app.py

import streamlit as st
import streamlit.components.v1 as components
import base64
# import leafmap.maplibregl as leafmap
import leafmap.foliumap as leafmap
import altair as alt
import ibis
from ibis import _
import ibis.selectors as s


# urls for main layer 
ca_pmtiles = "https://huggingface.co/datasets/boettiger-lab/ca-30x30/resolve/main/cpad-stats.pmtiles"
# ca_parquet = "https://huggingface.co/datasets/boettiger-lab/ca-30x30/resolve/main/cpad-stats.parquet"
ca_parquet = "cpad-stats.parquet" #local copy is faster 

ca_area_acres = 1.014e8 #acres 
style_choice = "GAP Status Code"

con = ibis.duckdb.connect(extensions=["spatial"])

ca = (con
      .read_parquet(ca_parquet)
      .cast({"geom": "geometry"})
     )

# urls for additional data layers 
url_sr = "https://huggingface.co/datasets/boettiger-lab/ca-30x30/resolve/main/species-richness-ca/{z}/{x}/{y}.png"
url_rsr = "https://huggingface.co/datasets/boettiger-lab/ca-30x30/resolve/main/range-size-rarity/{z}/{x}/{y}.png"
url_irr_carbon = "https://huggingface.co/datasets/boettiger-lab/ca-30x30/resolve/main/ca_irrecoverable_c_2018_cog.tif"
url_man_carbon = "https://huggingface.co/datasets/boettiger-lab/ca-30x30/resolve/main/ca_manageable_c_2018_cog.tif"
url_svi = "https://data.source.coop/cboettig/social-vulnerability/svi2020_us_county.pmtiles"
url_justice40 = "https://data.source.coop/cboettig/justice40/disadvantaged-communities.pmtiles"
url_loss_carbon = "https://huggingface.co/datasets/boettiger-lab/ca-30x30/resolve/main/deforest-carbon-ca/{z}/{x}/{y}.png"
url_hi = "https://huggingface.co/datasets/boettiger-lab/ca-30x30/resolve/main/ca_human_impact_cog.tif"
url_calfire = "https://huggingface.co/datasets/boettiger-lab/ca-30x30/resolve/main/cal_fire_2022.pmtiles"
url_rxburn = "https://huggingface.co/datasets/boettiger-lab/ca-30x30/resolve/main/cal_rxburn_2022.pmtiles"

# colors for plotting 
private_access_color = "#DE881E" # orange 
public_access_color = "#3388ff" # blue
tribal_color = "#BF40BF" # purple
mixed_color = "#005a00" # green
year2023_color = "#26542C" # green
year2024_color = "#F3AB3D" # orange 
federal_color = "#529642" # green
state_color = "#A1B03D" # light green
local_color = "#365591" # blue
special_color = "#0096FF" # blue
private_color = "#7A3F1A" # brown
joint_color = "#DAB0AE" # light pink
county_color = "#DE3163" # magenta
city_color = "#ADD8E6" #light blue
hoa_color = "#A89BBC" # purple
nonprofit_color =  "#D77031" #orange
justice40_color =  "#00008B" #purple
svi_color = "#850101" #red
white =  "#FFFFFF" 

