Ready for huggingface!

app.R → R/old_poc/app_20250110.R

@@ -1,3 +1,8 @@
+# truncate the name 
+# Geocoder shiny all -> Adapt !!!
+
+
+
 # Get working directory, perhaps shiny apps is not receiving the data and the www? 
 # rsconnect::setAccountInfo(name='diego-ellis-soto', token='A47BE3C9E4B9EBCDFEC889AF31F64154', secret='g2Q2rxeYCiwlH81EkPXcCGsiHMgdyhTznJRmHtea')
 # deployApp()
@@ -7,7 +12,7 @@
 
 # Optimize some calculations? Shorten 
 
-
+# Look at code human facets or relate social vulnerabiltiy income 
 
 
 
@@ -17,7 +22,7 @@
 # University of California Berkeley, ESPM
 # California Academy of Sciences
 ###############################################################################
-
+require(shinyjs)
 library(shiny)
 library(leaflet)
 library(mapboxapi)
@@ -31,57 +36,29 @@ library(data.table)  # for fread
 library(mapview)     # for mapview objects
 library(sjPlot)      # for plotting lm model coefficients
 library(sjlabelled)  # optional if needed for sjPlot
-
-# ------------------------------------------------
-# 1) API Keys
-# ------------------------------------------------
-mapbox_token <- "pk.eyJ1Ijoia3dhbGtlcnRjdSIsImEiOiJjbHc3NmI0cDMxYzhyMmt0OXBiYnltMjVtIn0.Thtu6WqIhOfin6AykskM2g" 
-mb_access_token(mapbox_token, install = FALSE)
-
-# ------------------------------------------------
-# 2) Load Data
-# ------------------------------------------------
-# -- Greenspace
-getwd()
-osm_greenspace <- st_read("data/greenspaces_osm_nad83.shp", quiet = TRUE) %>%
-  st_transform(4326)
-if (!"name" %in% names(osm_greenspace)) {
-  osm_greenspace$name <- "Unnamed Greenspace"
-}
-
-# -- NDVI Raster
-ndvi <- rast("data/SF_EastBay_NDVI_Sentinel_10.tif")
-
-# -- GBIF data
-# Load what is basically inter_gbif !!!!! 
-# load("data/sf_gbif.Rdata")  # => sf_gbif
-load('data/gbif_census_ndvi_anno.Rdata')
-vect_gbif <- vect(sf_gbif)
-# -- Precomputed CBG data
-load('data/cbg_vect_sf.Rdata')
-if (!"unique_species" %in% names(cbg_vect_sf)) {
-  cbg_vect_sf$unique_species <- cbg_vect_sf$n_species
-}
-if (!"n_observations" %in% names(cbg_vect_sf)) {
-  cbg_vect_sf$n_observations <- cbg_vect_sf$n
-}
-if (!"median_inc" %in% names(cbg_vect_sf)) {
-  cbg_vect_sf$median_inc <- cbg_vect_sf$medincE
-}
-if (!"ndvi_mean" %in% names(cbg_vect_sf)) {
-  cbg_vect_sf$ndvi_mean <- cbg_vect_sf$ndvi_sentinel
-}
-
-# -- Hotspots/Coldspots
-biodiv_hotspots  <- st_read("data/hotspots.shp",  quiet = TRUE) %>% st_transform(4326)
-biodiv_coldspots <- st_read("data/coldspots.shp", quiet = TRUE) %>% st_transform(4326)
+require(bslib)
+require(shinycssloaders)
+source('R/setup.R')
+# Global theme definition
+theme <- bs_theme(
+  bootswatch = "flatly",
+  base_font = font_google("Roboto"),
+  heading_font = font_google("Roboto Slab"),
+  bg = "#f8f9fa",
+  fg = "#212529"
+)
 
 # ------------------------------------------------
 # 3) UI
 # ------------------------------------------------
 ui <- fluidPage(
-  titlePanel("San Francisco Biodiversity Access Decision Support Tool"),
+  theme = theme, # Introduce a theme from bslib
   
+  # For dynamically show and hide a 'Calculating' message
+  useShinyjs(),  # Initialize shinyjs
+  div(id = "loading", style = "display:none; font-size: 20px; color: red;", "Calculating..."),
+  titlePanel("San Francisco Biodiversity Access Decision Support Tool"),
+  p('Explore your local biodiversity and your access to it!'),
   fluidRow(
     column(
       width = 12, align = "center",
@@ -91,7 +68,8 @@ ui <- fluidPage(
                height = "120px", style = "margin:10px;"),
       tags$img(src = "Reimagining_San_Francisco.png", 
                height = "120px", style = "margin:10px;")
-    )
+    ),
+    theme=bs_theme(bootswatch='yeti')
   ),
   
   fluidRow(
@@ -126,7 +104,9 @@ ui <- fluidPage(
     )
   ),
   br(),
-  
+  # fluidRow(
+  #   column(
+  #     width = 6 , # quitar
   tabsetPanel(
     
     # 1) Isochrone Explorer
@@ -180,32 +160,40 @@ ui <- fluidPage(
                    column(12,
                           br(),
                           uiOutput("bioScoreBox"),
+                          br(),
                           uiOutput("closestGreenspaceUI")
                    )
                  ),
                  
                  br(),
-                 DTOutput("dataTable"),
+                 DTOutput("dataTable") %>% withSpinner(type = 8, color = "#337ab7"),
                  
+                 br(),
                  br(),
                  fluidRow(
                    column(12,
-                          plotOutput("bioSocPlot", height = "400px")
+                          plotOutput("bioSocPlot", height = "400px") %>% withSpinner(type = 8, color = "#337ab7")
                    )
                  ),
                  
+                 br(),
+                 br(),
                  br(),
                  fluidRow(
                    column(12,
-                          plotOutput("collectionPlot", height = "300px")
+                          plotOutput("collectionPlot", height = "400px") %>% withSpinner(type = 8, color = "#f39c12")
                    )
                  )
                )
              )
     ),
     
+  
+  # ), # end of column wifth
     #br.?
-    tabPanel(
+  # column(
+  #   width=6,
+     tabPanel(
       "GBIF Summaries",
       sidebarLayout(
         sidebarPanel(
@@ -226,11 +214,16 @@ ui <- fluidPage(
           DTOutput("classTable"),
           br(),
           h3("Observations vs. Species Richness"),
-          plotOutput("obsVsSpeciesPlot", height = "400px"),
+          plotOutput("obsVsSpeciesPlot", height = "300px"),
           p("This plot displays the relationship between the number of observations and the species richness. Use this visualization to understand data coverage and biodiversity trends.")
         )
       )
-    ),
+    )  %>% withSpinner(type = 8, color = "#337ab7")
+  ),
+  # )
+  
+    # ),
+    
     fluidRow(
       column(
         width = 12,
@@ -317,7 +310,7 @@ ui <- fluidPage(
   #     )
   #   )
   # )
-)
+
 
 # ------------------------------------------------
 # 4) Server
@@ -955,7 +948,7 @@ server <- function(input, output, session) {
       geom_point(aes(y = EstimatedPopulation / 1000), color = "red", size = 3) +
       labs(
         x = "Isochrone (Mode-Time)",
-        y = "Blue bars: Unique Species \n | Red line: Population (thousands)",
+        y = "Unique Species (Blue) \n | Population (Red) (thousands)",
         title = "Biodiversity & Socioeconomic Summary"
       ) +
       theme_minimal(base_size = 14) +
@@ -997,12 +990,13 @@ server <- function(input, output, session) {
       st_drop_geometry() %>%
       group_by(institutionCode) %>%
       summarize(count = n(), .groups = "drop") %>%
-      arrange(desc(count))
+      arrange(desc(count)) %>%
+      mutate(truncatedCode = substr(institutionCode, 1, 5)) # Shorter version of the names 
     
-    ggplot(df_code, aes(x = reorder(institutionCode, -count), y = count)) +
+    ggplot(df_code, aes(x = reorder(truncatedCode, -count), y = count)) + # replaced institutionCode with trunacedCode
       geom_bar(stat = "identity", fill = "darkorange", alpha = 0.7) +
       labs(
-        x = "Institution Code",
+        x = "Institution Code (Truncoded)",
         y = "Number of Records",
         title = "GBIF Records by Institution Code (Isochrone Union)"
       ) +
@@ -1028,21 +1022,55 @@ server <- function(input, output, session) {
     map_s@map
   })
   
+  
+  
+  
+
+  
+
+  
+  
   # ------------------------------------------------
   # Additional Plot: n_observations vs n_species
   # ------------------------------------------------
+  
+  # Make it reactive: obsVsSpeciesPlot updates dynamically based on user-selected class_filter or family_filter. 
+  
+  filtered_data <- reactive({
+    data <- cbg_vect_sf
+    if (input$class_filter != "All") {
+      data <- data[data$class == input$class_filter, ]
+    }
+    if (input$family_filter != "All") {
+      data <- data[data$family == input$family_filter, ]
+    }
+    data
+  })
+
   output$obsVsSpeciesPlot <- renderPlot({
-    # A simple scatter plot of n_observations vs. n_species from cbg_vect_sf
-    ggplot(cbg_vect_sf, aes(x = log(n_observations+1), y = log(unique_species+1)) ) +
+    data <- filtered_data()
+    ggplot(data, aes(x = log(n_observations + 1), y = log(unique_species + 1))) +
       geom_point(color = "blue", alpha = 0.6) +
       labs(
-        x = "Number of Observations (n_observations)",
-        y = "Number of Species (n_species)",
-        title = "Data Availability vs. Species Richness"
+        x = "Log(Number of Observations)",
+        y = "Log(Species Richness)",
+        title = "Filtered Data Availability vs. Species Richness"
       ) +
       theme_minimal(base_size = 14)
   })
   
+  # output$obsVsSpeciesPlot <- renderPlot({
+  #   # A simple scatter plot of n_observations vs. n_species from cbg_vect_sf
+  #   ggplot(cbg_vect_sf, aes(x = log(n_observations+1), y = log(unique_species+1)) ) +
+  #     geom_point(color = "blue", alpha = 0.6) +
+  #     labs(
+  #       x = "Number of Observations (n_observations)",
+  #       y = "Number of Species (n_species)",
+  #       title = "Data Availability vs. Species Richness"
+  #     ) +
+  #     theme_minimal(base_size = 14)
+  # })
+  
   # ------------------------------------------------
   # Additional Plot: Linear model of n_species ~ n_observations + median_inc + ndvi_mean
   # ------------------------------------------------
@@ -1073,9 +1101,10 @@ server <- function(input, output, session) {
 }
 
 shinyApp(ui, server)
-
+# run_with_themer(shinyApp(ui, server))
 # library(profvis)
 # 
 # profvis({
 #   shinyApp(ui, server)
-# })
+# })
+

