wip

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -1 +1,3 @@
 .DS_Store
+.Rproj.user
+rsconnect

Dockerfile

@@ -1,14 +1,13 @@
-FROM rocker/r-base:latest
+FROM quay.io/jupyter/minimal-notebook:ubuntu-24.04
 
-WORKDIR /code
+USER root
 
-RUN install2.r --error \
-    shiny \
-    dplyr \
-    ggplot2 \
-    readr \
-    ggExtra
-    
+# R & RStudio
+RUN curl -s https://raw.githubusercontent.com/boettiger-lab/repo2docker-r/refs/heads/main/install_r.sh | bash
+RUN curl -s https://raw.githubusercontent.com/boettiger-lab/repo2docker-r/refs/heads/main/install_rstudio.sh | bash
+
+WORKDIR /code  
 COPY . .
+RUN Rscript install.r
 
 CMD ["R", "--quiet", "-e", "shiny::runApp(host='0.0.0.0', port=7860)"]

R/old_poc/app_20250110.R

@@ -0,0 +1,1110 @@
+# 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()
+# Add that you can hover over the greespace and get its name
+# Improve the titles of the ggplots of the model coefficient estimates and of ggplot using the gbif summary table on data avialability vs species richness. Also log transform these values for better data visualization 
+# Also the ggplot of  data avialability vs species richness. should also update if the user decides to subset by class or family. Until then, its okay to retain the general plot using all the data from gbif_sf
+
+# Optimize some calculations? Shorten 
+
+# Look at code human facets or relate social vulnerabiltiy income 
+
+
+
+###############################################################################
+# 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')
+# 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$img(src = "UC Berkeley_logo.png", 
+               height = "120px", style = "margin:10px;"),
+      tags$img(src = "California_academy_logo.png", 
+               height = "120px", style = "margin:10px;"),
+      tags$img(src = "Reimagining_San_Francisco.png", 
+               height = "120px", style = "margin:10px;")
+    ),
+    theme=bs_theme(bootswatch='yeti')
+  ),
+  
+  fluidRow(
+    column(
+      width = 12,
+      br(),
+      p("This application demonstrates an approach for exploring biodiversity access in San Francisco..."),
+      # (Your summary text can go here)
+    )
+  ),
+  br(),
+  fluidRow(
+    column(
+      width = 12,
+      br(),
+      tags$b("App Summary (Fill out with RSF data working group):"),
+      # Increasingly, we ask ourselves about what increasing access to biodiversity really means. 
+      #    Importantly, accessibility differs from human mobility in urban planning studies for equitable transportation systems.
+      p("
+        This application allows users to either click on a map or geocode an address (in progress) 
+         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. We explore what biodiversity access means
+         Users can explore information that we often relate 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(),
+  # fluidRow(
+  #   column(
+  #     width = 6 , # quitar
+  tabsetPanel(
+    
+    # 1) Isochrone Explorer
+    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'",
+                   textInput(
+                     "user_address", 
+                     "Enter Address:", 
+                     value = "", 
+                     placeholder = "e.g., 1600 Amphitheatre Parkway, Mountain View, CA"
+                   )
+                 ),
+                 
+                 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")
+                   )
+                 )
+               )
+             )
+    ),
+    
+  
+  # ), # end of column wifth
+    #br.?
+  # column(
+  #   width=6,
+     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$family))),
+            selected = "All"
+          )
+        ),
+        mainPanel(
+          DTOutput("classTable"),
+          br(),
+          h3("Observations vs. Species Richness"),
+          plotOutput("obsVsSpeciesPlot", height = "300px"),
+          p("This plot displays the relationship between the number of observations and the species richness. Use this visualization to understand data coverage and biodiversity trends.")
+        )
+      )
+    )  %>% withSpinner(type = 8, color = "#337ab7")
+  ),
+  # )
+  
+    # ),
+    
+    fluidRow(
+      column(
+        width = 12,
+        tags$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$li("Calculate cold-hotspots within ggregation of H6 bins instead of by census block group: Ask Carl"),
+        tags$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$li("For the GBIF summaries, add an annotated GBIF_sf with environmental variables so we can see landcover type association across the biodiversity within the isochrone.")
+      )
+    )
+    
+    
+    
+    # )
+    
+    # Separate section for the plot outside of the "GBIF Summaries" tab
+    
+    # tabsetPanel(
+    
+    #   # 1) Isochrone Explorer
+    #   tabPanel(
+    #     mainPanel(
+    #       DTOutput("classTable"),
+    #       br(),
+    #       fluidRow(
+    #         column(
+    #           6,
+    #           # A simple scatter or line plot for n_observations vs n_species
+    #           plotOutput("obsVsSpeciesPlot", height = "300px")
+    #         )
+    #         # ,
+    #         # column(
+    #         #   6,
+    #         #   # A regression model plot using sjPlot
+    #         #   plotOutput("lmCoefficientsPlot", height = "300px")
+    #         # )
+    #       )
+    #     )
+    #   )
+    # ),
+    # 
+    # br()
+    
+  )
+  
+  
+  # fluidRow(
+  #   column(
+  #     12,
+  #     tags$h3("Species Richness vs Data Availability"),
+  #     fluidRow(
+  #       column(6, uiOutput("mapNUI")),
+  #       column(6, uiOutput("mapSpeciesUI"))
+  #     )
+  #   )
+  # )
+
+
+# ------------------------------------------------
+# 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(unique_species),
+        fillColor = ~pal_cbg(medincE),
+        fillOpacity = 0.6,
+        color = "white",
+        weight = 1,
+        # label = "Income",
+        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"
+        )
+      ) %>%
+      
+      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$lat))
+      leafletProxy("isoMap") %>%
+        clearMarkers() %>%
+        addCircleMarkers(
+          lng = click$lng, lat = click$lat,
+          radius = 6, color = "firebrick",
+          label = "Map Click Location"
+        )
+    }
+  })
+  
+  # ------------------------------------------------
+  # Observe clearinf of map
+  # ------------------------------------------------
+  observeEvent(input$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 (nchar(input$user_address) < 5) {
+        showNotification("Please enter a more complete address.", type = "error")
+        return(NULL)
+      }
+      
+      loc_df <- tryCatch({
+        mb_geocode(input$user_address, access_token = mapbox_token)
+      }, error = function(e) {
+        showNotification(paste("Geocoding failed:", e$message), type = "error")
+        NULL
+      })
+      
+      # Check for valid lat/lon
+      if (is.null(loc_df) || nrow(loc_df) == 0 || is.na(loc_df$lon[1]) || is.na(loc_df$lat[1])) {
+        showNotification("No valid geocoding results found.", type = "warning")
+        return(NULL)
+      }
+      
+      chosen_point(c(lon = loc_df$lon[1], lat = loc_df$lat[1]))
+      