# gap codes 3 and 4 are off by default. 
default_gap = {
    3: False,
    4: False,
}

from functools import reduce

def get_summary(ca, combined_filter, column, colors=None): #summary stats, based on filtered data 
    # ca = ca.filter(_.reGAP.isin([1,2])) #only gap 1 and 2
    df = ca.filter(combined_filter)
    df = (df
            .group_by(*column)  # unpack the list for grouping
            .aggregate(percent_protected=100 * _.Acres.sum() / ca_area_acres,
                       mean_richness = (_.richness * _.Acres).sum() / _.Acres.sum(),
                       mean_rsr = (_.rsr * _.Acres).sum() / _.Acres.sum(),
                       mean_irrecoverable_carbon = (_.irrecoverable_carbon * _.Acres).sum() / _.Acres.sum(),
                       mean_manageable_carbon = (_.manageable_carbon * _.Acres).sum() / _.Acres.sum(),
                       mean_percent_fire_20yr = (_.percent_fire_20yr *_.Acres).sum()/_.Acres.sum(),
                       mean_percent_fire_10yr = (_.percent_fire_10yr *_.Acres).sum()/_.Acres.sum(),
                       mean_percent_fire_5yr = (_.percent_fire_5yr *_.Acres).sum()/_.Acres.sum(),
                       mean_percent_fire_2yr = (_.percent_fire_2yr *_.Acres).sum()/_.Acres.sum(),
                       mean_percent_rxburn_20yr = (_.percent_rxburn_20yr *_.Acres).sum()/_.Acres.sum(),
                       mean_percent_rxburn_10yr = (_.percent_rxburn_10yr *_.Acres).sum()/_.Acres.sum(),
                       mean_percent_rxburn_5yr = (_.percent_rxburn_5yr *_.Acres).sum()/_.Acres.sum(),
                       mean_percent_rxburn_2yr = (_.percent_rxburn_2yr *_.Acres).sum()/_.Acres.sum(),
                       mean_percent_disadvantaged =  (_.percent_disadvantaged * _.Acres).sum() / _.Acres.sum(),
                       mean_svi =  (_.svi * _.Acres).sum() / _.Acres.sum(),
                       mean_svi_socioeconomic_status =  (_.svi_socioeconomic_status * _.Acres).sum() / _.Acres.sum(),
                       mean_svi_household_char =  (_.svi_household_char * _.Acres).sum() / _.Acres.sum(),
                       mean_svi_racial_ethnic_minority =  (_.svi_racial_ethnic_minority * _.Acres).sum() / _.Acres.sum(),
                       mean_svi_housing_transit =  (_.svi_housing_transit * _.Acres).sum() / _.Acres.sum(),
                       mean_carbon_lost = (_.deforest_carbon * _.Acres).sum() / _.Acres.sum(),
                       mean_human_impact =  (_.human_impact * _.Acres).sum() / _.Acres.sum(),
                      )
            .mutate(percent_protected=_.percent_protected.round(1))
         )
    if colors is not None and not colors.empty: #only the df will have colors, df_tab doesn't since we are printing it.
        df = df.inner_join(colors, column) 
    df = df.cast({col: "string" for col in column})
    df = df.to_pandas()
    return df


def summary_table(column, colors, filter_cols, filter_vals,colorby_vals): # get df for charts + df_tab for printed table + df_percent for percentage (only gap 1 and 2)
    filters = [] 
    if filter_cols and filter_vals: #if a filter is selected, add to list of filters 
        for filter_col, filter_val in zip(filter_cols, filter_vals):
            if len(filter_val) > 1:
                filters.append(getattr(_, filter_col).isin(filter_val))
            else:
                filters.append(getattr(_, filter_col) == filter_val[0])
    if column not in filter_cols: #show color_by column in table by adding it as a filter (if it's not already a filter)
        filter_cols.append(column)
        filters.append(getattr(_, column).isin(colorby_vals[column]))  
    combined_filter = reduce(lambda x, y: x & y, filters) #combining all the filters into ibis filter expression 
    df = get_summary(ca, combined_filter, [column], colors) # df used for charts 
    df_tab = get_summary(ca, combined_filter, filter_cols, colors = None) #df used for printed table
    df_percent = get_summary(ca.filter(_.reGAP.isin([1,2])), combined_filter, [column], colors) # only gap 1 and 2 count towards percentage
    return df, df_tab, df_percent 
    

def area_plot(df, column): #percent protected pie chart 
    base = alt.Chart(df).encode(
        alt.Theta("percent_protected:Q").stack(True),
    )
    pie = ( base
           .mark_arc(innerRadius= 40, outerRadius=100)
           .encode(alt.Color("color:N").scale(None).legend(None),
                   tooltip=['percent_protected', column])
    )
    text = ( base
            .mark_text(radius=80, size=14, color="white")
            .encode(text = column + ":N")
    )
    plot = pie # pie + text
    return plot.properties(width="container", height=300)


def get_pmtiles_style(paint, alpha, cols, values): #style depends on the filters selected. 
    filters = []
    for col, val in zip(cols, values):
        filter_condition = ["match", ["get", col], val, True, False]
        filters.append(filter_condition)
    combined_filter = ["all"] + filters
    return {
        "version": 8,
        "sources": {
            "ca": {
                "type": "vector",
                "url": "pmtiles://" + ca_pmtiles,
            }
        },
        "layers": [{
            "id": "ca30x30",
            "source": "ca",
            "source-layer": "layer",
            "type": "fill",
            "filter": combined_filter,  # Use the combined filter
            "paint": {
                "fill-color": paint,
                "fill-opacity": alpha
            }
        }]
    }