R/old_poc/app_works_no_shinydashboard.R

@@ -0,0 +1,1022 @@
+###############################################################################
+# Shiny App: San Francisco Biodiversity Access Decision Support Tool
+# Author: Diego Ellis Soto, et al.
+# University of California Berkeley, ESPM
+# California Academy of Sciences
+###############################################################################
+require(shinyjs)
+library(shiny)
+library(leaflet)
+library(mapboxapi)
+library(tidyverse)
+library(tidycensus)
+library(sf)
+library(DT)
+library(RColorBrewer)
+library(terra)       
+library(data.table)  # for fread
+library(mapview)     # for mapview objects
+library(sjPlot)      # for plotting lm model coefficients
+library(sjlabelled)  # optional if needed for sjPlot
+require(bslib)
+require(shinycssloaders)
+
+source('R/setup.R') # Ensure this script loads necessary data objects
+
+# Define your Mapbox token securely
+mapbox_token <- "pk.eyJ1Ijoia3dhbGtlcnRjdSIsImEiOiJjbHc3NmI0cDMxYzhyMmt0OXBiYnltMjVtIn0.Thtu6WqIhOfin6AykskM2g" 
+
+# Global theme definition
+theme <- bs_theme(
+  bootswatch = "flatly",
+  base_font = font_google("Roboto"),
+  heading_font = font_google("Roboto Slab"),
+  bg = "#f8f9fa",
+  fg = "#212529"
+)
+
+# ------------------------------------------------
+# 3) UI
+# ------------------------------------------------
+ui <- fluidPage(
+  theme = theme, # Introduce a theme from bslib
+  
+  # For dynamically show and hide a 'Calculating' message
+  useShinyjs(),  # Initialize shinyjs
+  div(id = "loading", style = "display:none; font-size: 20px; color: red;", "Calculating..."),
+  
+  titlePanel("San Francisco Biodiversity Access Decision Support Tool"),
+  p('Explore your local biodiversity and your access to it!'),
+  
+  fluidRow(
+    column(
+      width = 12, align = "center",
+      tags$img(src = "www/UC Berkeley_logo.png", 
+               height = "120px", style = "margin:10px;"),
+      tags$img(src = "www/California_academy_logo.png", 
+               height = "120px", style = "margin:10px;"),
+      tags$img(src = "www/Reimagining_San_Francisco.png", 
+               height = "120px", style = "margin:10px;")
+    ),
+    theme=bs_theme(bootswatch='yeti')
+  ),
+  
+  fluidRow(
+    column(
+      width = 12,
+      br(),
+      tags$b("App Summary (Fill out with RSF data working group):"),
+      p("
+        This application allows users to either click on a map or geocode an address 
+        to generate travel-time isochrones across multiple transportation modes 
+        (e.g., pedestrian, cycling, driving, driving during traffic).
+        It retrieves socio-economic data from precomputed Census variables, calculates NDVI, 
+        and summarizes biodiversity records from GBIF. Users can explore information 
+        related to biodiversity in urban environments, including greenspace coverage, 
+        population estimates, and species diversity within each isochrone.
+      "),
+      
+      tags$b("Created by:"),
+      p(strong("Diego Ellis Soto", "Carl Boettiger, Rebecca Johnson, Christopher J. Schell")),
+      
+      p("Contact Information: ", strong("[email protected]"))
+    )
+  ),
+  
+  br(),
+  
+  # Tabbed Interface
+  tabsetPanel(
+    # 1) Isochrone Explorer Tab
+    tabPanel("Isochrone Explorer",
+             sidebarLayout(
+               sidebarPanel(
+                 radioButtons(
+                   "location_choice", 
+                   "Select how to choose your location:",
+                   choices = c("Address (Geocode)" = "address", 
+                               "Click on Map"      = "map_click"),
+                   selected = "map_click"  
+                 ),
+                 
+                 conditionalPanel(
+                   condition = "input.location_choice == 'address'",
+                   mapboxGeocoderInput(
+                     inputId = "geocoder",
+                     placeholder = "Search for an address",
+                     access_token = mapbox_token
+                   )
+                 ),
+                 
+                 checkboxGroupInput(
+                   "transport_modes", 
+                   "Select Transportation Modes:",
+                   choices = list("Driving"             = "driving",
+                                  "Walking"             = "walking",
+                                  "Cycling"             = "cycling",
+                                  "Driving with Traffic"= "driving-traffic"),
+                   selected = c("driving", "walking")
+                 ),
+                 
+                 checkboxGroupInput(
+                   "iso_times", 
+                   "Select Isochrone Times (minutes):",
+                   choices = list("5" = 5, "10" = 10, "15" = 15),
+                   selected = c(5, 10)
+                 ),
+                 
+                 actionButton("generate_iso", "Generate Isochrones"),
+                 actionButton("clear_map", "Clear")
+               ),
+               
+               mainPanel(
+                 leafletOutput("isoMap", height = 600),
+                 
+                 fluidRow(
+                   column(12,
+                          br(),
+                          uiOutput("bioScoreBox"),
+                          br(),
+                          uiOutput("closestGreenspaceUI")
+                   )
+                 ),
+                 
+                 br(),
+                 DTOutput("dataTable") %>% withSpinner(type = 8, color = "#337ab7"),
+                 
+                 br(),
+                 br(),
+                 fluidRow(
+                   column(12,
+                          plotOutput("bioSocPlot", height = "400px") %>% withSpinner(type = 8, color = "#337ab7")
+                   )
+                 ),
+                 
+                 br(),
+                 br(),
+                 br(),
+                 fluidRow(
+                   column(12,
+                          plotOutput("collectionPlot", height = "400px") %>% withSpinner(type = 8, color = "#f39c12")
+                   )
+                 )
+               )
+             )
+    ),
+    
+    # 2) GBIF Summaries Tab
+    tabPanel(
+      "GBIF Summaries",
+      sidebarLayout(
+        sidebarPanel(
+          selectInput(
+            "class_filter",
+            "Select a GBIF Class to Summarize:",
+            choices = c("All", sort(unique(sf_gbif$class))), 
+            selected = "All"
+          ),
+          selectInput(
+            "family_filter",
+            "Filter by Family (optional):",
+            choices = c("All", sort(unique(sf_gbif$family))),
+            selected = "All"
+          )
+        ),
+        mainPanel(
+          DTOutput("classTable"),
+          br(),
+          h3("Observations vs. Species Richness"),
+          plotOutput("obsVsSpeciesPlot", height = "300px"),
+          p("This plot displays the relationship between the number of observations and the species richness. Use this visualization to understand data coverage and biodiversity trends.")
+        )
+      )
+    )  %>% withSpinner(type = 8, color = "#337ab7")
+  ),
+  
+  # Additional Information and Next Steps
+  fluidRow(
+    column(
+      width = 12,
+      tags$b("Reimagining San Francisco (Fill out with CAS):"),
+      p("Reimagining San Francisco is an initiative aimed at integrating ecological, social, 
+         and technological dimensions to shape a sustainable future for the Bay Area. 
+         This collaboration unites diverse stakeholders to explore innovations in urban planning, 
+         conservation, and community engagement. The Reimagining San Francisco Data Working Group has been tasked with identifying and integrating multiple sources of socio-ecological biodiversity information in a co-development framework."),
+      
+      tags$b("Why Biodiversity Access Matters (Polish this):"),
+      p("Ensuring equitable access to biodiversity is essential for human well-being, 
+         ecological resilience, and global policy decisions related to conservation. 
+         Areas with higher biodiversity can support ecosystem services including pollinators, moderate climate extremes, 
+         and provide cultural, recreational, and health benefits to local communities. 
+         Recognizing that cities are particularly complex socio-ecological systems facing both legacies of sociocultural practices as well as current ongoing dynamic human activities and pressures.
+         Incorporating multiple facets of biodiversity metrics alongside variables employed by city planners, human geographers, and decision-makers into urban planning will allow a more integrative lens in creating a sustainable future for cities and their residents."),
+      
+      tags$b("How We Calculate Biodiversity Access Percentile:"),
+      p("Total unique species found within the user-generated isochrone. 
+         We then compare that value to the distribution of unique species counts across all census block groups, 
+         converting that comparison into a percentile ranking (Polish this, look at the 15 Minute city). 
+         A higher percentile indicates greater biodiversity within the chosen area, 
+         relative to other parts of the city or region.")
+    ),
+    
+    tags$b("Next Steps:"),
+    tags$ul(
+      tags$li("Add impervious surface"),
+      tags$li("National walkability score"),
+      tags$li("Social vulnerability score"),
+      tags$li("NatureServe biodiversity maps"),
+      tags$li("Calculate cold-hotspots within aggregation of H6 bins instead of by census block group: Ask Carl"),
+      tags$li("Species range maps"),
+      tags$li("Add common name GBIF"),
+      tags$li("Partner orgs"),
+      tags$li("Optimize speed -> store variables -> H-ify the world?"),
+      tags$li("Brainstorm and co-develop the biodiversity access score"),
+      tags$li("For the GBIF summaries, add an annotated GBIF_sf with environmental variables so we can see landcover type association across the biodiversity within the isochrone.")
+    )
+  )
+)
+
+# ------------------------------------------------
+# 4) Server
+# ------------------------------------------------
+server <- function(input, output, session) {
+  
+  chosen_point <- reactiveVal(NULL)
+  
+  # ------------------------------------------------
+  # Leaflet Base + Hide Overlays
+  # ------------------------------------------------
+  output$isoMap <- renderLeaflet({
+    pal_cbg <- colorNumeric("YlOrRd", cbg_vect_sf$medincE)
+    
+    pal_rich <- colorNumeric("YlOrRd", domain = cbg_vect_sf$unique_species)
+    # 2) Color palette for data availability
+    pal_data <- colorNumeric("Blues", domain = cbg_vect_sf$n_observations)
+    
+    leaflet() %>%
+      addTiles(group = "Street Map (Default)") %>%
+      addProviderTiles(providers$Esri.WorldImagery, group = "Satellite (ESRI)") %>%
+      addProviderTiles(providers$CartoDB.Positron, group = "CartoDB.Positron") %>%
+      
+      addPolygons(
+        data = cbg_vect_sf,
+        group = "Income",
+        fillColor = ~pal_cbg(medincE),
+        fillOpacity = 0.6,
+        color = "white",
+        weight = 1,
+        label=~medincE,
+        highlightOptions = highlightOptions(
+          weight = 5,
+          color = "blue",
+          fillOpacity = 0.5,
+          bringToFront = TRUE
+        ),
+        labelOptions = labelOptions(
+          style = list("font-weight" = "bold", "color" = "blue"),
+          textsize = "12px",
+          direction = "auto"
+        )
+      ) %>%
+      
+      addPolygons(
+        data = osm_greenspace,
+        group = "Greenspace",
+        fillColor = "darkgreen",
+        fillOpacity = 0.3,
+        color = "green",
+        weight = 1,
+        label = ~name,
+        highlightOptions = highlightOptions(
+          weight = 5,
+          color = "blue",
+          fillOpacity = 0.5,
+          bringToFront = TRUE
+        ),
+        labelOptions = labelOptions(
+          style = list("font-weight" = "bold", "color" = "blue"),
+          textsize = "12px",
+          direction = "auto",
+          noHide = FALSE # Labels appear on hover
+        )
+      ) %>%
+      
+      addPolygons(
+        data = biodiv_hotspots,
+        group = "Hotspots (KnowBR)",
+        fillColor = "firebrick",
+        fillOpacity = 0.2,
+        color = "firebrick",
+        weight = 2,
+        label = ~GEOID,