+      leafletProxy("isoMap") %>%
+        clearMarkers() %>%
+        addCircleMarkers(
+          lng = loc_df$lon[1], lat = loc_df$lat[1],
+          radius = 6, color = "navyblue",
+          label = "Geocoded Address"
+        ) %>%
+        setView(lng = loc_df$lon[1], lat = loc_df$lat[1], zoom = 13)
+    }
+    
+    pt <- chosen_point()
+    if (is.null(pt)) {
+      showNotification("No location selected! Provide an address or click the map.", type = "error")
+      return(NULL)
+    }
+    if (length(input$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)]
+    
+    # Could be removed ####
+    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 = ","))
+        }
+      }
+      
+      # inter_gs <- st_intersection(osm_greenspace, poly_i)
+      
+      vec_osm_greenspace = vect(osm_greenspace)
+      vect_poly_i <- vect(poly_i)
+      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)
+      
+      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)
+      
+      # inter_gbif <- st_intersection(sf_gbif, poly_i)
+      
+      vect_poly_i = vect(poly_i)
+      
+      inter_gbif = intersect(vect_gbif,vect_poly_i)
+      inter_gbif = st_as_sf(inter_gbif)
+      # inter_gbif <- st_intersection(sf_gbif, poly_i)
+      
+      
+      inter_gbif_acs = sf_gbif |> dplyr::mutate(income = medincE,
+                                                ndvi = ndvi_sentinel)
+      
+      
+      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$class %in% 
+                                                       c("Magnoliopsida","Liliopsida","Pinopsida","Polypodiopsida",
+                                                         "Equisetopsida","Bryopsida","Marchantiopsida") ]))
+      
+      iso_area_km2 <- round(iso_area_m2 / 1e6, 3)
+      #  iso_area_sqm <- round(iso_area_m2, 2)
+      
+      row_i <- data.frame(
+        Mode                = tools::toTitleCase(poly_i$mode),
+        Time                = poly_i$time,
+        #   IsochroneArea_m2    = iso_area_sqm,
+        IsochroneArea_km2   = iso_area_km2,
+        DistToHotspot_km    = dist_hot_km,
+        DistToColdspot_km   = dist_cold_km,
+        EstimatedPopulation = pop_total,
+        MedianIncome        = inc_str,
+        MeanNDVI            = ifelse(!is.na(mean_ndvi), mean_ndvi, "N/A"),
+        GBIF_Records        = n_records,
+        GBIF_Species        = n_species,
+        Bird_Species        = n_birds,
+        Mammal_Species      = n_mammals,
+        Plant_Species       = n_plants,
+        Greenspace_m2       = round(gs_area_m2, 2),
+        Greenspace_percent  = round(gs_percent, 2),
+        stringsAsFactors    = FALSE
+      )
+      results <- rbind(results, row_i)
+    }
+    
+    iso_union <- st_union(iso_data)
+    
+    # inter_all_gbif <- st_intersection(sf_gbif, iso_union)
+    
+    # vect_gbif <- vect(sf_gbif)
+    vect_iso <- vect(iso_union)
+    inter_all_gbif <- intersect(vect_gbif, vect_iso)
+    inter_all_gbif = st_as_sf(inter_all_gbif)
+    
+    
+    union_n_species <- length(unique(inter_all_gbif$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$dataTable <- renderDT({
+    df <- socio_data()
+    if (nrow(df) == 0) {
+      return(DT::datatable(data.frame("Message" = "No isochrones generated yet.")))
+    }
+    DT::datatable(
+      df,
+      colnames = c(
+        "Mode"                 = "Mode",
+        "Time (min)"           = "Time",
+        #   "Area (m²)"            = "IsochroneArea_m2",
+        "Area (km²)"           = "IsochroneArea_km2",
+        "Dist. Hotspot (km)"   = "DistToHotspot_km",
+        "Dist. Coldspot (km)"  = "DistToColdspot_km",
+        "Population"           = "EstimatedPopulation",
+        "Median Income"        = "MedianIncome",
+        "Mean NDVI"            = "MeanNDVI",
+        "GBIF Records"         = "GBIF_Records",
+        "Unique Species"       = "GBIF_Species",
+        "Bird Species"         = "Bird_Species",
+        "Mammal Species"       = "Mammal_Species",
+        "Plant Species"        = "Plant_Species",
+        "Greenspace (m²)"      = "Greenspace_m2",
+        "Greenspace (%)"       = "Greenspace_percent"
+      ),
+      options = list(pageLength = 10, autoWidth = TRUE),
+      rownames = FALSE
+    )
+  })
+  
+  # ------------------------------------------------
+  # Biodiversity Access Score + Closest Greenspace
+  # ------------------------------------------------
+  output$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$bioSocPlot <- renderPlot({
+    df <- socio_data()
+    if (nrow(df) == 0) return(NULL)
+    
+    df_plot <- df %>%
+      mutate(IsoLabel = paste0(Mode, "-", Time, "min"))
+    
+    ggplot(df_plot, aes(x = IsoLabel)) +
+      geom_col(aes(y = GBIF_Species), fill = "steelblue", alpha = 0.7) +
+      geom_line(aes(y = EstimatedPopulation / 1000, group = 1), color = "red", size = 1) +
+      geom_point(aes(y = EstimatedPopulation / 1000), color = "red", size = 3) +
+      labs(
+        x = "Isochrone (Mode-Time)",
+        y = "Unique Species (Blue) \n | Population (Red) (thousands)",
+        title = "Biodiversity & Socioeconomic Summary"
+      ) +
+      theme_minimal(base_size = 14) +
+      theme(
+        axis.text.x  = element_text(angle = 45, hjust = 1, size = 12),
+        axis.text.y  = element_text(size = 12),
+        axis.title.x = element_text(size = 14),
+        axis.title.y = element_text(size = 14)
+      )
+  })
+  
+  # ------------------------------------------------
+  # Bar plot: GBIF records by institutionCode
+  # ------------------------------------------------
+  output$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 trunacedCode
+      geom_bar(stat = "identity", fill = "darkorange", alpha = 0.7) +
+      labs(
+        x = "Institution Code (Truncoded)",
+        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)
+      )
+  })
+  
+  # ------------------------------------------------
+  # 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$obsVsSpeciesPlot <- renderPlot({
+    data <- filtered_data()
+    ggplot(data, aes(x = log(n_observations + 1), y = log(unique_species + 1))) +
+      geom_point(color = "blue", alpha = 0.6) +
+      labs(
+        x = "Log(Number of Observations)",
+        y = "Log(Species Richness)",
+        title = "Filtered Data Availability vs. Species Richness"
+      ) +
+      theme_minimal(base_size = 14)
+  })
+  
+  # output$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
+  # ------------------------------------------------
+  # output$lmCoefficientsPlot <- renderPlot({
+  #   # Build a linear model with cbg_vect_sf
+  #   # Must ensure there are no NAs
+  #   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) {
+  #     # not enough data
+  #     plot.new()
+  #     title("Not enough data for linear model.")
+  #     return(NULL)
+  #   }
+  #   
+  #   # Model
+  #   fit <- lm(unique_species ~ n_observations + median_inc + ndvi_mean, data = df_lm)
+  #   
+  #   # Using sjPlot to visualize coefficients
+  #   # We store in an object and then print it
+  #   p <- plot_model(fit, show.values = TRUE, value.offset = .3, title = "LM Coefficients: n_species ~ n_observations + median_inc + ndvi_mean")
+  #   print(p)
+  # })
+}
+
+shinyApp(ui, server)
+# run_with_themer(shinyApp(ui, server))
+# library(profvis)
+# 
+# profvis({
+#   shinyApp(ui, server)
+# })
+