def bar_chart(df, x, y): #display summary stats for color_by column 
    chart = alt.Chart(df).mark_bar().encode(
        x=x,
        y=y,
        color=alt.Color('color').scale(None)
    ).properties(width="container", height=300)
    return chart



def getButtons(style_options, style_choice, default_gap=None): #finding the buttons selected to use as filters 
    column = style_options[style_choice]['property']
    opts = [style[0] for style in style_options[style_choice]['stops']]   
    default_gap = default_gap or {}  
    buttons = {
        name: st.checkbox(f"{name}", value=default_gap.get(name, True), key=column + str(name))
        for name in opts
    }
    filter_choice = [key for key, value in buttons.items() if value]  # return only selected
    d = {}
    d[column] = filter_choice
    return d


def getColorVals(style_options, style_choice): 
    #df_tab only includes filters selected, we need to manually add "color_by" column (if it's not already a filter). 
    column = style_options[style_choice]['property']
    opts = [style[0] for style in style_options[style_choice]['stops']]   
    d = {}
    d[column] = opts
    return d
    
manager = {
    'property': 'manager_type',
    'type': 'categorical',
    'stops': [
        ['Federal', federal_color],
        ['State', state_color],
        ['Non Profit', nonprofit_color],
        ['Special District', special_color],
        ['Unknown', "grey"],
        ['County', county_color],
        ['City', city_color],
        ['Joint', joint_color],
        ['Tribal', tribal_color],
        ['Private', private_color],
        ['HOA', hoa_color]
    ]
}

easement = {
    'property': 'Easement',
    'type': 'categorical',
    'stops': [
        ['Fee', public_access_color],
        ['Easement', private_access_color]
    ]
}

year = {
    'property': 'established',
    'type': 'categorical',
    'stops': [
        [2023, year2023_color],
        [2024, year2024_color]
    ]
}

access = {
    'property': 'access_type',
    'type': 'categorical',
    'stops': [
        ['Open Access', public_access_color],
        ['No Public Access', private_access_color],
        ['Unknown Access', "grey"],
        ['Restricted Access', tribal_color]
    ]
}

gap = {
    'property': 'reGAP',
    'type': 'categorical',
    'stops': [
        [1, "#26633d"],
        [2, "#879647"],
        [3, "#BBBBBB"],
        [4, "#F8F8F8"]
    ]
}

style_options = {
    "Year": year,
    "GAP Status Code": gap,
    "Manager Type": manager,
    "Easement": easement,
    "Public Access": access,
}

justice40_fill = {
    'property': 'Disadvan',
    'type': 'categorical',
    'stops': [
        [0, white], 
        [1, justice40_color]
    ]
}

def get_justice40_style(url_justice40,justice40_fill,alpha):
   return {
    "version": 8,
    "sources": {
        "source1": {
            "type": "vector",
            "url": "pmtiles://" + url_justice40,
            "attribution": "Justice40"
        }
    },
    "layers": [
        {
            "id": "layer1",
            "source": "source1",
            "source-layer": "DisadvantagedCommunitiesCEJST",
            "filter": ["match", ["get", "StateName"], "California", True, False],
            "type": "fill",
            "paint": {
                "fill-color": justice40_fill,
                 "fill-opacity": alpha
            }
        }
    ]
}

def get_sv_style(url,column,alpha):
    return {
    'version': 8,
    'sources': {
        'svi_source': {
            'type': 'vector',
            'url': "pmtiles://" + url,
            'attribution': 'Social Vulnerability Index'
        }
    },
        "layers": [
            {
                "id": "SVI",
                "source": "svi_source",
                "source-layer": "SVI2020_US_county",
                "filter": ["match", ["get", "STATE"], "California", True, False],
                "type": "fill",
                "paint": {
                    "fill-color": [
                        "interpolate", ["linear"], ["get", column],
                        0, white,
                        1, svi_color
                    ],
                    "fill-opacity": alpha
                }
            }
        ]
    }


def get_fire_style(layer,alpha):
    return {
    'version': 8,
    'sources': {
        'source2': {
            'type': 'vector',
            'url': "pmtiles://" + url_calfire,
            'attribution': 'Historical Fire Perimeters'
        }
    },
        "layers": [
            {
                "id": "fire",
                "source": "source2",
                "source-layer": layer,
                "type": "fill",
                "paint": {
                    "fill-color": "#D22B2B",
                    "fill-opacity": alpha
                }
            }
        ]
    }