+        highlightOptions = highlightOptions(
+          weight = 5,
+          color = "blue",
+          fillOpacity = 0.5,
+          bringToFront = TRUE
+        ),
+        labelOptions = labelOptions(
+          style = list("font-weight" = "bold", "color" = "blue"),
+          textsize = "12px",
+          direction = "auto"
+        )
+      ) %>%
+      
+      addPolygons(
+        data = biodiv_coldspots,
+        group = "Coldspots (KnowBR)",
+        fillColor = "navyblue",
+        fillOpacity = 0.2,
+        color = "navyblue",
+        weight = 2,
+        label = ~GEOID,
+        highlightOptions = highlightOptions(
+          weight = 5,
+          color = "blue",
+          fillOpacity = 0.5,
+          bringToFront = TRUE
+        ),
+        labelOptions = labelOptions(
+          style = list("font-weight" = "bold", "color" = "blue"),
+          textsize = "12px",
+          direction = "auto"
+        )
+      ) %>%
+      
+      # Add richness and nobs
+      # -- Richness layer
+      addPolygons(
+        data = cbg_vect_sf,
+        group = "Species Richness",
+        fillColor = ~pal_rich(unique_species),
+        fillOpacity = 0.6,
+        color = "white",
+        weight = 1,
+        label =~unique_species,
+        popup = ~paste0(
+          "<strong>GEOID: </strong>", GEOID,
+          "<br><strong>Species Richness: </strong>", unique_species,
+          "<br><strong>Observations: </strong>", n_observations,
+          "<br><strong>Median Income: </strong>", median_inc,
+          "<br><strong>Mean NDVI: </strong>", ndvi_mean
+        )
+      ) %>%
+      
+      # -- Data Availability layer
+      addPolygons(
+        data = cbg_vect_sf,
+        group = "Data Availability",
+        fillColor = ~pal_data(n_observations),
+        fillOpacity = 0.6,
+        color = "white",
+        weight = 1,
+        label =~n_observations,
+        popup = ~paste0(
+          "<strong>GEOID: </strong>", GEOID,
+          "<br><strong>Observations: </strong>", n_observations,
+          "<br><strong>Species Richness: </strong>", unique_species,
+          "<br><strong>Median Income: </strong>", median_inc,
+          "<br><strong>Mean NDVI: </strong>", ndvi_mean
+        )
+      ) %>%
+      
+      
+      setView(lng = -122.4194, lat = 37.7749, zoom = 12) %>%
+      addLayersControl(
+        baseGroups    = c("Street Map (Default)", "Satellite (ESRI)", "CartoDB.Positron"),
+        overlayGroups = c("Income", "Greenspace","Species Richness", "Data Availability", 
+                          "Hotspots (KnowBR)", "Coldspots (KnowBR)"),
+        options       = layersControlOptions(collapsed = FALSE)
+      ) %>%
+      hideGroup("Income") %>%
+      hideGroup("Greenspace") %>%
+      hideGroup("Hotspots (KnowBR)") %>%
+      hideGroup("Coldspots (KnowBR)") %>%
+      hideGroup("Species Richness") %>%
+      hideGroup("Data Availability")
+  })
+  
+  
+  # ------------------------------------------------
+  # Observe map clicks (location_choice = 'map_click')
+  # ------------------------------------------------
+  observeEvent(input$isoMap_click, {
+    req(input$location_choice == "map_click")
+    click <- input$isoMap_click
+    if (!is.null(click)) {
+      chosen_point(c(lon = click$lng, lat = click$lat))
+      
+      # Provide feedback with coordinates
+      showNotification(
+        paste0("Map clicked at Longitude: ", round(click$lng, 5), 
+               ", Latitude: ", round(click$lat, 5)),
+        type = "message"
+      )
+      
+      # Update the map with a marker
+      leafletProxy("isoMap") %>%
+        clearMarkers() %>%
+        addCircleMarkers(
+          lng = click$lng, lat = click$lat,
+          radius = 6, color = "firebrick",
+          label = "Map Click Location"
+        )
+    }
+  })
+  
+  # ------------------------------------------------
+  # Observe geocoder input
+  # ------------------------------------------------
+  observeEvent(input$geocoder, {
+    req(input$location_choice == "address")
+    geocode_result <- input$geocoder
+    if (!is.null(geocode_result)) {
+      # Extract coordinates
+      xy <- geocoder_as_xy(geocode_result)
+      
+      # Update the chosen_point reactive value
+      chosen_point(c(lon = xy[1], lat = xy[2]))
+      
+      # Provide feedback with the geocoded address and coordinates
+      showNotification(
+        paste0("Address geocoded to Longitude: ", round(xy[1], 5), 
+               ", Latitude: ", round(xy[2], 5)),
+        type = "message"
+      )
+      
+      # Update the map with a marker
+      leafletProxy("isoMap") %>%
+        clearMarkers() %>%
+        addCircleMarkers(
+          lng = xy[1], lat = xy[2],
+          radius = 6, color = "navyblue",
+          label = "Geocoded Address"
+        ) %>%
+        flyTo(lng = xy[1], lat = xy[2], zoom = 13)
+    }
+  })
+  
+  # ------------------------------------------------
+  # Observe clearing of map
+  # ------------------------------------------------
+  observeEvent(input$clear_map, {
+    # Reset the chosen point
+    chosen_point(NULL)
+    
+    # Clear all markers and isochrones from the map
+    leafletProxy("isoMap") %>%
+       clearMarkers() %>%
+      # clearShapes() %>%
+      clearGroup("Isochrones") %>%
+      clearGroup("NDVI Raster")
+    
+    # Optional: Reset any other reactive values if needed
+    showNotification("Map cleared. You can select a new location.")
+  })
+  
+  # ------------------------------------------------
+  # Generate Isochrones
+  # ------------------------------------------------
+  isochrones_data <- eventReactive(input$generate_iso, {
+    
+    leafletProxy("isoMap") %>%
+      clearGroup("Isochrones") %>%
+      clearGroup("NDVI Raster")
+    
+    # If user selected address:
+    if (input$location_choice == "address") {
+      if (is.null(input$geocoder)) {
+        showNotification("Please use the geocoder to select an address.", type = "error")
+        return(NULL)
+      }
+      
+      # Coordinates are already set via the geocoder observer
+      # No need to geocode again
+    }
+    
+    pt <- chosen_point()
+    if (is.null(pt)) {
+      showNotification("No location selected! Provide an address or click the map.", type = "error")
+      return(NULL)
+    }
+    if (length(input$transport_modes) == 0) {
+      showNotification("Select at least one transportation mode.", type = "error")
+      return(NULL)
+    }
+    if (length(input$iso_times) == 0) {
+      showNotification("Select at least one isochrone time.", type = "error")
+      return(NULL)
+    }
+    
+    location_sf <- st_as_sf(
+      data.frame(lon = pt["lon"], lat = pt["lat"]),
+      coords = c("lon","lat"), crs = 4326
+    )
+    
+    iso_list <- list()
+    for (mode in input$transport_modes) {
+      for (t in input$iso_times) {
+        iso <- tryCatch({
+          mb_isochrone(location_sf, time = as.numeric(t), profile = mode, 
+                       access_token = mapbox_token)
+        }, error = function(e) {
+          showNotification(paste("Isochrone error:", mode, t, e$message), type = "error")
+          NULL
+        })
+        if (!is.null(iso)) {
+          iso$mode <- mode
+          iso$time <- t
+          iso_list <- append(iso_list, list(iso))
+        }
+      }
+    }
+    if (length(iso_list) == 0) {
+      showNotification("No isochrones generated.", type = "warning")
+      return(NULL)
+    }
+    
+    all_iso <- do.call(rbind, iso_list) %>% st_transform(4326)
+    all_iso
+  })
+  
+  # ------------------------------------------------
+  # Plot Isochrones + NDVI
+  # ------------------------------------------------
+  observeEvent(isochrones_data(), {
+    iso_data <- isochrones_data()
+    req(iso_data)
+    
+    iso_data$iso_group <- paste(iso_data$mode, iso_data$time, sep = "_")
+    pal <- colorRampPalette(brewer.pal(8, "Set2"))
+    cols <- pal(nrow(iso_data))
+    
+    for (i in seq_len(nrow(iso_data))) {
+      poly_i <- iso_data[i, ]
+      leafletProxy("isoMap") %>%
+        addPolygons(
+          data = poly_i,
+          group = "Isochrones",
+          color = cols[i],
+          weight = 2,
+          fillOpacity = 0.4,
+          label = paste0(poly_i$mode, " - ", poly_i$time, " mins")
+        )
+    }
+    
+    iso_union <- st_union(iso_data)
+    iso_union_vect <- vect(iso_union)
+    ndvi_crop <- crop(ndvi, iso_union_vect)
+    ndvi_mask <- mask(ndvi_crop, iso_union_vect)
+    ndvi_vals <- values(ndvi_mask)
+    ndvi_vals <- ndvi_vals[!is.na(ndvi_vals)]
+    
+    if (length(ndvi_vals) > 0) {
+      ndvi_pal <- colorNumeric("YlGn", domain = range(ndvi_vals, na.rm = TRUE), na.color = "transparent")
+      
+      leafletProxy("isoMap") %>%
+        addRasterImage(
+          x = ndvi_mask,
+          colors = ndvi_pal,
+          opacity = 0.7,
+          project = TRUE,
+          group = "NDVI Raster"
+        ) %>%
+        addLegend(
+          position = "bottomright",
+          pal = ndvi_pal,
+          values = ndvi_vals,
+          title = "NDVI"
+        )
+    }
+    
+    leafletProxy("isoMap") %>%
+      addLayersControl(
+        baseGroups = c("Street Map (Default)", "Satellite (ESRI)", "CartoDB.Positron"),
+        overlayGroups = c("Income", "Greenspace", 
+                          "Hotspots (KnowBR)", "Coldspots (KnowBR)",
+                          "Isochrones", "NDVI Raster"),
+        options = layersControlOptions(collapsed = FALSE)
+      )
+  })
+  
+  # ------------------------------------------------
+  # socio_data Reactive + Summaries
+  # ------------------------------------------------
+  socio_data <- reactive({
+    iso_data <- isochrones_data()
+    if (is.null(iso_data) || nrow(iso_data) == 0) {
+      return(data.frame())
+    }
+    
+    acs_wide <- cbg_vect_sf %>%
+      mutate(
+        population = popE,
+        med_income = medincE
+      )
+    
+    hotspot_union  <- st_union(biodiv_hotspots)
+    coldspot_union <- st_union(biodiv_coldspots)
+    
+    results <- data.frame()
+    
+    # Calculate distance to coldspot and hotspots
+    for (i in seq_len(nrow(iso_data))) {
+      poly_i <- iso_data[i, ]
+      
+      dist_hot  <- st_distance(poly_i, hotspot_union)
+      dist_cold <- st_distance(poly_i, coldspot_union)
+      dist_hot_km  <- round(as.numeric(min(dist_hot))  / 1000, 3)
+      dist_cold_km <- round(as.numeric(min(dist_cold)) / 1000, 3)
+      
+      inter_acs <- st_intersection(acs_wide, poly_i)
+      
+      vect_acs_wide <- vect(acs_wide)
+      vect_poly_i <- vect(poly_i)
+      inter_acs <- intersect(vect_acs_wide, vect_poly_i)
+      inter_acs = st_as_sf(inter_acs)
+      
+      pop_total <- 0
+      inc_str   <- "N/A"
+      if (nrow(inter_acs) > 0) {
+        inter_acs$area <- st_area(inter_acs)
+        inter_acs$area_num <- as.numeric(inter_acs$area)
+        inter_acs$area_ratio <- inter_acs$area_num / as.numeric(st_area(inter_acs))
+        inter_acs$weighted_pop <- inter_acs$population * inter_acs$area_ratio
+        
+        pop_total <- round(sum(inter_acs$weighted_pop, na.rm = TRUE))
+        
+        w_income <- sum(inter_acs$med_income * inter_acs$area_num, na.rm = TRUE) /
+          sum(inter_acs$area_num, na.rm = TRUE)
+        if (!is.na(w_income) && w_income > 0) {
+          inc_str <- paste0("$", formatC(round(w_income, 2), format = "f", big.mark = ","))
+        }
+      }
+      
+      # Intersection with greenspace
+      vec_osm_greenspace <- vect(osm_greenspace)
+      inter_gs <- intersect(vec_osm_greenspace, vect_poly_i)
+      inter_gs = st_as_sf(inter_gs)
+      
+      gs_area_m2 <- 0
+      if (nrow(inter_gs) > 0) {
+        gs_area_m2 <- sum(st_area(inter_gs))
+      }
+      iso_area_m2 <- as.numeric(st_area(poly_i))
+      gs_area_m2 <- as.numeric(gs_area_m2)
+      gs_percent <- ifelse(iso_area_m2 > 0, 100 * gs_area_m2 / iso_area_m2, 0)
+      
+      # NDVI Calculation
+      poly_vect <- vect(poly_i)
+      ndvi_crop <- crop(ndvi, poly_vect)
+      ndvi_mask <- mask(ndvi_crop, poly_vect)
+      ndvi_vals <- values(ndvi_mask)
+      ndvi_vals <- ndvi_vals[!is.na(ndvi_vals)]
+      mean_ndvi <- ifelse(length(ndvi_vals) > 0, round(mean(ndvi_vals, na.rm=TRUE), 3), NA)
+      
+      # Intersection with GBIF data
+      inter_gbif <- intersect(vect_gbif, vect_poly_i)
+      inter_gbif <- st_as_sf(inter_gbif)
+      
+      inter_gbif_acs <- sf_gbif %>% 
+        mutate(