R/old_poc/app_old.R

@@ -0,0 +1,986 @@
+# Sharing the app https://shiny.posit.co/r/getstarted/shiny-basics/lesson7/ 
+# rsconnect::setAccountInfo(name='diego-ellis-soto', token='A47BE3C9E4B9EBCDFEC889AF31F64154', secret='g2Q2rxeYCiwlH81EkPXcCGsiHMgdyhTznJRmHtea')
+# deployApp()
+# Add that you can hover over the greespace and get its name
+# Improve the titles of the ggplots of the model coefficient estimates and of ggplot using the gbif summary table on data avialability vs species richness. Also log transform these values for better data visualization 
+# Also the ggplot of  data avialability vs species richness. should also update if the user decides to subset by class or family. Until then, its okay to retain the general plot using all the data from gbif_sf
+
+# Optimize some calculations? Shorten 
+
+
+
+
+
+###############################################################################
+# Shiny App: San Francisco Biodiversity Access Decision Support Tool
+# Author: Diego Ellis Soto, et al.
+# University of California Berkeley, ESPM
+# California Academy of Sciences
+###############################################################################
+
+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
+
+# ------------------------------------------------
+# 1) API Keys
+# ------------------------------------------------
+mapbox_token <- "pk.eyJ1Ijoia3dhbGtlcnRjdSIsImEiOiJjbHc3NmI0cDMxYzhyMmt0OXBiYnltMjVtIn0.Thtu6WqIhOfin6AykskM2g" 
+mb_access_token(mapbox_token, install = FALSE)
+
+# ------------------------------------------------
+# 2) Load Data
+# ------------------------------------------------
+# -- Greenspace
+osm_greenspace <- st_read("data/greenspaces_osm_nad83.shp", quiet = TRUE) %>%
+  st_transform(4326)
+if (!"name" %in% names(osm_greenspace)) {
+  osm_greenspace$name <- "Unnamed Greenspace"
+}
+
+# -- NDVI Raster
+ndvi <- rast("data/SF_EastBay_NDVI_Sentinel_10.tif")
+
+# -- GBIF data
+load("data/sf_gbif.Rdata")  # => sf_gbif
+
+# -- Precomputed CBG data
+load('data/cbg_vect_sf.Rdata')
+if (!"unique_species" %in% names(cbg_vect_sf)) {
+  cbg_vect_sf$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$ndvi_sentinel
+}
+
+# -- Hotspots/Coldspots
+biodiv_hotspots  <- st_read("data/hotspots.shp",  quiet = TRUE) %>% st_transform(4326)
+biodiv_coldspots <- st_read("data/coldspots.shp", quiet = TRUE) %>% st_transform(4326)
+
+# ------------------------------------------------
+# 3) UI
+# ------------------------------------------------
+ui <- fluidPage(
+  titlePanel("San Francisco Biodiversity Access Decision Support Tool"),
+  
+  fluidRow(
+    column(
+      width = 12, align = "center",
+      tags$img(src = "UC Berkeley_logo.png", 
+               height = "120px", style = "margin:10px;"),
+      tags$img(src = "California_academy_logo.png", 
+               height = "120px", style = "margin:10px;"),
+      tags$img(src = "Reimagining_San_Francisco.png", 
+               height = "120px", style = "margin:10px;")
+    )
+  ),
+  
+  fluidRow(
+    column(
+      width = 12,
+      br(),
+      p("This application demonstrates an approach for exploring biodiversity access in San Francisco..."),
+      # (Your summary text can go here)
+    )
+  ),
+  br(),
+  fluidRow(
+    column(
+      width = 12,
+      br(),
+      tags$b("App Summary (Fill out with RSF data working group):"),
+      # Increasingly, we ask ourselves about what increasing access to biodiversity really means. 
+      #    Importantly, accessibility differs from human mobility in urban planning studies for equitable transportation systems.
+      p("
+        This application allows users to either click on a map or geocode an address (in progress) 
+         to generate travel-time isochrones across multiple transportation modes (e.g., pedestrian, cycling, driving, driving during traffic).
+         It retrieves socio-economic data from precomputed Census variables, calculates NDVI, 
+         and summarizes biodiversity records from GBIF. We explore what biodiversity access means
+         Users can explore information that we often relate to biodiversity in urban environments including greenspace coverage, population estimates, and species diversity within each isochrone."),
+      
+      tags$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$b("Created by:"),
+      p(strong("Diego Ellis Soto", "Carl Boettiger, Rebecca Johnson, Christopher J. Schell")),
+      
+      p("Contact Information", 
+        strong("[email protected]")),
+      
+      tags$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 ggregation 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.")
+      )
+    )
+  ),
+  br(),
+  
+  tabsetPanel(
+    
+    # 1) Isochrone Explorer
+    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'",
+                   textInput(
+                     "user_address", 
+                     "Enter Address:", 
+                     value = "", 
+                     placeholder = "e.g., 1600 Amphitheatre Parkway, Mountain View, CA"
+                   )
+                 ),
+                 