def get_rx_style(layer,alpha):
    return {
    'version': 8,
    'sources': {
        'source2': {
            'type': 'vector',
            'url': "pmtiles://" + url_rxburn,
            'attribution': 'Prescribed Burns'
        }
    },
        "layers": [
            {
                "id": "fire",
                "source": "source2",
                "source-layer": layer,
                "type": "fill",
                "paint": {
                    "fill-color": "#702963",
                    "fill-opacity": alpha
                }
            }
        ]
    }


st.set_page_config(layout="wide", page_title="CA Protected Areas Explorer", page_icon=":globe:")



'''
# CA 30X30 Prototype (Safari/iOS Compatible)

An interactive cloud-native geospatial tool for exploring and visualizing California’s protected lands through open data and generative AI.
- ⬅️ Use the left sidebar to color-code the map by different attributes, toggle on data layers and view summary charts, or filter data.
- ℹ️ For non-Safari/iOS users, see [this version](https://huggingface.co/spaces/boettiger-lab/ca-30x30) for a cleaner tooltip display.


'''

st.divider()

# m = leafmap.Map(style="positron")
m = leafmap.Map()
m.add_basemap("CartoDB.PositronNoLabels")

filters = {}

with st.sidebar:
    color_choice = st.radio("Color by:", style_options)      
    colorby_vals = getColorVals(style_options, color_choice) #get options for selected color_by column 
    alpha = st.slider("transparency", 0.0, 1.0, 0.5) 

    st.divider()
    "Data Layers:"

    # Biodiversity Section 
    with st.expander("🦜 Biodiversity"):
        a_bio = st.slider("transparency", 0.0, 1.0, 0.4, key = "biodiversity")
        show_richness = st.toggle("Species Richness")
        show_rsr = st.toggle("Range-Size Rarity")
        
        if show_richness:
            m.add_tile_layer(url_sr, name="MOBI Species Richness", attribution = "MOBI", opacity=a_bio)
            
        if show_rsr:           
            m.add_tile_layer(url_rsr, name="MOBI Range-Size Rarity",attribution = "MOBI", opacity=a_bio)

    #Carbon Section
    with st.expander("⛅ Carbon & Climate"):
        a_climate = st.slider("transparency", 0.0, 1.0, 0.3, key = "climate")
        show_irrecoverable_carbon = st.toggle("Irrecoverable Carbon")
        show_manageable_carbon = st.toggle("Manageable Carbon")
        
        if show_irrecoverable_carbon:
            m.add_cog_layer(url_irr_carbon, palette="reds", name="Irrecoverable Carbon (2010-2018)", attribution = "Conservation International", opacity = a_climate, fit_bounds=False)
        
        if show_manageable_carbon:
           m.add_cog_layer(url_man_carbon, palette="purples", name="Manageable Carbon (2010-2018)", attribution = "Conservation International", opacity = a_climate, fit_bounds=False)
            
    # Fire Section
    with st.expander("🔥 Fire"):
        a_fire = st.slider("transparency", 0.0, 1.0, 0.3, key = "fire")
        show_fire_20 = st.toggle("Fires (2003-2022)")
        show_fire_10 = st.toggle("Fires (2013-2022)")
        show_fire_5 = st.toggle("Fires (2018-2022)")
        show_fire_2 = st.toggle("Fires (2022)")

        show_rx_20 = st.toggle("Prescribed Burns (2003-2022)")
        show_rx_10 = st.toggle("Prescribed Burns (2013-2022)")
        show_rx_5 = st.toggle("Prescribed Burns (2018-2022)")
        show_rx_2 = st.toggle("Prescribed Burns (2022)")

        if show_fire_20:
            m.add_pmtiles(url_calfire, style=get_fire_style("layer1",a_fire), name="CAL FIRE Fire Polygons (2003-2022)", tooltip=False, zoom_to_layer = True)

        if show_fire_10:
            m.add_pmtiles(url_calfire, style=get_fire_style("layer2",a_fire), name="CAL FIRE Fire Polygons (2013-2022)", tooltip=False, zoom_to_layer = True)
                    