+          income = medincE,
+          ndvi = ndvi_sentinel
+        )
+      
+      if (nrow(inter_gbif) > 0) {
+        inter_gbif_acs <- inter_gbif_acs[inter_gbif_acs$GEOID %in% inter_gbif$GEOID, ]
+      }
+      
+      n_records   <- nrow(inter_gbif)
+      n_species   <- length(unique(inter_gbif$species))
+      
+      n_birds   <- length(unique(inter_gbif$species[inter_gbif$class == "Aves"]))
+      n_mammals <- length(unique(inter_gbif$species[inter_gbif$class == "Mammalia"]))
+      n_plants  <- length(unique(inter_gbif$species[inter_gbif$class %in% 
+                                                      c("Magnoliopsida","Liliopsida","Pinopsida","Polypodiopsida",
+                                                        "Equisetopsida","Bryopsida","Marchantiopsida") ]))
+      
+      iso_area_km2 <- round(iso_area_m2 / 1e6, 3)
+      
+      row_i <- data.frame(
+        Mode                = tools::toTitleCase(poly_i$mode),
+        Time                = poly_i$time,
+        IsochroneArea_km2   = iso_area_km2,
+        DistToHotspot_km    = dist_hot_km,
+        DistToColdspot_km   = dist_cold_km,
+        EstimatedPopulation = pop_total,
+        MedianIncome        = inc_str,
+        MeanNDVI            = ifelse(!is.na(mean_ndvi), mean_ndvi, "N/A"),
+        GBIF_Records        = n_records,
+        GBIF_Species        = n_species,
+        Bird_Species        = n_birds,
+        Mammal_Species      = n_mammals,
+        Plant_Species       = n_plants,
+        Greenspace_m2       = round(gs_area_m2, 2),
+        Greenspace_percent  = round(gs_percent, 2),
+        stringsAsFactors    = FALSE
+      )
+      results <- rbind(results, row_i)
+    }
+    
+    iso_union <- st_union(iso_data)
+    vect_iso <- vect(iso_union)
+    inter_all_gbif <- intersect(vect_gbif, vect_iso)
+    inter_all_gbif <- st_as_sf(inter_all_gbif)
+    
+    union_n_species <- length(unique(inter_all_gbif$species))
+    rank_percentile <- round(100 * ecdf(cbg_vect_sf$unique_species)(union_n_species), 1)
+    attr(results, "bio_percentile") <- rank_percentile
+    
+    # Closest Greenspace from ANY part of the isochrone
+    dist_mat <- st_distance(iso_union, osm_greenspace)  # 1 x N matrix
+    if (length(dist_mat) > 0) {
+      min_dist <- min(dist_mat)
+      min_idx  <- which.min(dist_mat)
+      gs_name  <- osm_greenspace$name[min_idx]
+      attr(results, "closest_greenspace") <- gs_name
+    } else {
+      attr(results, "closest_greenspace") <- "None"
+    }
+    
+    results
+  })
+  
+  # ------------------------------------------------
+  # Render main summary table
+  # ------------------------------------------------
+  output$dataTable <- renderDT({
+    df <- socio_data()
+    if (nrow(df) == 0) {
+      return(DT::datatable(data.frame("Message" = "No isochrones generated yet.")))
+    }
+    DT::datatable(
+      df,
+      colnames = c(
+        "Mode"                 = "Mode",
+        "Time (min)"           = "Time",
+        "Area (km²)"           = "IsochroneArea_km2",
+        "Dist. Hotspot (km)"   = "DistToHotspot_km",
+        "Dist. Coldspot (km)"  = "DistToColdspot_km",
+        "Population"           = "EstimatedPopulation",
+        "Median Income"        = "MedianIncome",
+        "Mean NDVI"            = "MeanNDVI",
+        "GBIF Records"         = "GBIF_Records",
+        "Unique Species"       = "GBIF_Species",
+        "Bird Species"         = "Bird_Species",
+        "Mammal Species"       = "Mammal_Species",
+        "Plant Species"        = "Plant_Species",
+        "Greenspace (m²)"      = "Greenspace_m2",
+        "Greenspace (%)"       = "Greenspace_percent"
+      ),
+      options = list(pageLength = 10, autoWidth = TRUE),
+      rownames = FALSE
+    )
+  })
+  
+  # ------------------------------------------------
+  # Biodiversity Access Score + Closest Greenspace
+  # ------------------------------------------------
+  output$bioScoreBox <- renderUI({
+    df <- socio_data()
+    if (nrow(df) == 0) return(NULL)
+    
+    percentile <- attr(df, "bio_percentile")
+    if (is.null(percentile)) percentile <- "N/A"
+    else percentile <- paste0(percentile, "th Percentile")
+    
+    wellPanel(
+      HTML(paste0("<h2>Biodiversity Access Score: ", percentile, "</h2>"))
+    )
+  })
+  
+  output$closestGreenspaceUI <- renderUI({
+    df <- socio_data()
+    if (nrow(df) == 0) return(NULL)
+    gs_name <- attr(df, "closest_greenspace")
+    if (is.null(gs_name)) gs_name <- "None"
+    
+    tagList(
+      strong("Closest Greenspace (from any part of the Isochrone):"),
+      p(gs_name)
+    )
+  })
+  
+  # ------------------------------------------------
+  # Secondary table: user-selected CLASS & FAMILY
+  # ------------------------------------------------
+  output$classTable <- renderDT({
+    iso_data <- isochrones_data()
+    if (is.null(iso_data) || nrow(iso_data) == 0) {
+      return(DT::datatable(data.frame("Message" = "No isochrones generated yet.")))
+    }
+    
+    iso_union <- st_union(iso_data)
+    # inter_gbif <- st_intersection(sf_gbif, iso_union)
+    
+    vect_iso <- vect(iso_union)
+    inter_gbif <- intersect(vect_gbif, vect_iso)
+    inter_gbif = st_as_sf(inter_gbif)
+    
+    # Add a quick ACS intersection for mean income & NDVI if needed
+    acs_wide <- cbg_vect_sf %>% mutate(
+      income = median_inc,
+      ndvi   = ndvi_mean
+    )
+    # this can be skipped ! 
+    # inter_gbif_acs <- st_intersection(inter_gbif, acs_wide)
+    
+    inter_gbif_acs = sf_gbif |> dplyr::mutate(income = medincE,
+                                              ndvi = ndvi_sentinel)#We can do this because we preannotated ndvi and us census information
+    
+    if (input$class_filter != "All") {
+      inter_gbif_acs <- inter_gbif_acs[ inter_gbif_acs$class == input$class_filter, ]
+    }
+    if (input$family_filter != "All") {
+      inter_gbif_acs <- inter_gbif_acs[ inter_gbif_acs$family == input$family_filter, ]
+    }
+    
+    if (nrow(inter_gbif_acs) == 0) {
+      return(DT::datatable(data.frame("Message" = "No records for that combination in the isochrone.")))
+    }
+    
+    species_counts <- inter_gbif_acs %>%
+      st_drop_geometry() %>%
+      group_by(species) %>%
+      summarize(
+        n_records   = n(),
+        mean_income = round(mean(income, na.rm=TRUE), 2),
+        mean_ndvi   = round(mean(ndvi, na.rm=TRUE), 3),
+        .groups = "drop"
+      ) %>%
+      arrange(desc(n_records))
+    
+    DT::datatable(
+      species_counts,
+      colnames = c("Species", "Number of Records", "Mean Income", "Mean NDVI"),
+      options = list(pageLength = 10),
+      rownames = FALSE
+    )
+  })
+  
+  # ------------------------------------------------
+  # Ggplot: Biodiversity & Socioeconomic Summary
+  # ------------------------------------------------
+  output$bioSocPlot <- renderPlot({
+    df <- socio_data()
+    if (nrow(df) == 0) return(NULL)
+    
+    df_plot <- df %>%
+      mutate(IsoLabel = paste0(Mode, "-", Time, "min"))
+    
+    ggplot(df_plot, aes(x = IsoLabel)) +
+      geom_col(aes(y = GBIF_Species), fill = "steelblue", alpha = 0.7) +
+      geom_line(aes(y = EstimatedPopulation / 1000, group = 1), color = "red", size = 1) +
+      geom_point(aes(y = EstimatedPopulation / 1000), color = "red", size = 3) +
+      labs(
+        x = "Isochrone (Mode-Time)",
+        y = "Unique Species (Blue) | Population (Red) (Thousands)",
+        title = "Biodiversity & Socioeconomic Summary"
+      ) +
+      theme_minimal(base_size = 14) +
+      theme(
+        axis.text.x  = element_text(angle = 45, hjust = 1, size = 12),
+        axis.text.y  = element_text(size = 12),
+        axis.title.x = element_text(size = 14),
+        axis.title.y = element_text(size = 14),
+        plot.title   = element_text(hjust = 0.5, size = 16, face = "bold")
+      )
+  })
+  
+  # ------------------------------------------------
+  # Bar plot: GBIF records by institutionCode
+  # ------------------------------------------------
+  output$collectionPlot <- renderPlot({
+    iso_data <- isochrones_data()
+    if (is.null(iso_data) || nrow(iso_data) == 0) {
+      plot.new()
+      title("No GBIF records found in this isochrone.")
+      return(NULL)
+    }
+    
+    iso_union <- st_union(iso_data)
+    # inter_gbif <- st_intersection(sf_gbif, iso_union)
+    
+    vect_iso <- vect(iso_union)
+    inter_gbif <- intersect(vect_gbif, vect_iso)
+    inter_gbif = st_as_sf(inter_gbif)
+    
+    if (nrow(inter_gbif) == 0) {
+      plot.new()
+      title("No GBIF records found in this isochrone.")
+      return(NULL)
+    }
+    
+    df_code <- inter_gbif %>%
+      st_drop_geometry() %>%
+      group_by(institutionCode) %>%
+      summarize(count = n(), .groups = "drop") %>%
+      arrange(desc(count)) %>%
+      mutate(truncatedCode = substr(institutionCode, 1, 5)) # Shorter version of the names 
+    
+    ggplot(df_code, aes(x = reorder(truncatedCode, -count), y = count)) + # replaced institutionCode with truncatedCode
+      geom_bar(stat = "identity", fill = "darkorange", alpha = 0.7) +
+      labs(
+        x = "Institution Code (Truncated)",
+        y = "Number of Records",
+        title = "GBIF Records by Institution Code (Isochrone Union)"
+      ) +
+      theme_minimal(base_size = 14) +
+      theme(
+        axis.text.x  = element_text(angle = 45, hjust = 1, size = 12),
+        axis.text.y  = element_text(size = 12),
+        axis.title.x = element_text(size = 14),
+        axis.title.y = element_text(size = 14),
+        plot.title   = element_text(hjust = 0.5, size = 16, face = "bold")
+      )
+  })
+  
+  # ------------------------------------------------
+  # Additional Section: mapview for species richness vs. data availability
+  # ------------------------------------------------
+  output$mapNUI <- renderUI({
+    map_n <- mapview(cbg_vect_sf, zcol = "n", layer.name="Data Availability (n)")
+    map_n@map
+  })
+  
+  output$mapSpeciesUI <- renderUI({
+    map_s <- mapview(cbg_vect_sf, zcol = "n_species", layer.name="Species Richness (n_species)")
+    map_s@map
+  })
+  
+  
+  
+  
+  # ------------------------------------------------
+  # Additional Plot: n_observations vs n_species
+  # ------------------------------------------------
+  
+  # Make it reactive: obsVsSpeciesPlot updates dynamically based on user-selected class_filter or family_filter. 
+  
+  filtered_data <- reactive({
+    data <- cbg_vect_sf
+    if (input$class_filter != "All") {
+      data <- data[data$class == input$class_filter, ]
+    }
+    if (input$family_filter != "All") {
+      data <- data[data$family == input$family_filter, ]
+    }
+    data
+  })
+  
+  output$obsVsSpeciesPlot <- renderPlot({
+    data <- filtered_data()
+    if (nrow(data) == 0) {
+      plot.new()
+      title("No data available for selected filters.")
+      return(NULL)
+    }
+    
+    ggplot(data, aes(x = log(n_observations + 1), y = log(unique_species + 1))) +
+      geom_point(color = "blue", alpha = 0.6) +
+      labs(
+        x = "Log(Number of Observations + 1)",
+        y = "Log(Species Richness + 1)",
+        title = "Data Availability vs. Species Richness"
+      ) +
+      theme_minimal(base_size = 14) +
+      theme(
+        axis.text.x  = element_text(size = 12),
+        axis.text.y  = element_text(size = 12),
+        axis.title.x = element_text(size = 14),
+        axis.title.y = element_text(size = 14),
+        plot.title   = element_text(hjust = 0.5, size = 16, face = "bold")
+      )
+  })
+  
+  # ------------------------------------------------
+  # [Optional: Linear Model Plot (Commented Out)]
+  # ------------------------------------------------
+  # Uncomment and adjust if needed
+  # output$lmCoefficientsPlot <- renderPlot({
+  #   df_lm <- cbg_vect_sf %>% 
+  #     filter(!is.na(n_observations), 
+  #            !is.na(unique_species),
+  #            !is.na(median_inc),
+  #            !is.na(ndvi_mean))
+  #   
+  #   if (nrow(df_lm) < 5) {
+  #     plot.new()
+  #     title("Not enough data for linear model.")
+  #     return(NULL)
+  #   }
+  #   
+  #   fit <- lm(unique_species ~ n_observations + median_inc + ndvi_mean, data = df_lm)
+  #   
+  #   p <- plot_model(fit, show.values = TRUE, value.offset = .3, title = "LM Coefficients: n_species ~ n_observations + median_inc + ndvi_mean")
+  #   print(p)
+  # })
+}
+
+# Run the Shiny app
+shinyApp(ui, server)