+                 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"),
+                          uiOutput("closestGreenspaceUI")
+                   )
+                 ),
+                 
+                 br(),
+                 DTOutput("dataTable"),
+                 
+                 br(),
+                 fluidRow(
+                   column(12,
+                          plotOutput("bioSocPlot", height = "400px")
+                   )
+                 ),
+                 
+                 br(),
+                 fluidRow(
+                   column(12,
+                          plotOutput("collectionPlot", height = "300px")
+                   )
+                 )
+               )
+             )
+    ),
+    
+    #br.?
+    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 = "400px"),
+          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.")
+        )
+      )
+    )
+    
+    
+    # )
+    
+    # Separate section for the plot outside of the "GBIF Summaries" tab
+    
+       # tabsetPanel(
+    
+    #   # 1) Isochrone Explorer
+    #   tabPanel(
+    #     mainPanel(
+    #       DTOutput("classTable"),
+    #       br(),
+    #       fluidRow(
+    #         column(
+    #           6,
+    #           # A simple scatter or line plot for n_observations vs n_species
+    #           plotOutput("obsVsSpeciesPlot", height = "300px")
+    #         )
+    #         # ,
+    #         # column(
+    #         #   6,
+    #         #   # A regression model plot using sjPlot
+    #         #   plotOutput("lmCoefficientsPlot", height = "300px")
+    #         # )
+    #       )
+    #     )
+    #   )
+    # ),
+    # 
+    # br()
+    
+  )
+  
+  
+  # fluidRow(
+  #   column(
+  #     12,
+  #     tags$h3("Species Richness vs Data Availability"),
+  #     fluidRow(
+  #       column(6, uiOutput("mapNUI")),
+  #       column(6, uiOutput("mapSpeciesUI"))
+  #     )
+  #   )
+  # )
+)
+
+# ------------------------------------------------
+# 4) Server
+# ------------------------------------------------
+server <- function(input, output, session) {
+  
+  chosen_point <- reactiveVal(NULL)
+  
+  # ------------------------------------------------
+  # Leaflet Base + Hide Overlays
+  # ------------------------------------------------
+  output$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(unique_species),
+        fillColor = ~pal_cbg(medincE),
+        fillOpacity = 0.6,
+        color = "white",
+        weight = 1,
+        label = "Income"
+      ) %>%
+      
+      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"
+        )
+      ) %>%
+      
+      addPolygons(
+        data = biodiv_hotspots,
+        group = "Hotspots (KnowBR)",
+        fillColor = "firebrick",
+        fillOpacity = 0.2,
+        color = "firebrick",
+        weight = 2,
+        label = "Biodiversity Hotspot"
+      ) %>%
+      
+      addPolygons(
+        data = biodiv_coldspots,
+        group = "Coldspots (KnowBR)",
+        fillColor = "navyblue",
+        fillOpacity = 0.2,
+        color = "navyblue",
+        weight = 2,
+        label = "Biodiversity Coldspot"
+      ) %>%
+      
+      # 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,
+        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,
+        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))
+      leafletProxy("isoMap") %>%
+        clearMarkers() %>%
+        addCircleMarkers(
+          lng = click$lng, lat = click$lat,
+          radius = 6, color = "firebrick",
+          label = "Map Click Location"
+        )
+    }
+  })
+  
+  # ------------------------------------------------
+  # Observe clearinf 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$location_choice == "address") {
+      if (nchar(input$user_address) < 5) {
+        showNotification("Please enter a more complete address.", type = "error")
+        return(NULL)
+      }
+      
+      loc_df <- tryCatch({
+        mb_geocode(input$user_address, access_token = mapbox_token)
+      }, error = function(e) {
+        showNotification(paste("Geocoding failed:", e$message), type = "error")
+        NULL
+      })
+      
+      # Check for valid lat/lon
+      if (is.null(loc_df) || nrow(loc_df) == 0 || is.na(loc_df$lon[1]) || is.na(loc_df$lat[1])) {
+        showNotification("No valid geocoding results found.", type = "warning")
+        return(NULL)
+      }
+      
+      chosen_point(c(lon = loc_df$lon[1], lat = loc_df$lat[1]))
+      
+      leafletProxy("isoMap") %>%
+        clearMarkers() %>%
+        addCircleMarkers(
+          lng = loc_df$lon[1], lat = loc_df$lat[1],
+          radius = 6, color = "navyblue",
+          label = "Geocoded Address"
+        ) %>%
+        setView(lng = loc_df$lon[1], lat = loc_df$lat[1], zoom = 13)
+    }
+    
+    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()
+    
+    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)
+      
+      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("$", formatC(round(w_income, 2), format = "f", big.mark = ","))
+        }
+      }