        if show_fire_5:
            m.add_pmtiles(url_calfire, style=get_fire_style("layer3",a_fire), name="CAL FIRE Fire Polygons (2018-2022)", tooltip=False, zoom_to_layer = True)
            
        if show_fire_2:
            m.add_pmtiles(url_calfire, style=get_fire_style("layer4",a_fire), name="CAL FIRE Fire Polygons (2022)", tooltip=False, zoom_to_layer = True)

        if show_rx_20:
            m.add_pmtiles(url_rxburn, style=get_rx_style("layer1",a_fire), name="CAL FIRE Prescribed Burns (2003-2022)", tooltip=False, zoom_to_layer = True)

        if show_rx_10:
            m.add_pmtiles(url_rxburn, style=get_rx_style("layer2",a_fire), name="CAL FIRE Prescribed Burns (2013-2022)", tooltip=False, zoom_to_layer = True)
                    
        if show_rx_5:
            m.add_pmtiles(url_rxburn, style=get_rx_style("layer3",a_fire), name="CAL FIRE Prescribed Burns (2018-2022)", tooltip=False, zoom_to_layer = True)
            
        if show_rx_2:
            m.add_pmtiles(url_rxburn, style=get_rx_style("layer4",a_fire), name="CAL FIRE Prescribed Burns (2022)", tooltip=False, zoom_to_layer = True)


    
    # Justice40 Section 
    with st.expander("🌱 Climate & Economic Justice"):
        a_justice = st.slider("transparency", 0.0, 1.0, 0.3, key = "social justice")
        show_justice40 = st.toggle("Justice40")

        if show_justice40:
            justice_style = get_justice40_style(url_justice40,justice40_fill,a_justice)
            m.add_pmtiles(url_justice40, style=justice_style, name="Justice40", tooltip=False, zoom_to_layer = False)

    # SVI Section 
    with st.expander("🏡 Social Vulnerability"):
        a_svi = st.slider("transparency", 0.0, 1.0, 0.3, key = "SVI")
        show_sv = st.toggle("Social Vulnerability Index (SVI)")
        show_sv_socio = st.toggle("Socioeconomic Status")
        show_sv_household = st.toggle("Household Characteristics")
        show_sv_minority = st.toggle("Racial & Ethnic Minority Status")
        show_sv_housing = st.toggle("Housing Type & Transportation")
        
        if show_sv:
            m.add_pmtiles(url_svi, style = get_sv_style(url_svi, "RPL_THEMES",a_svi), tooltip=False, name = "SVI (2020)", zoom_to_layer = False)
        
        if show_sv_socio:
            m.add_pmtiles(url_svi, style = get_sv_style(url_svi, "RPL_THEME1",a_svi), tooltip=False, name = "Socioeconomic Status - SVI (2020)", zoom_to_layer = False)
        
        if show_sv_household:
            m.add_pmtiles(url_svi, style = get_sv_style(url_svi, "RPL_THEME2",a_svi), tooltip=False, name = "Household Characteristics - SVI (2020)",  zoom_to_layer = False)
        
        if show_sv_minority:
            m.add_pmtiles(url_svi, style = get_sv_style(url_svi, "RPL_THEME3",a_svi), tooltip=False, name = "Racial & Ethnic Minority Status - SVI (2020)", zoom_to_layer = False)
        
        if show_sv_housing:
            m.add_pmtiles(url_svi, style = get_sv_style(url_svi, "RPL_THEME4",a_svi), tooltip=False, name = "Housing Type & Transportation - SVI (2020)", zoom_to_layer = False)

    # HI Section 
    with st.expander("🚜 Human Impacts"):
        a_hi = st.slider("transparency", 0.0, 1.0, 0.5, key = "hi")
        show_carbon_lost = st.toggle("Deforested Carbon (2002-2022)")
        show_human_impact = st.toggle("Human Footprint (2017-2021)")
        
        if show_carbon_lost:
            m.add_tile_layer(url_loss_carbon, name="Deforested Carbon (2002-2022)",attribution = "Gassert et al. (2023)", opacity = a_hi)
        
        if show_human_impact:
            m.add_cog_layer(url_hi, name="Human Footprint (2017-2021)", attribution = "Gassert et al. (2023)", opacity = a_hi, fit_bounds=False)

    st.divider()
    