R/setup.R

@@ -0,0 +1,97 @@
+# setup
+
+# ------------------------------------------------
+# 1) API Keys
+# ------------------------------------------------
+mapbox_token <- "pk.eyJ1Ijoia3dhbGtlcnRjdSIsImEiOiJjbHc3NmI0cDMxYzhyMmt0OXBiYnltMjVtIn0.Thtu6WqIhOfin6AykskM2g" 
+mb_access_token(mapbox_token, install = FALSE)
+
+# ------------------------------------------------
+# 2) Load Data
+# ------------------------------------------------
+# -- Greenspace
+getwd()
+osm_greenspace <- st_read("data/greenspaces_osm_nad83.shp", quiet = TRUE) %>%
+  st_transform(4326)
+if (!"name" %in% names(osm_greenspace)) {
+  osm_greenspace$name <- "Unnamed Greenspace"
+}
+
+# -- NDVI Raster
+ndvi <- rast("data/SF_EastBay_NDVI_Sentinel_10.tif")
+
+# -- GBIF data
+# Load what is basically inter_gbif !!!!! 
+# load("data/sf_gbif.Rdata")  # => sf_gbif
+load('data/gbif_census_ndvi_anno.Rdata')
+vect_gbif <- vect(sf_gbif)
+# -- Precomputed CBG data
+load('data/cbg_vect_sf.Rdata')
+if (!"unique_species" %in% names(cbg_vect_sf)) {
+  cbg_vect_sf$unique_species <- cbg_vect_sf$n_species
+}
+if (!"n_observations" %in% names(cbg_vect_sf)) {
+  cbg_vect_sf$n_observations <- cbg_vect_sf$n
+}
+if (!"median_inc" %in% names(cbg_vect_sf)) {
+  cbg_vect_sf$median_inc <- cbg_vect_sf$medincE
+}
+if (!"ndvi_mean" %in% names(cbg_vect_sf)) {
+  cbg_vect_sf$ndvi_mean <- cbg_vect_sf$ndvi_sentinel
+}
+
+# -- Hotspots/Coldspots
+biodiv_hotspots  <- st_read("data/hotspots.shp",  quiet = TRUE) %>% st_transform(4326)
+biodiv_coldspots <- st_read("data/coldspots.shp", quiet = TRUE) %>% st_transform(4326)
+
+
+# 
+# # Community Organizations shapefile
+# # For now simulate
+# 
+# # Define San Francisco bounding box coordinates
+# sf_bbox <- st_bbox(c(
+#   xmin = -122.5247,  # Western longitude
+#   ymin = 37.7045,     # Southern latitude
+#   xmax = -122.3569,   # Eastern longitude
+#   ymax = 37.8334       # Northern latitude
+# ), crs = st_crs(4326))  # WGS84 CRS
+# 
+# # Convert bounding box to polygon
+# sf_boundary <- st_as_sfc(sf_bbox) %>% st_make_valid()
+# 
+# # Transform boundary to projected CRS for accurate buffering (EPSG:3310)
+# sf_boundary_proj <- st_transform(sf_boundary, 3310)
+# 
+# # Set seed for reproducibility
+# set.seed(123)
+# 
+# # Simulate 20 random points within San Francisco boundary
+# community_points <- st_sample(sf_boundary_proj, size = 20, type = "random")
+# 
+# # Convert to sf object with POINT geometry and assign unique names
+# community_points_sf <- st_sf(
+#   NAME = paste("Community Org", 1:20),
+#   geometry = community_points
+# )
+# # Select first 3 points to buffer
+# buffered_points_sf <- community_points_sf[1:3, ] %>%
+#   st_buffer(dist = 100)  # Buffer distance in meters
+# 
+# # Update the NAME column to indicate buffered areas
+# buffered_points_sf$NAME <- paste(buffered_points_sf$NAME, "Area")
+# community_points_sf <- st_transform(community_points_sf, 4326)
+# buffered_points_sf <- st_transform(buffered_points_sf, 4326)
+# 
+# # Combine points and polygons into one sf object
+# community_orgs <- bind_rows(
+#   community_points_sf,
+#   buffered_points_sf
+# )
+# 
+# # View the combined dataset
+# print(community_orgs)
+# 
+# community_points_only <- community_orgs %>% filter(st_geometry_type(geometry) == "POINT")
+# community_polygons_only <- community_orgs %>% filter(st_geometry_type(geometry) == "POLYGON")
+# 

README.md

@@ -8,6 +8,11 @@ It further calculates a summary table of the GBIF data located within the isochr
 