+      
+      inter_gs <- st_intersection(osm_greenspace, poly_i)
+      gs_area_m2 <- 0
+      if (nrow(inter_gs) > 0) {
+        gs_area_m2 <- sum(st_area(inter_gs))
+      }
+      iso_area_m2 <- as.numeric(st_area(poly_i))
+      gs_area_m2 <- as.numeric(gs_area_m2)
+      gs_percent <- ifelse(iso_area_m2 > 0, 100 * gs_area_m2 / iso_area_m2, 0)
+      
+      poly_vect <- vect(poly_i)
+      ndvi_crop <- crop(ndvi, poly_vect)
+      ndvi_mask <- mask(ndvi_crop, poly_vect)
+      ndvi_vals <- values(ndvi_mask)
+      ndvi_vals <- ndvi_vals[!is.na(ndvi_vals)]
+      mean_ndvi <- ifelse(length(ndvi_vals) > 0, round(mean(ndvi_vals, na.rm=TRUE), 3), NA)
+      
+      inter_gbif <- st_intersection(sf_gbif, poly_i)
+      n_records   <- nrow(inter_gbif)
+      n_species   <- length(unique(inter_gbif$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)
+      iso_area_sqm <- round(iso_area_m2, 2)
+      
+      row_i <- data.frame(
+        Mode                = tools::toTitleCase(poly_i$mode),
+        Time                = poly_i$time,
+        IsochroneArea_m2    = iso_area_sqm,
+        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)
+    inter_all_gbif <- st_intersection(sf_gbif, iso_union)
+    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 (m²)"            = "IsochroneArea_m2",
+        "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$classTable <- renderDT({
+    iso_data <- isochrones_data()
+    if (is.null(iso_data) || nrow(iso_data) == 0) {
+      return(DT::datatable(data.frame("Message" = "No isochrones generated yet.")))
+    }
+    
+    iso_union <- st_union(iso_data)
+    inter_gbif <- st_intersection(sf_gbif, iso_union)
+    
+    # Add a quick ACS intersection for mean income & NDVI if needed
+    acs_wide <- cbg_vect_sf %>% mutate(
+      income = median_inc,
+      ndvi   = ndvi_mean
+    )
+    
+    inter_gbif_acs <- st_intersection(inter_gbif, acs_wide)
+    
+    if (input$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 = "Blue bars: Unique Species \n | Red line: Population (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)
+      )
+  })
+  
+  # ------------------------------------------------
+  # Bar plot: GBIF records by institutionCode
+  # ------------------------------------------------
+  output$collectionPlot <- renderPlot({
+    iso_data <- isochrones_data()
+    if (is.null(iso_data) || nrow(iso_data) == 0) {
+      plot.new()
+      title("No GBIF records found in this isochrone.")
+      return(NULL)
+    }
+    
+    iso_union <- st_union(iso_data)
+    inter_gbif <- st_intersection(sf_gbif, iso_union)
+    if (nrow(inter_gbif) == 0) {
+      plot.new()
+      title("No GBIF records found in this isochrone.")
+      return(NULL)
+    }
+    
+    df_code <- inter_gbif %>%
+      st_drop_geometry() %>%
+      group_by(institutionCode) %>%
+      summarize(count = n(), .groups = "drop") %>%
+      arrange(desc(count))
+    
+    ggplot(df_code, aes(x = reorder(institutionCode, -count), y = count)) +
+      geom_bar(stat = "identity", fill = "darkorange", alpha = 0.7) +
+      labs(
+        x = "Institution Code",
+        y = "Number of Records",
+        title = "GBIF Records by Institution Code (Isochrone Union)"
+      ) +
+      theme_minimal(base_size = 14) +
+      theme(
+        axis.text.x  = element_text(angle = 45, hjust = 1, size = 12),
+        axis.text.y  = element_text(size = 12),
+        axis.title.x = element_text(size = 14),
+        axis.title.y = element_text(size = 14)
+      )
+  })
+  
+  # ------------------------------------------------
+  # Additional Section: mapview for species richness vs. data availability
+  # ------------------------------------------------
+  output$mapNUI <- renderUI({
+    map_n <- mapview(cbg_vect_sf, zcol = "n", layer.name="Data Availability (n)")
+    map_n@map
+  })
+  
+  output$mapSpeciesUI <- renderUI({
+    map_s <- mapview(cbg_vect_sf, zcol = "n_species", layer.name="Species Richness (n_species)")
+    map_s@map
+  })
+  
+  # ------------------------------------------------
+  # Additional Plot: n_observations vs n_species
+  # ------------------------------------------------
+  output$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
+  # ------------------------------------------------
+  # output$lmCoefficientsPlot <- renderPlot({
+  #   # Build a linear model with cbg_vect_sf
+  #   # Must ensure there are no NAs
+  #   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) {
+  #     # not enough data
+  #     plot.new()
+  #     title("Not enough data for linear model.")
+  #     return(NULL)
+  #   }
+  #   
+  #   # Model
+  #   fit <- lm(unique_species ~ n_observations + median_inc + ndvi_mean, data = df_lm)
+  #   
+  #   # Using sjPlot to visualize coefficients
+  #   # We store in an object and then print it
+  #   p <- plot_model(fit, show.values = TRUE, value.offset = .3, title = "LM Coefficients: n_species ~ n_observations + median_inc + ndvi_mean")
+  #   print(p)
+  # })
+}
+
+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/old_poc/make_RSF_hexbin.R

