app.py

# -*- coding: utf-8 -*-
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import leafmap.foliumap as leafmap
import streamlit as st
import altair as alt
import ibis
from ibis import _
import ibis.selectors as s

# defaults, consider user palette via st.color_picker()
private_color = "#DE881E" # orange #"#850101" # red
tribal_color = "#BF40BF" # purple
mixed_color = "#005a00" # green
public_color = "#3388ff" # blue

# default color breaks, consider tool via st.slider()
low = 2
high = 3
alpha = .5
style_choice = "Manager Type"
us_lower_48_area_m2 = 7.8e+12


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

'''
# US Protected Area Database Explorer

'''

#pad_pmtiles = "https://data.source.coop/cboettig/pad-us-3/pad-stats.pmtiles"
#parquet = "https://data.source.coop/cboettig/pad-us-3/pad-stats.parquet"
pad_pmtiles = "https://huggingface.co/datasets/boettiger-lab/pad-us-3/resolve/main/pad-stats.pmtiles"
parquet = "https://huggingface.co/datasets/boettiger-lab/pad-us-3/resolve/main/pad-stats.parquet"


m = leafmap.Map(center=[35, -100], zoom=4, layers_control=True)

custom_style = '''
"blue"
'''


sample_q = '''(
ibis.read_parquet('https://minio.carlboettiger.info/public-biodiversity/pad-us-3/pad-mobi.parquet').
group_by(_.bucket).
aggregate(percent_protected =  100 * _.area.sum() / us_lower_48_area_m2,
        mean_richness = (_.richness * _.area).sum() / _.area.sum(),
        mean_rsr = (_.rsr * _.area).sum() / _.area.sum()
        ).
mutate(percent_protected = _.percent_protected.round())
)
'''

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


manager = {
            'property': 'manager_group',
            'type': 'categorical',
            'stops': [
                ['public', public_color],
                ['private', private_color],
                ['mixed', mixed_color],
                ['tribal', tribal_color]
            ]
            }
easement = {
            'property': 'category',
            'type': 'categorical',
            'stops': [
                ['Fee', public_color],
                ['Easement', private_color],
                ['Proclamation', tribal_color]
            ]
            }

access = {
    'property': 'public_access',
    'type': 'categorical',
    'stops': [
        ['Open Access', public_color],
        ['Closed', private_color],
        ['Unknown', "grey"],
        ['Restricted Access', tribal_color]
    ]
}

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

iucn = {
            'property': 'iucn_category',
            'type': 'categorical',
            'stops': [
                ["Ia: Strict nature reserves", "#4B0082"],
                ["Ib: Wilderness areas", "#663399"],
                ["II: National park", "#7B68EE"],
                ["III: Natural monument or feature", "#9370DB"],
                ["IV: Habitat / species management", "#8A2BE2"],
                ["V: Protected landscape / seascape", "#9932CC"],
                ["VI: Protected area with sustainable use of natural resources", "#9400D3"],
                ["Other Conservation Area", "#DDA0DD"],
                ["Unassigned", "#F8F8F8"],
            ]
        }

thresholds = ['case',
              ['<', ['get', 'richness'], low],
               private_color,
              ['>=', ['get', 'richness'], high],
               mixed_color,
               public_color # default
             ]

richness = ["interpolate",
           ["linear"],
           ["get", "richness"],
           0, "#FFE6EE",
           4.8, "#850101"
           ]

rsr =  ["interpolate",
       ["linear"],
       ["get", "rsr"],
       0, "#FFE6EE",
       0.006, "#850101"
       ]

def pad_style(paint, alpha):
    return {
    "version": 8,
    "sources": {
        "pad": {
            "type": "vector",
            "url": "pmtiles://" + pad_pmtiles,
            "attribution": "US PAD v3"}},
    "layers": [{
            "id": "public",
            "source": "pad",
            "source-layer": "pad-stats",
            "type": "fill",
            "paint": {
                "fill-color": paint,
                "fill-opacity": alpha
            }
        }]}

code_ex='''
m.add_cog_layer("https://data.source.coop/vizzuality/lg-land-carbon-data/natcrop_expansion_100m_cog.tif",
                palette="oranges", name="Cropland Expansion", transparent_bg=True, opacity = 0.7, zoom_to_layer=False)
'''
# +
## Map controls sidebar

with st.sidebar:

    "## Protected Areas"

    if st.toggle("PAD US-3", True):
        alpha = st.slider("transparency", 0.0, 1.0, 0.5)

        with st.expander("custom style"):
                custom = st.text_area(
                label = "Define a custom mapbox fill-color rule",
                value = custom_style, 
                height = 100)

        style_options = {
                         "GAP Status Code": gap,
                         "IUCN Status Code": iucn,
                         "Manager Type": manager,
                         "Fee/Easement": easement,
                         "Public Access": access,
                         "Mean Richness": richness,
                         "Mean RSR": rsr,
                         "custom": eval(custom)}

        style_choice = st.radio("Color protected Areas by", style_options)
        style = pad_style(style_options[style_choice], alpha)
        m.add_pmtiles(pad_pmtiles, name="Protected Areas (PAD-US-3)", style=style, overlay=True, show=True, zoom_to_layer=False)
        ## Add legend based on selected style?
        # m.add_legend(legend_dict=legend_dict)