 # Next steps: Optimize preanno of sf gbif and cbg
 
+Add Imp Surf, Walking Scores, SVI to cbg_sf
+
 # Public transport ddata
 
+Calculate accessability matrix for SF
+
 # Show difference on the day
+

app_shinydashboards.R

@@ -0,0 +1,1008 @@
+###############################################################################
+# Shiny App: San Francisco Biodiversity Access Decision Support Tool
+# Author: Diego Ellis Soto, et al.
+# University of California Berkeley, ESPM
+# California Academy of Sciences
+###############################################################################
+require(shinyjs)
+library(shiny)
+library(shinydashboard)
+library(leaflet)
+library(mapboxapi)
+library(tidyverse)
+library(tidycensus)
+library(sf)
+library(DT)
+library(RColorBrewer)
+library(terra)       
+library(data.table)  # for fread
+library(mapview)     # for mapview objects
+library(sjPlot)      # for plotting lm model coefficients
+library(sjlabelled)  # optional if needed for sjPlot
+library(bslib)
+library(shinycssloaders)
+
+source('R/setup.R') # Ensure this script loads necessary data objects
+
+# Define your Mapbox token securely
+mapbox_token <- "pk.eyJ1Ijoia3dhbGtlcnRjdSIsImEiOiJjbHc3NmI0cDMxYzhyMmt0OXBiYnltMjVtIn0.Thtu6WqIhOfin6AykskM2g" 
+
+# Global theme definition using a green-themed bootswatch
+theme <- bs_theme(
+  bootswatch = "minty", # 'minty' is a light green-themed bootswatch
+  base_font = font_google("Roboto"),
+  heading_font = font_google("Roboto Slab"),
+  bg = "#f0fff0",       # Honeydew background
+  fg = "#2e8b57"        # SeaGreen foreground
+)
+
+# UI
+ui <- dashboardPage(
+  skin = "green", # shinydashboard skin color
+  dashboardHeader(title = "SF Biodiversity Access Tool"),
+  dashboardSidebar(
+    sidebarMenu(
+      menuItem("Isochrone Explorer", tabName = "isochrone", icon = icon("map-marker-alt")),
+      menuItem("GBIF Summaries", tabName = "gbif", icon = icon("table")),
+      menuItem("Community Science", tabName = "community_science", icon = icon("users")),
+      menuItem("About", tabName = "about", icon = icon("info-circle"))
+    )
+  ),
+  dashboardBody(
+    theme = theme, # Apply the custom theme
+    useShinyjs(),  # Initialize shinyjs
+    # Loading message
+    div(id = "loading", style = "display:none; font-size: 20px; color: red;", "Calculating..."),
+    
+    # Tab Items
+    tabItems(
+      # Isochrone Explorer Tab
+      tabItem(tabName = "isochrone",
+              fluidRow(
+                box(
+                  title = "Controls", status = "success", solidHeader = TRUE, width = 4,
+                  radioButtons(
+                    "location_choice", 
+                    "Select Location Method:",
+                    choices = c("Address (Geocode)" = "address", 
+                                "Click on Map"      = "map_click"),
+                    selected = "map_click"  
+                  ),
+                  
+                  conditionalPanel(
+                    condition = "input.location_choice == 'address'",
+                    mapboxGeocoderInput(
+                      inputId = "geocoder",
+                      placeholder = "Search for an address",
+                      access_token = mapbox_token
+                    )
+                  ),
+                  
+                  checkboxGroupInput(
+                    "transport_modes", 
+                    "Select Transportation Modes:",
+                    choices = list("Driving"             = "driving",
+                                   "Walking"             = "walking",
+                                   "Cycling"             = "cycling",
+                                   "Driving with Traffic"= "driving-traffic"),
+                    selected = c("driving", "walking")
+                  ),
+                  
+                  checkboxGroupInput(
+                    "iso_times", 
+                    "Select Isochrone Times (minutes):",
+                    choices = list("5" = 5, "10" = 10, "15" = 15),
+                    selected = c(5, 10)
+                  ),
+                  
+                  actionButton("generate_iso", "Generate Isochrones", icon = icon("play")),
+                  actionButton("clear_map", "Clear", icon = icon("times"))
+                ),
+                box(
+                  title = "Map", status = "success", solidHeader = TRUE, width = 8,
+                  leafletOutput("isoMap", height = 600)
+                )
+              ),
+              fluidRow(
+                box(
+                  title = "Biodiversity Access Score", status = "success", solidHeader = TRUE, width = 6,
+                  uiOutput("bioScoreBox")
+                ),
+                box(
+                  title = "Closest Greenspace", status = "success", solidHeader = TRUE, width = 6,
+                  uiOutput("closestGreenspaceUI")
+                )
+              ),
+              fluidRow(
+                box(
+                  title = "Summary Data", status = "success", solidHeader = TRUE, width = 12,
+                  DTOutput("dataTable") %>% withSpinner(type = 8, color = "#28a745")
+                )
+              ),
+              fluidRow(
+                box(
+                  title = "Biodiversity & Socioeconomic Summary", status = "success", solidHeader = TRUE, width = 12,
+                  plotOutput("bioSocPlot", height = "400px") %>% withSpinner(type = 8, color = "#28a745")
+                )
+              ),
+              fluidRow(
+                box(
+                  title = "GBIF Records by Institution", status = "success", solidHeader = TRUE, width = 12,
+                  plotOutput("collectionPlot", height = "400px") %>% withSpinner(type = 8, color = "#28a745")
+                )
+              )
+      ),
+      
+      # GBIF Summaries Tab
+      tabItem(tabName = "gbif",
+              fluidRow(
+                box(
+                  title = "Filters", status = "success", solidHeader = TRUE, width = 4,
+                  selectInput(
+                    "class_filter",
+                    "Select a GBIF Class to Summarize:",
+                    choices = c("All", sort(unique(sf_gbif$class))), 
+                    selected = "All"
+                  ),
+                  selectInput(
+                    "family_filter",
+                    "Filter by Family (optional):",
+                    choices = c("All", sort(unique(sf_gbif$family))),
+                    selected = "All"
+                  )
+                ),
+                box(
+                  title = "Data Summary", status = "success", solidHeader = TRUE, width = 8,
+                  DTOutput("classTable")
+                )
+              ),
+              fluidRow(
+                box(
+                  title = "Observations vs. Species Richness", status = "success", solidHeader = TRUE, width = 12,
+                  plotOutput("obsVsSpeciesPlot", height = "300px") %>% withSpinner(type = 8, color = "#28a745"),
+                  p("This plot displays the relationship between the number of observations and the species richness. Use this visualization to understand data coverage and biodiversity trends.")
+                )
+              )
+      ),
+      # Community Science Tab
+      tabItem(tabName = "community_science",
+              fluidRow(
+                box(
+                  title = "Partner Community Organizations", status = "success", solidHeader = TRUE, width = 12,
+                  leafletOutput("communityMap", height = 600)
+                )
+              ),
+              fluidRow(
+                box(
+                  title = "Community Organizations Data", status = "success", solidHeader = TRUE, width = 12,
+                  DTOutput("communityTable") %>% withSpinner(type = 8, color = "#28a745")
+                )
+              )
+      ),
+      
+      # About Tab
+      tabItem(tabName = "about",
+              fluidRow(
+                box(
+                  title = "App Summary", status = "success", solidHeader = TRUE, width = 12,
+                  tags$b("App Summary (Fill out with RSF data working group):"),
+                  p("
+                    This application allows users to either click on a map or geocode an address 
+                    to generate travel-time isochrones across multiple transportation modes 
+                    (e.g., pedestrian, cycling, driving, driving during traffic).
+                    It retrieves socio-economic data from precomputed Census variables, calculates NDVI, 
+                    and summarizes biodiversity records from GBIF. Users can explore information 
+                    related to biodiversity in urban environments, including greenspace coverage, 
+                    population estimates, and species diversity within each isochrone.
+                  "),
+                  
+                  tags$b("Created by:"),
+                  p(strong("Diego Ellis Soto", "Carl Boettiger, Rebecca Johnson, Christopher J. Schell")),
+                  
+                  p("Contact Information: ", strong("[email protected]"))
+                )
+              ),
+              fluidRow(
+                box(
+                  title = "Reimagining San Francisco", status = "success", solidHeader = TRUE, width = 12,
+                  tags$b("Reimagining San Francisco (Fill out with CAS):"),
+                  p("Reimagining San Francisco is an initiative aimed at integrating ecological, social, 
+                     and technological dimensions to shape a sustainable future for the Bay Area. 
+                     This collaboration unites diverse stakeholders to explore innovations in urban planning, 
+                     conservation, and community engagement. The Reimagining San Francisco Data Working Group has been tasked with identifying and integrating multiple sources of socio-ecological biodiversity information in a co-development framework.")
+                )
+              ),
+              fluidRow(
+                box(
+                  title = "Why Biodiversity Access Matters", status = "success", solidHeader = TRUE, width = 12,
+                  p("Ensuring equitable access to biodiversity is essential for human well-being, 
+                     ecological resilience, and global policy decisions related to conservation. 
+                     Areas with higher biodiversity can support ecosystem services including pollinators, moderate climate extremes, 
+                     and provide cultural, recreational, and health benefits to local communities. 
+                     Recognizing that cities are particularly complex socio-ecological systems facing both legacies of sociocultural practices as well as current ongoing dynamic human activities and pressures.
+                     Incorporating multiple facets of biodiversity metrics alongside variables employed by city planners, human geographers, and decision-makers into urban planning will allow a more integrative lens in creating a sustainable future for cities and their residents.")
+                )
+              ),
+              fluidRow(
+                box(
+                  title = "How We Calculate Biodiversity Access Percentile", status = "success", solidHeader = TRUE, width = 12,
+                  p("Total unique species found within the user-generated isochrone. 
+                     We then compare that value to the distribution of unique species counts across all census block groups, 
+                     converting that comparison into a percentile ranking (Polish this, look at the 15 Minute city). 
+                     A higher percentile indicates greater biodiversity within the chosen area, 
+                     relative to other parts of the city or region.")
+                )
+              ),
+              fluidRow(
+                box(
+                  title = "Next Steps", status = "success", solidHeader = TRUE, width = 12,
+                  tags$ul(
+                    tags$li("Add impervious surface"),
+                    tags$li("National walkability score"),
+                    tags$li("Social vulnerability score"),
+                    tags$li("NatureServe biodiversity maps"),
+                    tags$li("Calculate cold-hotspots within aggregation of H6 bins instead of by census block group: Ask Carl"),
+                    tags$li("Species range maps"),
+                    tags$li("Add common name GBIF"),
+                    tags$li("Partner orgs"),
+                    tags$li("Optimize speed -> store variables -> H-ify the world?"),
+                    tags$li("Brainstorm and co-develop the biodiversity access score"),
+                    tags$li("For the GBIF summaries, add an annotated GBIF_sf with environmental variables so we can see landcover type association across the biodiversity within the isochrone.")
+                  )
+                )
+              )
+      )
+    )
+  )
+)
+
+# ------------------------------------------------
+# Server
+# ------------------------------------------------
+server <- function(input, output, session) {
+  