    "Filters:"
    for label in style_options: # get selected filters (based on the buttons selected)
        with st.expander(label): 
            if label == "GAP Status Code": # gap code 1 and 2 are on by default
                opts = getButtons(style_options, label, default_gap)
            else: # other buttons are not on by default.
                opts = getButtons(style_options, label) 
            filters.update(opts)
            
        selected = {k: v for k, v in filters.items() if v}
        if selected: 
            filter_cols = list(selected.keys())
            filter_vals = list(selected.values())
        else: 
            filter_cols = []
            filter_vals = []

    # Display CA 30x30 Data 
    style = get_pmtiles_style(style_options[color_choice], alpha, filter_cols, filter_vals)
    legend_d = {cat: color for cat, color in style_options[color_choice]['stops']}
    m.add_legend(legend_dict = legend_d)
    m.add_pmtiles(ca_pmtiles, style=style, name="CA 30x30", tooltip=True, overlay = True)



select_column = {
    "Year": "established",
    "GAP Status Code": "reGAP",
    "Manager Type": "manager_type",
    "Easement": "Easement",
    "Public Access": "access_type",
}

column = select_column[color_choice]

select_colors = {
    "Year": year["stops"],
    "GAP Status Code": gap["stops"],
    "Manager Type": manager["stops"],
    "Easement": easement["stops"],
    "Public Access": access["stops"],
}

colors = (
    ibis
    .memtable(select_colors[color_choice], columns=[column, "color"])
    .to_pandas()
)

# get summary tables used for charts + printed table + percentage 
# df - charts; df_tab - printed table (omits colors) + df_percent - only gap codes 1 & 2 count towards percentage 
df,df_tab,df_percent = summary_table(column, colors, filter_cols, filter_vals, colorby_vals)

# compute area covered (only gap 1 and 2)
# df_onlygap = df[df.reGAP.isin([1,2])]
total_percent = df_percent.percent_protected.sum().round(1) 

# charts displayed based on color_by variable
richness_chart = bar_chart(df, column, 'mean_richness')
rsr_chart = bar_chart(df, column, 'mean_rsr')
irr_carbon_chart = bar_chart(df, column, 'mean_irrecoverable_carbon')
man_carbon_chart = bar_chart(df, column, 'mean_manageable_carbon')
fire_20_chart = bar_chart(df, column, 'mean_percent_fire_20yr')
fire_10_chart = bar_chart(df, column, 'mean_percent_fire_10yr')
fire_5_chart = bar_chart(df, column, 'mean_percent_fire_5yr')
fire_2_chart =  bar_chart(df, column, 'mean_percent_fire_2yr')
rx_20_chart = bar_chart(df, column, 'mean_percent_rxburn_20yr')
rx_10_chart = bar_chart(df, column, 'mean_percent_rxburn_10yr')
rx_5_chart = bar_chart(df, column, 'mean_percent_rxburn_5yr')
rx_2_chart = bar_chart(df, column, 'mean_percent_rxburn_2yr')
justice40_chart = bar_chart(df, column, 'mean_percent_disadvantaged')
svi_chart = bar_chart(df, column, 'mean_svi')
svi_socio_chart = bar_chart(df, column, 'mean_svi_socioeconomic_status')
svi_house_chart = bar_chart(df, column, 'mean_svi_household_char')
svi_minority_chart = bar_chart(df, column, 'mean_svi_racial_ethnic_minority')
svi_transit_chart = bar_chart(df, column, 'mean_svi_housing_transit')
carbon_loss_chart = bar_chart(df, column, 'mean_carbon_lost')
hi_chart = bar_chart(df, column, 'mean_human_impact')

main = st.container()

with main:
    map_col, stats_col = st.columns([2,1])
    
    with map_col:

        m.to_streamlit(scrolling = True)
        st.dataframe(df_tab, use_container_width = True)

    with stats_col:
        with st.container():
            f"{total_percent}% CA Covered"
            st.altair_chart(area_plot(df_percent, column), use_container_width=True)

            if show_richness:
                "Species Richness"
                st.altair_chart(richness_chart, use_container_width=True)

            if show_rsr:
                "Range-Size Rarity"
                st.altair_chart(rsr_chart, use_container_width=True)

            if show_irrecoverable_carbon:
                "Irrecoverable Carbon"
                st.altair_chart(irr_carbon_chart, use_container_width=True)

            if show_manageable_carbon:
                "Manageable Carbon"
                st.altair_chart(man_carbon_chart, use_container_width=True)
                
