+
+# define color palette for urban expansion levels
+pal_nbspecieis_municipality <- colorNumeric(palette = "Reds", 
+                                             domain = birds_adm2_stat_geo$nb_species,
+                                             na.color = "transparent",
+                                             revers = FALSE)
+
+# define map labels
+labels_adm2_nbspecies <- sprintf("<strong>%s</strong><br/>%s: %s %s",
+                  birds_adm2_stat_geo$shapeName.1, 
+                  "Observations: ",
+                  round(birds_adm2_stat_geo$nb_species, 2), "") %>% 
+  lapply(htmltools::HTML)
+
+
+# plot map
+leaflet(height = 500, width = "100%") %>%
+  addTiles() %>%
+  addProviderTiles(providers$CartoDB.DarkMatter , group = "CartoDB") %>%
+  addProviderTiles("OpenStreetMap.France", group = "OSM") %>%
+  addPolygons(data = birds_adm2_stat_geo,
+              group = "Nb species",
+              fillColor = ~pal_nbspecieis_municipality(nb_species),
+              weight = 1,
+              opacity = 1,
+              color = "grey",
+              fillOpacity = 0.7,
+              label = labels_adm2_nbspecies,
+              highlightOptions = highlightOptions(color = "black", weight = 2,
+                                                  bringToFront = FALSE),
+              labelOptions = labelOptions(
+                style = list("font-weight" = "normal", padding = "3px 6px"),
+                textsize = "15px",
+                direction = "auto")) %>% 
+  addLegend(pal = pal_nbspecieis_municipality, 
+            values = birds_adm2_stat_geo$nb_species, 
+            opacity = 0.9, 
+            title = "Number of species (2020)",
+            position = "topright",
+            labFormat = labelFormat(suffix = "")) %>%
+  # add birds layer
+  addCircleMarkers(birds2020$lon, birds2020$lat,
+                   radius = 3,
+                   fillColor = col_BoldRiverBlue,
+                   color  = "black",
+                   stroke = TRUE,
+                   weight = 0.8,
+                   fillOpacity = 0.6,
+                   label = label_birds2020,
+                   group = "Birds observations") %>% 
+  # Layers control
+  addLayersControl(
+    baseGroups = c("CartoDB","OSM"),
+    overlayGroups = c("Nb species", "Birds observations"),
+    options = layersControlOptions(collapsed = FALSE)
+  ) %>% 
+  hideGroup("Birds observations")
+```
+
+```{r Birds_barplot_adm2, warning=FALSE, message=FALSE, echo = FALSE,  eval = TRUE}
+# plot chart
+birds_in_geo_adm2_stat %>% 
+  arrange(desc(nb_species)) %>%
+  plot_ly(height = 500, width = 900) %>% 
+  add_trace(x = ~factor(shapeName.1), 
+            y = ~nb_species, 
+            marker = list(color = col_BoldRiverBlue),
+            type = "bar",
+            orientation = "v")  %>% 
+  layout(title = "Number of birds by municipality (2020)",
+         xaxis = list(title = '', categoryorder = "array",categoryarray = ~nb_species),
+         yaxis = list(title = 'Number of observations in 2020'))
 ```
+
+
+## Prrtected areas
+
+```{r protected_area_map, warning=FALSE, message=FALSE, echo = FALSE,  eval = TRUE}
+# protected areas map -----
+protected_areas_sanjose = st_read("https://cities-urbanshift.s3.eu-west-3.amazonaws.com/baseline-indicators/biodiversity/data/biodiversity_map_layers_protected_areas_sanjose.geojson",
+                                    quiet = TRUE)
+
+leaflet(data = boundary, height = 500, width = "100%") %>% 
+  addTiles() %>%
+  addProviderTiles(providers$Stamen.TonerLite, group = "Toner Lite") %>%
+  addProviderTiles("OpenStreetMap.France", group = "OSM") %>%
+  addProviderTiles(providers$CartoDB.DarkMatter , group = "CartoDB") %>% 
+  # Add boundaries: metropolitan
+  addPolygons(data = boundary,
+              group = "Administrative boundaries",
+              stroke = TRUE, color = col_Black, weight = 3,dashArray = "3",
+              smoothFactor = 0.3, fill = FALSE, fillOpacity = 0.2,
+              highlight = highlightOptions(
+                weight = 5,
+                color = "#666",
+                dashArray = "",
+                fillOpacity = 0.3,
+                bringToFront = FALSE)) %>%
+  # Add boundaries: Municipality
+  addPolygons(data = boundary_municipality,
+              group = "Municipality boundaries",
+              stroke = TRUE, color = "gray", weight = 3,dashArray = "3",
+              smoothFactor = 0.3, fill = TRUE, fillOpacity = 0.2,
+              highlight = highlightOptions(
+                weight = 5,
+                color = "#666",
+                dashArray = "",
+                fillOpacity = 0.3,
+                bringToFront = FALSE)) %>%
+  # add protected areas layer
+  addPolygons(data = protected_areas_sanjose,
+              group = "Protected Areas",
+              stroke = TRUE, color = col_BoldGrassGreen, weight = 3,dashArray = "3",
+              smoothFactor = 0.3, fill = TRUE, fillOpacity = 0.8,
+              highlight = highlightOptions(
+                weight = 5,
+                color = col_LightGrassGreen,
+                dashArray = "",
+                fillOpacity = 0.3,
+                bringToFront = FALSE)) %>% 
+  # Layers control
+  addLayersControl(
+    baseGroups = c("Toner Lite","OSM","CartoDB"),
+    overlayGroups = c("Municipality boundaries", "Protected Areas"),
+    options = layersControlOptions(collapsed = FALSE)
+  ) %>% 
+  hideGroup("Municipality boundaries")
+```
+
+
+```{r protected_area_barplot, warning=FALSE, message=FALSE, echo = FALSE,  eval = TRUE}
+protectedarea_percent_by_municipality = read.csv("https://cities-urbanshift.s3.eu-west-3.amazonaws.com/baseline-indicators/biodiversity/data/protectedarea_percent_by_municipality.csv")
+
+# rename columns
+protectedarea_percent_by_municipality = protectedarea_percent_by_municipality %>% 
+  rename_with(.cols = 1, ~'municpality_name') %>% 
+  rename_with(.cols = 2, ~'percent_protected_area')
+
+# plot chart
+protectedarea_percent_by_municipality %>% 
+  arrange(desc(percent_protected_area)) %>%
+  plot_ly(height = 500, width = 900) %>% 
+  add_trace(x = ~factor(municpality_name), 
+            y = ~percent_protected_area, 
+            marker = list(color = col_BoldRiverBlue),
+            type = "bar",
+            orientation = "v")  %>% 
+  layout(title = "Percent of protected area by municipality (2020)",
+         xaxis = list(title = '', categoryorder = "array",categoryarray = ~percent_protected_area),
+         yaxis = list(title = 'Percent of protected area (%)'))
+```