+  chosen_point <- reactiveVal(NULL)
+  
+  # ------------------------------------------------
+  # Leaflet Base + Hide Overlays
+  # ------------------------------------------------
+  output$isoMap <- renderLeaflet({
+    pal_cbg <- colorNumeric("YlOrRd", cbg_vect_sf$medincE)
+    
+    pal_rich <- colorNumeric("YlOrRd", domain = cbg_vect_sf$unique_species)
+    # Color palette for data availability
+    pal_data <- colorNumeric("Blues", domain = cbg_vect_sf$n_observations)
+    
+    leaflet() %>%
+      addTiles(group = "Street Map (Default)") %>%
+      addProviderTiles(providers$Esri.WorldImagery, group = "Satellite (ESRI)") %>%
+      addProviderTiles(providers$CartoDB.Positron, group = "CartoDB.Positron") %>%
+      
+      addPolygons(
+        data = cbg_vect_sf,
+        group = "Income",
+        fillColor = ~pal_cbg(medincE),
+        fillOpacity = 0.6,
+        color = "white",
+        weight = 1,
+        label=~GEOID,
+        highlightOptions = highlightOptions(
+          weight = 5,
+          color = "blue",
+          fillOpacity = 0.5,
+          bringToFront = TRUE
+        ),
+        labelOptions = labelOptions(
+          style = list("font-weight" = "bold", "color" = "blue"),
+          textsize = "12px",
+          direction = "auto"
+        )
+      ) %>%
+      
+      addPolygons(
+        data = osm_greenspace,
+        group = "Greenspace",
+        fillColor = "darkgreen",
+        fillOpacity = 0.3,
+        color = "green",
+        weight = 1,
+        label = ~name,
+        highlightOptions = highlightOptions(
+          weight = 5,
+          color = "blue",
+          fillOpacity = 0.5,
+          bringToFront = TRUE
+        ),
+        labelOptions = labelOptions(
+          style = list("font-weight" = "bold", "color" = "blue"),
+          textsize = "12px",
+          direction = "auto",
+          noHide = FALSE # Labels appear on hover
+        )
+      ) %>%
+      
+      addPolygons(
+        data = biodiv_hotspots,
+        group = "Hotspots (KnowBR)",
+        fillColor = "firebrick",
+        fillOpacity = 0.2,
+        color = "firebrick",
+        weight = 2,
+        label = ~GEOID,
+        highlightOptions = highlightOptions(
+          weight = 5,
+          color = "blue",
+          fillOpacity = 0.5,
+          bringToFront = TRUE
+        ),
+        labelOptions = labelOptions(
+          style = list("font-weight" = "bold", "color" = "blue"),
+          textsize = "12px",
+          direction = "auto"
+        )
+      ) %>%
+      
+      addPolygons(
+        data = biodiv_coldspots,
+        group = "Coldspots (KnowBR)",
+        fillColor = "navyblue",
+        fillOpacity = 0.2,
+        color = "navyblue",
+        weight = 2,
+        label = ~GEOID,
+        highlightOptions = highlightOptions(
+          weight = 5,
+          color = "blue",
+          fillOpacity = 0.5,
+          bringToFront = TRUE
+        ),
+        labelOptions = labelOptions(
+          style = list("font-weight" = "bold", "color" = "blue"),
+          textsize = "12px",
+          direction = "auto"
+        )
+      ) %>%
+      
+      # Add Species Richness Layer
+      addPolygons(
+        data = cbg_vect_sf,
+        group = "Species Richness",
+        fillColor = ~pal_rich(unique_species),
+        fillOpacity = 0.6,
+        color = "white",
+        weight = 1,
+        label = ~unique_species,
+        popup = ~paste0(
+          "<strong>GEOID: </strong>", GEOID,
+          "<br><strong>Species Richness: </strong>", unique_species,
+          "<br><strong>Observations: </strong>", n_observations,
+          "<br><strong>Median Income: </strong>", median_inc,
+          "<br><strong>Mean NDVI: </strong>", ndvi_mean
+        )
+      ) %>%
+      
+      # Add Data Availability Layer
+      addPolygons(
+        data = cbg_vect_sf,
+        group = "Data Availability",
+        fillColor = ~pal_data(n_observations),
+        fillOpacity = 0.6,
+        color = "white",
+        weight = 1,
+        label = ~n_observations,
+        popup = ~paste0(
+          "<strong>GEOID: </strong>", GEOID,
+          "<br><strong>Observations: </strong>", n_observations,
+          "<br><strong>Species Richness: </strong>", unique_species,
+          "<br><strong>Median Income: </strong>", median_inc,
+          "<br><strong>Mean NDVI: </strong>", ndvi_mean
+        )
+      ) %>%
+      
+      setView(lng = -122.4194, lat = 37.7749, zoom = 12) %>%
+      addLayersControl(
+        baseGroups    = c("Street Map (Default)", "Satellite (ESRI)", "CartoDB.Positron"),
+        overlayGroups = c("Income", "Greenspace", 
+                          "Hotspots (KnowBR)", "Coldspots (KnowBR)",
+                          "Species Richness", "Data Availability",
+                          "Isochrones", "NDVI Raster"),
+        options       = layersControlOptions(collapsed = FALSE)
+      ) %>%
+      hideGroup("Income") %>%
+      hideGroup("Greenspace") %>%
+      hideGroup("Hotspots (KnowBR)") %>%
+      hideGroup("Coldspots (KnowBR)") %>%
+      hideGroup("Species Richness") %>%
+      hideGroup("Data Availability")
+  })
+  
+  
+  # ------------------------------------------------
+  # Observe map clicks (location_choice = 'map_click')
+  # ------------------------------------------------
+  observeEvent(input$isoMap_click, {
+    req(input$location_choice == "map_click")
+    click <- input$isoMap_click
+    if (!is.null(click)) {
+      chosen_point(c(lon = click$lng, lat = click$lat))
+      
+      # Provide feedback with coordinates
+      showNotification(
+        paste0("Map clicked at Longitude: ", round(click$lng, 5), 
+               ", Latitude: ", round(click$lat, 5)),
+        type = "message"
+      )
+      
+      # Update the map with a marker
+      leafletProxy("isoMap") %>%
+        clearMarkers() %>%
+        addCircleMarkers(
+          lng = click$lng, lat = click$lat,
+          radius = 6, color = "firebrick",
+          label = "Map Click Location"
+        )
+    }
+  })
+  
+  # ------------------------------------------------
+  # Observe geocoder input
+  # ------------------------------------------------
+  observeEvent(input$geocoder, {
+    req(input$location_choice == "address")
+    geocode_result <- input$geocoder
+    if (!is.null(geocode_result)) {
+      # Extract coordinates
+      xy <- geocoder_as_xy(geocode_result)
+      
+      # Update the chosen_point reactive value
+      chosen_point(c(lon = xy[1], lat = xy[2]))
+      
+      # Provide feedback with the geocoded address and coordinates
+      showNotification(
+        paste0("Address geocoded to Longitude: ", round(xy[1], 5), 
+               ", Latitude: ", round(xy[2], 5)),
+        type = "message"
+      )
+      
+      # Update the map with a marker
+      leafletProxy("isoMap") %>%
+        clearMarkers() %>%
+        addCircleMarkers(
+          lng = xy[1], lat = xy[2],
+          radius = 6, color = "navyblue",
+          label = "Geocoded Address"
+        ) %>%
+        flyTo(lng = xy[1], lat = xy[2], zoom = 13)
+    }
+  })
+  
+  # ------------------------------------------------
+  # Observe clearing of map
+  # ------------------------------------------------
+  observeEvent(input$clear_map, {
+    # Reset the chosen point
+    chosen_point(NULL)
+    
+    # Clear all markers and isochrones from the map, but keep other layers
+    leafletProxy("isoMap") %>%
+      clearMarkers() %>%
+      clearGroup("Isochrones") %>%
+      clearGroup("NDVI Raster")
+    
+    # Provide feedback to the user
+    showNotification("Map cleared. You can select a new location.", type = "message")
+  })
+  
+  # ------------------------------------------------
+  # Generate Isochrones
+  # ------------------------------------------------
+  isochrones_data <- eventReactive(input$generate_iso, {
+    
+    leafletProxy("isoMap") %>%
+      clearGroup("Isochrones") %>%
+      clearGroup("NDVI Raster")
+    
+    # Validate inputs
+    pt <- chosen_point()
+    if (is.null(pt)) {
+      showNotification("No location selected! Provide an address or click the map.", type = "error")
+      return(NULL)
+    }
+    if (length(input$transport_modes) == 0) {
+      showNotification("Select at least one transportation mode.", type = "error")
+      return(NULL)
+    }
+    if (length(input$iso_times) == 0) {
+      showNotification("Select at least one isochrone time.", type = "error")
+      return(NULL)
+    }
+    
+    location_sf <- st_as_sf(
+      data.frame(lon = pt["lon"], lat = pt["lat"]),
+      coords = c("lon","lat"), crs = 4326
+    )
+    
+    iso_list <- list()
+    for (mode in input$transport_modes) {
+      for (t in input$iso_times) {
+        iso <- tryCatch({
+          mb_isochrone(location_sf, time = as.numeric(t), profile = mode, 
+                       access_token = mapbox_token)
+        }, error = function(e) {
+          showNotification(paste("Isochrone error:", mode, t, e$message), type = "error")
+          NULL
+        })
+        if (!is.null(iso)) {
+          iso$mode <- mode
+          iso$time <- t
+          iso_list <- append(iso_list, list(iso))
+        }
+      }
+    }
+    if (length(iso_list) == 0) {
+      showNotification("No isochrones generated.", type = "warning")
+      return(NULL)
+    }
+    
+    all_iso <- do.call(rbind, iso_list) %>% st_transform(4326)
+    all_iso
+  })
+  
+  # ------------------------------------------------
+  # Plot Isochrones + NDVI
+  # ------------------------------------------------
+  observeEvent(isochrones_data(), {
+    iso_data <- isochrones_data()
+    req(iso_data)
+    
+    iso_data$iso_group <- paste(iso_data$mode, iso_data$time, sep = "_")
+    pal <- colorRampPalette(brewer.pal(8, "Set2"))
+    cols <- pal(nrow(iso_data))
+    
+    for (i in seq_len(nrow(iso_data))) {
+      poly_i <- iso_data[i, ]
+      leafletProxy("isoMap") %>%
+        addPolygons(
+          data = poly_i,
+          group = "Isochrones",
+          color = cols[i],
+          weight = 2,
+          fillOpacity = 0.4,
+          label = paste0(poly_i$mode, " - ", poly_i$time, " mins")
+        )
+    }
+    
+    iso_union <- st_union(iso_data)
+    iso_union_vect <- vect(iso_union)
+    ndvi_crop <- crop(ndvi, iso_union_vect)
+    ndvi_mask <- mask(ndvi_crop, iso_union_vect)
+    ndvi_vals <- values(ndvi_mask)
+    ndvi_vals <- ndvi_vals[!is.na(ndvi_vals)]
+    
+    if (length(ndvi_vals) > 0) {
+      ndvi_pal <- colorNumeric("YlGn", domain = range(ndvi_vals, na.rm = TRUE), na.color = "transparent")
+      
+      leafletProxy("isoMap") %>%
+        addRasterImage(
+          x = ndvi_mask,
+          colors = ndvi_pal,
+          opacity = 0.7,
+          project = TRUE,
+          group = "NDVI Raster"
+        ) %>%
+        addLegend(
+          position = "bottomright",
+          pal = ndvi_pal,
+          values = ndvi_vals,
+          title = "NDVI"
+        )
+    }
+    
+    # Ensure other layers remain
+    leafletProxy("isoMap") %>%
+      addLayersControl(
+        baseGroups = c("Street Map (Default)", "Satellite (ESRI)", "CartoDB.Positron"),
+        overlayGroups = c("Income", "Greenspace", 
+                          "Hotspots (KnowBR)", "Coldspots (KnowBR)",
+                          "Species Richness", "Data Availability",
+                          "Isochrones", "NDVI Raster"),
+        options = layersControlOptions(collapsed = FALSE)
+      )
+  })
+  
+  # ------------------------------------------------
+  # socio_data Reactive + Summaries
+  # ------------------------------------------------
+  socio_data <- reactive({
+    iso_data <- isochrones_data()
+    if (is.null(iso_data) || nrow(iso_data) == 0) {
+      return(data.frame())
+    }
+    
+    acs_wide <- cbg_vect_sf %>%
+      mutate(
+        population = popE,
+        med_income = medincE
+      )
+    
+    hotspot_union  <- st_union(biodiv_hotspots)
+    coldspot_union <- st_union(biodiv_coldspots)
+    
+    results <- data.frame()
+    
+    # Calculate distance to coldspot and hotspots
+    for (i in seq_len(nrow(iso_data))) {
+      poly_i <- iso_data[i, ]
+      
+      dist_hot  <- st_distance(poly_i, hotspot_union)
+      dist_cold <- st_distance(poly_i, coldspot_union)
+      dist_hot_km  <- round(as.numeric(min(dist_hot))  / 1000, 3)
+      dist_cold_km <- round(as.numeric(min(dist_cold)) / 1000, 3)
+      
+      inter_acs <- st_intersection(acs_wide, poly_i)
+      
+      vect_acs_wide <- vect(acs_wide)
+      vect_poly_i <- vect(poly_i)
+      inter_acs <- intersect(vect_acs_wide, vect_poly_i)
+      inter_acs = st_as_sf(inter_acs)
+      
+      pop_total <- 0
+      inc_str   <- "N/A"
+      if (nrow(inter_acs) > 0) {
+        inter_acs$area <- st_area(inter_acs)
+        inter_acs$area_num <- as.numeric(inter_acs$area)
+        inter_acs$area_ratio <- inter_acs$area_num / as.numeric(st_area(inter_acs))
+        inter_acs$weighted_pop <- inter_acs$population * inter_acs$area_ratio
+        
+        pop_total <- round(sum(inter_acs$weighted_pop, na.rm = TRUE))
+        
+        w_income <- sum(inter_acs$med_income * inter_acs$area_num, na.rm = TRUE) /