            if show_fire_20:
                "Fires (2003-2022)"
                st.altair_chart(fire_20_chart, use_container_width=True)
                
            if show_fire_10:
                "Fires (2013-2022)"
                st.altair_chart(fire_10_chart, use_container_width=True)
                
            if show_fire_5:
                "Fires (2018-2022)"
                st.altair_chart(fire_5_chart, use_container_width=True)
                
            if show_fire_2:
                "Fires (2022)"
                st.altair_chart(fire_2_chart, use_container_width=True)
                
            if show_rx_20:
                "Prescribed Burns (2003-2022)"
                st.altair_chart(rx_20_chart, use_container_width=True)
                
            if show_rx_10:
                "Prescribed Burns (2013-2022)"
                st.altair_chart(rx_10_chart, use_container_width=True)
                
            if show_rx_5:
                "Prescribed Burns (2018-2022)"
                st.altair_chart(rx_5_chart, use_container_width=True)
                
            if show_rx_2:
                "Prescribed Burns (2022)"
                st.altair_chart(rx_2_chart, use_container_width=True)
                
            if show_justice40:
                "Justice40"
                st.altair_chart(justice40_chart, use_container_width=True)
                
            if show_sv:
                "Social Vulnerability Index"
                st.altair_chart(svi_chart, use_container_width=True)
                
            if show_sv_socio:
                "SVI - Socioeconomic Status"
                st.altair_chart(svi_socio_chart, use_container_width=True)
            
            if show_sv_household:
                "SVI - Household Characteristics"
                st.altair_chart(svi_house_chart, use_container_width=True)
            
            if show_sv_minority:
                "SVI - Racial and Ethnic Minority"
                st.altair_chart(svi_minority_chart, use_container_width=True)
            
            if show_sv_housing:
                "SVI - Housing Type and Transit"
                st.altair_chart(svi_transit_chart, use_container_width=True)
            
            if show_carbon_lost:
                "Deforested Carbon (2002-2022)"
                st.altair_chart(carbon_loss_chart, use_container_width=True)

            if show_human_impact:
                "Human Footprint (2017-2021)"
                st.altair_chart(hi_chart, use_container_width=True)


st.divider()
footer = st.container()


st.caption("***The label 'established' is inferred from the California Protected Areas Database, which may introduce artifacts. For details on our methodology, please refer to our code: https://github.com/boettiger-lab/ca-30x30.") 

st.caption("***Under California’s 30x30 framework, only GAP codes 1 and 2 are counted toward the conservation goal. While our dashboard displays GAP codes 1-4 for reference, the '25.2% of CA Covered' statistic reflects only GAP codes 1 and 2, as designated by CA 30x30 criteria.") 

'''
## Credits
Authors: Cassie Buhler & Carl Boettiger, UC Berkeley
License: BSD-2-clause

Data: https://huggingface.co/datasets/boettiger-lab/ca-30x30

### Data sources
- California Protected Areas Database by CA Nature. Data: https://www.californianature.ca.gov/datasets/CAnature::30x30-conserved-areas-terrestrial-2024/about. License: Public Domain

- Imperiled Species Richness and Range-Size-Rarity from NatureServe (2022). Data: https://beta.source.coop/repositories/cboettig/mobi. License CC-BY-NC-ND

- Irrecoverable Carbon from Conservation International, reprocessed to COG on https://beta.source.coop/cboettig/carbon, citation: https://doi.org/10.1038/s41893-021-00803-6, License: CC-BY-NC

- Fire polygons by CAL FIRE (2022), reprocessed to PMTiles on https://beta.source.coop/cboettig/fire/. License: Public Domain

- Climate and Economic Justice Screening Tool, US Council on Environmental Quality, Justice40, data: https://beta.source.coop/repositories/cboettig/justice40/description/, License: Public Domain

- CDC 2020 Social Vulnerability Index by US Census Track. Data: https://source.coop/repositories/cboettig/social-vulnerability/description. License: Public Domain

- Carbon-loss by Vizzuality, on https://beta.source.coop/repositories/vizzuality/lg-land-carbon-data. Citation: https://doi.org/10.1101/2023.11.01.565036, License: CC-BY

- Human Footprint by Vizzuality, on https://beta.source.coop/repositories/vizzuality/hfp-100.  Citation: https://doi.org/10.3389/frsen.2023.1130896, License: Public Domain

'''