@@ -0,0 +1,9 @@
+
+require(hexSticker)
+
+imgurl <- "www/Reimagining_San_Francisco.png"
+
+sticker(imgurl, package="BioDivAccess", p_size=20, s_x=1, s_y=.75, s_width=.6,p_family = "Roboto",
+        filename="www/hexbin_RSF_logo.png")
+
+

R/setup.R

@@ -0,0 +1,121 @@
+# setup
+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)
+library(mapview)   
+library(sjPlot)    
+library(sjlabelled)
+library(bslib)
+library(shinycssloaders)
+
+# ------------------------------------------------
+# 1) API Keys
+# ------------------------------------------------
+mapbox_token <- "pk.eyJ1Ijoia3dhbGtlcnRjdSIsImEiOiJjbHc3NmI0cDMxYzhyMmt0OXBiYnltMjVtIn0.Thtu6WqIhOfin6AykskM2g" 
+# mb_access_token(mapbox_token, install = FALSE)
+
+# ------------------------------------------------
+# 2) Load Data
+# ------------------------------------------------
+# -- Greenspace
+getwd()
+osm_greenspace <- st_read("/vsicurl/https://huggingface.co/datasets/boettiger-lab/sf_biodiv_access/resolve/main/greenspaces_osm_nad83.shp", quiet = TRUE) %>%
+
+  st_transform(4326)
+if (!"name" %in% names(osm_greenspace)) {
+  osm_greenspace$name <- "Unnamed Greenspace"
+}
+
+# -- NDVI Raster
+ndvi <- terra::rast("/vsicurl/https://huggingface.co/datasets/boettiger-lab/sf_biodiv_access/resolve/main/SF_EastBay_NDVI_Sentinel_10.tif")
+
+
+# -- GBIF data
+# Load what is basically inter_gbif !!!!! 
+# load("data/sf_gbif.Rdata")  # => sf_gbif
+
+download.file('https://huggingface.co/datasets/boettiger-lab/sf_biodiv_access/resolve/main/gbif_census_ndvi_anno.Rdata', '/tmp/gbif_census_ndvi_anno.Rdata')
+load('/tmp/gbif_census_ndvi_anno.Rdata')
+vect_gbif <- vect(sf_gbif)
+# -- Precomputed CBG data
+download.file('https://huggingface.co/datasets/boettiger-lab/sf_biodiv_access/resolve/main/cbg_vect_sf.Rdata', '/tmp/cbg_vect_sf.Rdata')
+load('/tmp/cbg_vect_sf.Rdata')
+
+if (!"unique_species" %in% names(cbg_vect_sf)) {
+  cbg_vect_sf$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$ndvi_sentinel
+}
+
+# -- Hotspots/Coldspots
+biodiv_hotspots  <- st_read("/vsicurl/https://huggingface.co/datasets/boettiger-lab/sf_biodiv_access/resolve/main/hotspots.shp",  quiet = TRUE) %>% st_transform(4326)
+biodiv_coldspots <- st_read("/vsicurl/https://huggingface.co/datasets/boettiger-lab/sf_biodiv_access/resolve/main/coldspots.shp", quiet = TRUE) %>% st_transform(4326)
+
+
+
+# 
+# # Community Organizations shapefile
+# # For now simulate
+# 
+# # Define San Francisco bounding box coordinates
+# sf_bbox <- st_bbox(c(
+#   xmin = -122.5247,  # Western longitude
+#   ymin = 37.7045,     # Southern latitude
+#   xmax = -122.3569,   # Eastern longitude
+#   ymax = 37.8334       # Northern latitude
+# ), crs = st_crs(4326))  # WGS84 CRS
+# 
+# # Convert bounding box to polygon
+# sf_boundary <- st_as_sfc(sf_bbox) %>% st_make_valid()
+# 
+# # Transform boundary to projected CRS for accurate buffering (EPSG:3310)
+# sf_boundary_proj <- st_transform(sf_boundary, 3310)
+# 
+# # Set seed for reproducibility
+# set.seed(123)
+# 
+# # Simulate 20 random points within San Francisco boundary
+# community_points <- st_sample(sf_boundary_proj, size = 20, type = "random")
+# 
+# # Convert to sf object with POINT geometry and assign unique names
+# community_points_sf <- st_sf(
+#   NAME = paste("Community Org", 1:20),
+#   geometry = community_points
+# )
+# # Select first 3 points to buffer
+# buffered_points_sf <- community_points_sf[1:3, ] %>%
+#   st_buffer(dist = 100)  # Buffer distance in meters
+# 
+# # Update the NAME column to indicate buffered areas
+# buffered_points_sf$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