+          sum(inter_acs$area_num, na.rm = TRUE)
+        if (!is.na(w_income) && w_income > 0) {
+          inc_str <- paste0("$", formatC(round(w_income, 2), format = "f", big.mark = ","))
+        }
+      }
+      
+      # Intersection with greenspace
+      vec_osm_greenspace <- vect(osm_greenspace)
+      inter_gs <- intersect(vec_osm_greenspace, vect_poly_i)
+      inter_gs = st_as_sf(inter_gs)
+      
+      gs_area_m2 <- 0
+      if (nrow(inter_gs) > 0) {
+        gs_area_m2 <- sum(st_area(inter_gs))
+      }
+      iso_area_m2 <- as.numeric(st_area(poly_i))
+      gs_area_m2 <- as.numeric(gs_area_m2)
+      gs_percent <- ifelse(iso_area_m2 > 0, 100 * gs_area_m2 / iso_area_m2, 0)
+      
+      # NDVI Calculation
+      poly_vect <- vect(poly_i)
+      ndvi_crop <- crop(ndvi, poly_vect)
+      ndvi_mask <- mask(ndvi_crop, poly_vect)
+      ndvi_vals <- values(ndvi_mask)
+      ndvi_vals <- ndvi_vals[!is.na(ndvi_vals)]
+      mean_ndvi <- ifelse(length(ndvi_vals) > 0, round(mean(ndvi_vals, na.rm=TRUE), 3), NA)
+      
+      # Intersection with GBIF data
+      inter_gbif <- intersect(vect_gbif, vect_poly_i)
+      inter_gbif <- st_as_sf(inter_gbif)
+      
+      inter_gbif_acs <- sf_gbif %>% 
+        mutate(
+          income = medincE,
+          ndvi = ndvi_sentinel
+        )
+      
+      if (nrow(inter_gbif) > 0) {
+        inter_gbif_acs <- inter_gbif_acs[inter_gbif_acs$GEOID %in% inter_gbif$GEOID, ]
+      }
+      
+      n_records   <- nrow(inter_gbif)
+      n_species   <- length(unique(inter_gbif$species))
+      
+      n_birds   <- length(unique(inter_gbif$species[inter_gbif$class == "Aves"]))
+      n_mammals <- length(unique(inter_gbif$species[inter_gbif$class == "Mammalia"]))
+      n_plants  <- length(unique(inter_gbif$species[inter_gbif$class %in% 
+                                                      c("Magnoliopsida","Liliopsida","Pinopsida","Polypodiopsida",
+                                                        "Equisetopsida","Bryopsida","Marchantiopsida") ]))
+      
+      iso_area_km2 <- round(iso_area_m2 / 1e6, 3)
+      
+      row_i <- data.frame(
+        Mode                = tools::toTitleCase(poly_i$mode),
+        Time                = poly_i$time,
+        IsochroneArea_km2   = iso_area_km2,
+        DistToHotspot_km    = dist_hot_km,
+        DistToColdspot_km   = dist_cold_km,
+        EstimatedPopulation = pop_total,
+        MedianIncome        = inc_str,
+        MeanNDVI            = ifelse(!is.na(mean_ndvi), mean_ndvi, "N/A"),
+        GBIF_Records        = n_records,
+        GBIF_Species        = n_species,
+        Bird_Species        = n_birds,
+        Mammal_Species      = n_mammals,
+        Plant_Species       = n_plants,
+        Greenspace_m2       = round(gs_area_m2, 2),
+        Greenspace_percent  = round(gs_percent, 2),
+        stringsAsFactors    = FALSE
+      )
+      results <- rbind(results, row_i)
+    }
+    
+    iso_union <- st_union(iso_data)
+    vect_iso <- vect(iso_union)
+    inter_all_gbif <- intersect(vect_gbif, vect_iso)
+    inter_all_gbif <- st_as_sf(inter_all_gbif)
+    
+    union_n_species <- length(unique(inter_all_gbif$species))
+    rank_percentile <- round(100 * ecdf(cbg_vect_sf$unique_species)(union_n_species), 1)
+    attr(results, "bio_percentile") <- rank_percentile
+    
+    # Closest Greenspace from ANY part of the isochrone
+    dist_mat <- st_distance(iso_union, osm_greenspace)  # 1 x N matrix
+    if (length(dist_mat) > 0) {
+      min_dist <- min(dist_mat)
+      min_idx  <- which.min(dist_mat)
+      gs_name  <- osm_greenspace$name[min_idx]
+      attr(results, "closest_greenspace") <- gs_name
+    } else {
+      attr(results, "closest_greenspace") <- "None"
+    }
+    
+    results
+  })
+  
+  # ------------------------------------------------
+  # Render main summary table
+  # ------------------------------------------------
+  output$dataTable <- renderDT({
+    df <- socio_data()
+    if (nrow(df) == 0) {
+      return(DT::datatable(data.frame("Message" = "No isochrones generated yet.")))
+    }
+    DT::datatable(
+      df,
+      colnames = c(
+        "Mode"                 = "Mode",
+        "Time (min)"           = "Time",
+        "Area (km²)"           = "IsochroneArea_km2",
+        "Dist. Hotspot (km)"   = "DistToHotspot_km",
+        "Dist. Coldspot (km)"  = "DistToColdspot_km",
+        "Population"           = "EstimatedPopulation",
+        "Median Income"        = "MedianIncome",
+        "Mean NDVI"            = "MeanNDVI",
+        "GBIF Records"         = "GBIF_Records",
+        "Unique Species"       = "GBIF_Species",
+        "Bird Species"         = "Bird_Species",
+        "Mammal Species"       = "Mammal_Species",
+        "Plant Species"        = "Plant_Species",
+        "Greenspace (m²)"      = "Greenspace_m2",
+        "Greenspace (%)"       = "Greenspace_percent"
+      ),
+      options = list(pageLength = 10, autoWidth = TRUE),
+      rownames = FALSE
+    )
+  })
+  
+  # ------------------------------------------------
+  # Biodiversity Access Score + Closest Greenspace
+  # ------------------------------------------------
+  output$bioScoreBox <- renderUI({
+    df <- socio_data()
+    if (nrow(df) == 0) return(NULL)
+    
+    percentile <- attr(df, "bio_percentile")
+    if (is.null(percentile)) percentile <- "N/A"
+    else percentile <- paste0(percentile, "th Percentile")
+    
+    wellPanel(
+      HTML(paste0("<h2>Biodiversity Access Score: ", percentile, "</h2>"))
+    )
+  })
+  
+  output$closestGreenspaceUI <- renderUI({
+    df <- socio_data()
+    if (nrow(df) == 0) return(NULL)
+    gs_name <- attr(df, "closest_greenspace")
+    if (is.null(gs_name)) gs_name <- "None"
+    
+    tagList(
+      strong("Closest Greenspace (from any part of the Isochrone):"),
+      p(gs_name)
+    )
+  })
+  
+  # ------------------------------------------------
+  # Secondary table: user-selected CLASS & FAMILY
+  # ------------------------------------------------
+  output$classTable <- renderDT({
+    iso_data <- isochrones_data()
+    if (is.null(iso_data) || nrow(iso_data) == 0) {
+      return(DT::datatable(data.frame("Message" = "No isochrones generated yet.")))
+    }
+    
+    iso_union <- st_union(iso_data)
+    vect_iso <- vect(iso_union)
+    inter_gbif <- intersect(vect_gbif, vect_iso)
+    inter_gbif = st_as_sf(inter_gbif)
+    
+    inter_gbif_acs = sf_gbif %>% 
+      mutate(
+        income = medincE,
+        ndvi = ndvi_sentinel
+      )
+    
+    if (input$class_filter != "All") {
+      inter_gbif_acs <- inter_gbif_acs[ inter_gbif_acs$class == input$class_filter, ]
+    }
+    if (input$family_filter != "All") {
+      inter_gbif_acs <- inter_gbif_acs[ inter_gbif_acs$family == input$family_filter, ]
+    }
+    
+    if (nrow(inter_gbif_acs) == 0) {
+      return(DT::datatable(data.frame("Message" = "No records for that combination in the isochrone.")))
+    }
+    
+    species_counts <- inter_gbif_acs %>%
+      st_drop_geometry() %>%
+      group_by(species) %>%
+      summarize(
+        n_records   = n(),
+        mean_income = round(mean(income, na.rm=TRUE), 2),
+        mean_ndvi   = round(mean(ndvi, na.rm=TRUE), 3),
+        .groups = "drop"
+      ) %>%
+      arrange(desc(n_records))
+    
+    DT::datatable(
+      species_counts,
+      colnames = c("Species", "Number of Records", "Mean Income", "Mean NDVI"),
+      options = list(pageLength = 10),
+      rownames = FALSE
+    )
+  })
+  
+  # ------------------------------------------------
+  # Ggplot: Biodiversity & Socioeconomic Summary
+  # ------------------------------------------------
+  output$bioSocPlot <- renderPlot({
+    df <- socio_data()
+    if (nrow(df) == 0) return(NULL)
+    
+    df_plot <- df %>%
+      mutate(IsoLabel = paste0(Mode, "-", Time, "min"))
+    
+    ggplot(df_plot, aes(x = IsoLabel)) +
+      geom_col(aes(y = GBIF_Species), fill = "steelblue", alpha = 0.7) +
+      geom_line(aes(y = EstimatedPopulation / 1000, group = 1), color = "red", size = 1) +
+      geom_point(aes(y = EstimatedPopulation / 1000), color = "red", size = 3) +
+      labs(
+        x = "Isochrone (Mode-Time)",
+        y = "Unique Species (Blue) | Population (Red) (Thousands)",
+        title = "Biodiversity & Socioeconomic Summary"
+      ) +
+      theme_minimal(base_size = 14) +
+      theme(
+        axis.text.x  = element_text(angle = 45, hjust = 1, size = 12),
+        axis.text.y  = element_text(size = 12),
+        axis.title.x = element_text(size = 14),
+        axis.title.y = element_text(size = 14),
+        plot.title   = element_text(hjust = 0.5, size = 16, face = "bold")
+      )
+  })
+  
+  # ------------------------------------------------
+  # Bar plot: GBIF records by institutionCode
+  # ------------------------------------------------
+  output$collectionPlot <- renderPlot({
+    iso_data <- isochrones_data()
+    if (is.null(iso_data) || nrow(iso_data) == 0) {
+      plot.new()
+      title("No GBIF records found in this isochrone.")
+      return(NULL)
+    }
+    
+    iso_union <- st_union(iso_data)
+    vect_iso <- vect(iso_union)
+    inter_gbif <- intersect(vect_gbif, vect_iso)
+    inter_gbif = st_as_sf(inter_gbif)
+    
+    if (nrow(inter_gbif) == 0) {
+      plot.new()
+      title("No GBIF records found in this isochrone.")
+      return(NULL)
+    }
+    
+    df_code <- inter_gbif %>%
+      st_drop_geometry() %>%
+      group_by(institutionCode) %>%
+      summarize(count = n(), .groups = "drop") %>%
+      arrange(desc(count)) %>%
+      mutate(truncatedCode = substr(institutionCode, 1, 5)) # Shorter version of the names 
+    
+    ggplot(df_code, aes(x = reorder(truncatedCode, -count), y = count)) +
+      geom_bar(stat = "identity", fill = "darkorange", alpha = 0.7) +
+      labs(
+        x = "Institution Code (Truncated)",
+        y = "Number of Records",
+        title = "GBIF Records by Institution Code (Isochrone Union)"
+      ) +
+      theme_minimal(base_size = 14) +
+      theme(
+        axis.text.x  = element_text(angle = 45, hjust = 1, size = 12),
+        axis.text.y  = element_text(size = 12),
+        axis.title.x = element_text(size = 14),
+        axis.title.y = element_text(size = 14),
+        plot.title   = element_text(hjust = 0.5, size = 16, face = "bold")
+      )
+  })
+  
+  # ------------------------------------------------
+  # Additional Plot: n_observations vs n_species
+  # ------------------------------------------------
+  
+  # Make it reactive: obsVsSpeciesPlot updates dynamically based on user-selected class_filter or family_filter. 
+  
+  filtered_data <- reactive({
+    data <- cbg_vect_sf
+    if (input$class_filter != "All") {
+      data <- data[data$class == input$class_filter, ]
+    }
+    if (input$family_filter != "All") {
+      data <- data[data$family == input$family_filter, ]
+    }
+    data
+  })
+  
+  output$obsVsSpeciesPlot <- renderPlot({
+    data <- filtered_data()
+    if (nrow(data) == 0) {
+      plot.new()
+      title("No data available for selected filters.")
+      return(NULL)
+    }
+    
+    ggplot(data, aes(x = log(n_observations + 1), y = log(unique_species + 1))) +
+      geom_point(color = "blue", alpha = 0.6) +
+      labs(
+        x = "Log(Number of Observations + 1)",
+        y = "Log(Species Richness + 1)",
+        title = "Data Availability vs. Species Richness"
+      ) +
+      theme_minimal(base_size = 14) +
+      theme(
+        axis.text.x  = element_text(size = 12),
+        axis.text.y  = element_text(size = 12),
+        axis.title.x = element_text(size = 14),
+        axis.title.y = element_text(size = 14),
+        plot.title   = element_text(hjust = 0.5, size = 16, face = "bold")
+      )
+  })
+  
+  # ------------------------------------------------
+  # [Optional: Linear Model Plot (Commented Out)]
+  # ------------------------------------------------
+  # Uncomment and adjust if needed
+  # output$lmCoefficientsPlot <- renderPlot({
+  #   df_lm <- cbg_vect_sf %>% 
+  #     filter(!is.na(n_observations), 
+  #            !is.na(unique_species),
+  #            !is.na(median_inc),
+  #            !is.na(ndvi_mean))
+  #   
+  #   if (nrow(df_lm) < 5) {
+  #     plot.new()
+  #     title("Not enough data for linear model.")
+  #     return(NULL)
+  #   }
+  #   
+  #   fit <- lm(unique_species ~ n_observations + median_inc + ndvi_mean, data = df_lm)
+  #   
+  #   p <- plot_model(fit, show.values = TRUE, value.offset = .3, title = "LM Coefficients: n_species ~ n_observations + median_inc + ndvi_mean")
+  #   print(p)
+  # })
+}
+
+# Run the Shiny app
+shinyApp(ui, server)

rsconnect/shinyapps.io/diego-ellis-soto/RSF_Biodiversity_Access.dcf

@@ -1,13 +1,12 @@
 name: RSF_Biodiversity_Access
-title: RSF_Biodiversity_Access
+title: SF_biodiv_access_shiny
 username: diego-ellis-soto
 account: diego-ellis-soto
 server: shinyapps.io
 hostUrl: https://api.shinyapps.io/v1
 appId: 13693040
-bundleId: 9631125
+bundleId: 9633861
 url: https://diego-ellis-soto.shinyapps.io/RSF_Biodiversity_Access/
 version: 1
 asMultiple: FALSE
 asStatic: FALSE
-ignoredFiles: app_old.R