@@ -1,5 +1,5 @@
 ---
-title: Reimagining SF Shiny App
+title: Sf Biodiv Access Shiny
 emoji: 📚
 colorFrom: blue
 colorTo: yellow
@@ -7,4 +7,72 @@ sdk: docker
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# SF Biodiversity Access Shiny App
+
+This Shiny app provides decision support for the **Reimagining San Francisco Initiative**, aiming to explore the intersection of biodiversity, socio-economic variables, and greenspace accessibility.
+
+![Screenshot of the App](www/app_screenshot_1.png)
+
+---
+
+## Features
+
+- Users can either **click on the map** or **type an address** to generate isochrones for travel-time accessibility.
+- Supports multiple transportation modes, including pedestrian, cycling, driving, and traffic-sensitive driving.
+- Retrieves socio-economic data from **precomputed Census variables**.
+- Calculates and overlays **NDVI** for vegetation analysis.
+- Summarizes biodiversity records from **GBIF** and identifies species richness, greenspace, and socio-economic patterns.
+
+![Combined Logos](www/Combined_logos.png)
+
+---
+
+## App Summary
+
+This application allows users to:
+
+- Generate travel-time isochrones across multiple transportation modes.
+- Retrieve biodiversity and socio-economic data for a chosen area.
+- Explore greenspace coverage, population estimates, and species diversity.
+
+**Created by:**
+Diego Ellis Soto. In collaboration with Carl Boettiger, Rebecca Johnson, Christopher J. Schell  
+Contact: [email protected]  
+
+---
+
+
+## Why Biodiversity Access Matters
+
+Ensuring equitable access to biodiversity is essential for:
+
+- **Human well-being**: Promoting physical and mental health through exposure to nature.
+- **Ecological resilience**: Supporting pollinators, moderating climate extremes, and enhancing ecosystem services.
+- **Urban planning**: Incorporating biodiversity metrics into decision-making for sustainable urban futures.
+
+---
+
+## Next Steps
+
+1. Add impervious surface data, national walkability score, and social vulnerability index.
+2. Integrate community organizations and NatureServe biodiversity maps.
+3. Optimize speed by pre-storing variables and aggregating data.
+4. Develop a comprehensive biodiversity access score in collaboration with stakeholders.
+5. Annotate GBIF data with additional environmental variables for enhanced summaries.
+
+## Public Transport Data
+
+Future plans include integrating public transportation accessibility to further enhance decision-making capabilities.
+
+---
+
+## Repository Structure
+
+- **App.R**: Main application file containing UI and server logic.
+- **R/setup.R**: Script to load necessary datasets (e.g., annotated GBIF, NDVI).
+- **www/**: Contains logos, screenshots, and other resources.
+
+---
+
+<img src="www/hexbin_RSF_logo.png" width="100">

app.R

@@ -1,58 +1,1157 @@
+###############################################################################
+# 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)
+library(mapview)   
+library(sjPlot)    
+library(sjlabelled)
 library(bslib)
-library(dplyr)
-library(ggplot2)
-
-df <- readr::read_csv("penguins.csv")
-# Find subset of columns that are suitable for scatter plot
-df_num <- df |> select(where(is.numeric), -Year)
-
-ui <- page_sidebar(
-  theme = bs_theme(bootswatch = "minty"),
-  title = "Penguins explorer",
-  sidebar = sidebar(
-    varSelectInput("xvar", "X variable", df_num, selected = "Bill Length (mm)"),
-    varSelectInput("yvar", "Y variable", df_num, selected = "Bill Depth (mm)"),
-    checkboxGroupInput("species", "Filter by species",
-      choices = unique(df$Species), selected = unique(df$Species)
-    ),
-    hr(), # Add a horizontal rule
-    checkboxInput("by_species", "Show species", TRUE),
-    checkboxInput("show_margins", "Show marginal plots", TRUE),
-    checkboxInput("smooth", "Add smoother"),
+library(shinycssloaders)
+
+source('R/setup.R') # Load necessary data (annotated gbif, annotated cbg, ndvi)
+
+# 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"))
+    )
   ),
-  plotOutput("scatter")
+  dashboardBody(
+    theme = theme, # Apply the custom theme
+    useShinyjs(),  
+    # Loading message
+    div(id = "loading", style = "display:none; font-size: 20px; color: red;", "Calculating..."),
+    
+    # fluidRow(
+    #   column(
+    #     width = 2,
+    #     imageOutput("Combined_logos")
+    #   )
+    # ),
+    
+    # fluidPage(
+    #   box(
+    #     tags$img(height = 100, width = 100,src = "Combined_logos.png"),
+    #     imageOutput('Combined_logos')
+    #   )
+    # ),
+    
+    
+    # fluidRow(
+    #   column(
+    #     width = 2,
+    #     imageOutput("uc_berkeley_logo")
+    #   ),
+    #   column(
+    #     width = 4,
+    #     imageOutput("california_academy_logo")
+    #   ),
+    #   column(
+    #     width = 6,
+    #     imageOutput("reimagining_sf_logo")
+    #   )
+    # ),
+    # fluidPage(
+    #   # Application title
+    #   # titlePanel("Test app"),
+    #   # to render images in the www folder 
+    #   box(uiOutput("houz"), width = 3)
+    # ),
+    
+    # 
+    # fluidRow(
+    #   column(
+    #     width = 12, align = "center",
+    #     tags$img(src = "UC_Berkeley_logo.png",
+    #              height = "200px", style = "margin:10px;", alt = "UC Berkeley Logo"),
+    #     tags$img(src = "California_academy_logo.png",
+    #              height = "200px", style = "margin:10px;", alt = "California Academy Logo"),
+    #     tags$img(src = "Reimagining_San_Francisco.png",
+    #              height = "200px", style = "margin:10px;", alt = "Reimagining San Francisco Logo")
+    #   )
+    # ),
+    # fluidPage(
+    #   box(
+    #     tags$img(height = 100, width = 100,src = "Rlogo.png"),
+    #     imageOutput('image_logos')
+    #   )
+    # ),
+
+    # 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) {
-  subsetted <- reactive({
-    req(input$species)
-    df |> filter(Species %in% input$species)
-  })
+  
+  chosen_point <- reactiveVal(NULL)
+  
+  # ------------------------------------------------
+  # Render logos 
+  # ------------------------------------------------
+  
+
+  output$combine_logo <- renderImage({
+    list(
+      src = file.path("www", "Combined_logos.png"),
+      width = "50%",
+      height = "45%",
+      alt = "Combined_logos"
+    )
+  }, deleteFile = FALSE)
+  
+    # output$uc_berkeley_logo <- renderImage({
+  #   list(
+  #     src = file.path("www", "UC_Berkeley_logo.png"),
+  #     width = "50%",
+  #     height = "45%",
+  #     alt = "UC Berkeley Logo"
+  #   )
+  # }, deleteFile = FALSE)
+  # 
+  # output$california_academy_logo <- renderImage({
+  #   list(
+  #     src = file.path("www", "California_academy_logo.png"),
+  #     width = "50%",
+  #     height = "45%",
+  #     alt = "California Academy Logo"
+  #   )
+  # }, deleteFile = FALSE)
+  # 
+  # output$reimagining_sf_logo <- renderImage({
+  #   list(
+  #     src = file.path("www", "Reimagining_San_Francisco.png"),
+  #     width = "50%",
+  #     height = "45%",
+  #     alt = "Reimagining San Francisco Logo"
+  #   )
+  # }, deleteFile = FALSE)
 
-  output$scatter <- renderPlot(
-    {
-      p <- ggplot(subsetted(), aes(!!input$xvar, !!input$yvar)) +
-        theme_light() +
-        list(
-          theme(legend.position = "bottom"),
-          if (input$by_species) aes(color = Species),
-          geom_point(),
-          if (input$smooth) geom_smooth()
-        )
 
-      if (input$show_margins) {
-        margin_type <- if (input$by_species) "density" else "histogram"
-        p <- p |> ggExtra::ggMarginal(
-          type = margin_type, margins = "both",
-          size = 8, groupColour = input$by_species, groupFill = input$by_species
+  # ------------------------------------------------
+  # 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 <- terra::crop(ndvi, iso_union_vect)
+    ndvi_mask <- terra::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("$", formatC(round(w_income, 2), format = "f", big.mark = ","))
+        }
+      }
+      
+      # Intersection with greenspace
+      vec_osm_greenspace <- vect(osm_greenspace)
+      inter_gs <- intersect(vec_osm_greenspace, vect_poly_i)
+      inter_gs = st_as_sf(inter_gs)
+      
+      gs_area_m2 <- 0
+      if (nrow(inter_gs) > 0) {
+        gs_area_m2 <- sum(st_area(inter_gs))
+      }
+      iso_area_m2 <- as.numeric(st_area(poly_i))
+      gs_area_m2 <- as.numeric(gs_area_m2)
+      gs_percent <- ifelse(iso_area_m2 > 0, 100 * gs_area_m2 / iso_area_m2, 0)
+      
+      # NDVI Calculation
+      poly_vect <- vect(poly_i)
+      ndvi_crop <- terra::crop(ndvi, poly_vect)
+      ndvi_mask <- terra::mask(ndvi_crop, poly_vect)
+      ndvi_vals <- values(ndvi_mask)
+      ndvi_vals <- ndvi_vals[!is.na(ndvi_vals)]
+      mean_ndvi <- ifelse(length(ndvi_vals) > 0, round(mean(ndvi_vals, na.rm=TRUE), 3), NA)
+      
+      # Intersection with GBIF data
+      inter_gbif <- intersect(vect_gbif, vect_poly_i)
+      inter_gbif <- st_as_sf(inter_gbif)
+      
+      inter_gbif_acs <- sf_gbif %>% 
+        mutate(
+          income = medincE,
+          ndvi = ndvi_sentinel
         )
+      
+      if (nrow(inter_gbif) > 0) {
+        inter_gbif_acs <- inter_gbif_acs[inter_gbif_acs$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$lmCoefficientsPlot <- renderPlot({
+  #   df_lm <- cbg_vect_sf %>% 
+  #     filter(!is.na(n_observations), 
+  #            !is.na(unique_species),
+  #            !is.na(median_inc),
+  #            !is.na(ndvi_mean))
+  #   
+  #   if (nrow(df_lm) < 5) {
+  #     plot.new()
+  #     title("Not enough data for linear model.")
+  #     return(NULL)
+  #   }
+  #   
+  #   fit <- lm(unique_species ~ n_observations + median_inc + ndvi_mean, data = df_lm)
+  #   
+  #   p <- plot_model(fit, show.values = TRUE, value.offset = .3, title = "LM Coefficients: n_species ~ n_observations + median_inc + ndvi_mean")
+  #   print(p)
+  # })
+
+
+# 
+# # Add Images:
+# df_img = data.frame(id = c(1:3), img_path=c('California_academy_logo.png', 'Reimagining_San_Francisco.png', 'UC Berkeley_logo.png'))
+# n <- nrow(df_img)
+# 
+# n <- nrow(df_img)
+# 
+# observe({
+#   for (i in 1:n)
+#   {
+#     print(i)
+#     local({
+#       my_i <- i
+#       imagename = paste0("img", my_i)
+#       print(imagename)
+#       output[[imagename]] <-
+#         renderImage({
+#           list(src = file.path('www', df_img$img_path[my_i]), 
+#                width = "100%", height = "55%",
+#                alt = "Image failed to render")
+#         }, deleteFile = FALSE)
+#     })
+#   }
+# })
+# 
+# 
+# output$houz <- renderUI({
+#   
+#   image_output_list <- 
+#     lapply(1:n,
+#            function(i)
+#            {
+#              imagename = paste0("img", i)
+#              imageOutput(imagename)
+#            })
+#   
+#   do.call(tagList, image_output_list)
+# })
+
 
-      p
-    },
-    res = 100
-  )
 }
 
+
+
+# Run the Shiny app
 shinyApp(ui, server)
+
+# 
+
+

install.r

@@ -0,0 +1,17 @@
+install.packages(c("shinyjs",
+"shiny",
+"shinydashboard",
+"leaflet",
+"mapboxapi",
+"tidyverse",
+"tidycensus",
+"sf",
+"DT",
+"RColorBrewer",
+"terra",       
+"data.table",
+"mapview",   
+"sjPlot",    
+"sjlabelled",
+"bslib",
+"shinycssloaders"))