    "## Data layers"
    if st.toggle("Species Richness", True):
        m.add_tile_layer(url="https://data.source.coop/cboettig/mobi/tiles/red/species-richness-all/{z}/{x}/{y}.png",
                         name="MOBI Species Richness",
                         attribution="NatureServe",
                         opacity=0.9
                        )
    
    if st.toggle("Range-Size Rarity"):
          m.add_tile_layer(url="https://data.source.coop/cboettig/mobi/tiles/green/range-size-rarity-all/{z}/{x}/{y}.png",
                         name="MOBI Range-Size Rarity",
                         attribution="NatureServe",
                         opacity=0.9
                        )
        #m.add_cog_layer("https://data.source.coop/cboettig/mobi/range-size-rarity-all/RSR_All.tif",
        #                palette="greens", name="Range-Size Rarity", transparent_bg=True, opacity = 0.9, zoom_to_layer=False)

    if st.toggle("Carbon Lost (2002-2022)"):
        m.add_cog_layer("https://data.source.coop/vizzuality/lg-land-carbon-data/deforest_carbon_100m_cog.tif",
                        palette="reds", name="Carbon Lost (2002-2022)", transparent_bg=True, opacity = 0.8, zoom_to_layer=False)
    
    if st.toggle("Irrecoverable Carbon"):
        m.add_cog_layer("https://data.source.coop/cboettig/carbon/cogs/irrecoverable_c_total_2018.tif",
                        palette="purples", name="Irrecoverable Carbon", transparent_bg=True, opacity = 0.8, zoom_to_layer=False)

    if st.toggle("Manageable Carbon"):
       m.add_cog_layer("https://data.source.coop/cboettig/carbon/cogs/manageable_c_total_2018.tif",
                        palette="greens", name="Manageable Carbon", transparent_bg=True, opacity = 0.8, zoom_to_layer=False)

    if st.toggle("Human Impact"):
        hi="https://data.source.coop/vizzuality/hfp-100/hfp_2021_100m_v1-2_cog.tif"
        m.add_cog_layer(hi, palette="purples", name="Human Impact", transparent_bg=True, opacity = 0.8, zoom_to_layer=False)

    if st.toggle("cropland expansion"):
        m.add_cog_layer("https://data.source.coop/vizzuality/lg-land-carbon-data/natcrop_expansion_100m_cog.tif",
                        palette="greens", name="cropland expansion", transparent_bg=True, opacity = 0.8, zoom_to_layer=False)

    if st.toggle("Biodiversity Intactness Loss"):
        m.add_cog_layer("https://data.source.coop/vizzuality/lg-land-carbon-data/natcrop_bii_100m_cog.tif",
                        palette="reds", name="biodiversity intactness loss", transparent_bg=True, opacity = 0.8, zoom_to_layer=False)
 
    if st.toggle("Forest Integrity Loss"):
        m.add_cog_layer("https://data.source.coop/vizzuality/lg-land-carbon-data/natcrop_fii_100m_cog.tif",
                        palette="reds", name="forest integrity loss", transparent_bg=True, opacity = 0.8, zoom_to_layer=False)



    if st.toggle("Custom map layers"):
        
        code = st.text_area(label = "leafmap code:",
                            value = code_ex, 
                            height = 100)
        eval(compile(code, "<string>", "exec"))

#    "## Boundaries"
#    boundaries = st.radio("Boundaries:",
#                          ["None",
#                           "State Boundaries",
#                           "County Boundaries",
#                           "Congressional District",  
#                           "custom"]
#    )

    "## Basemaps"
    if st.toggle("Shaded Relief Topo"):
        m.add_basemap("Esri.WorldShadedRelief")
   
    "## Additional elements"
    # Fire Polygons, USGS
    if st.toggle("Fire boundaries"):
        usgs = "https://data.source.coop/cboettig/fire/usgs-mtbs.pmtiles"
        combined_style = {
            "version": 8,
            "sources": {
                "source1": {
                    "type": "vector",
                    "url": "pmtiles://" + usgs,
                    "attribution": "USGS"}},
            "layers": [{
                    "id": "usgs",
                    "source": "source1",
                    "source-layer": "mtbs_perims_DD",
                    "type": "fill",
                    "paint": {"fill-color": "#FFA500", "fill-opacity": 0.4}}]}
        m.add_pmtiles(usgs, name="Fire", style=combined_style, overlay=True, show=True, zoom_to_layer=False)

# Map radio buttons to corresponding column:
select_column = {
                "GAP Status Code": "gap_code",
                "IUCN Status Code": "iucn_category",
                "Manager Type": "manager_group",
                "Fee/Easement": "category",
                "Public Access": "public_access",
                "Mean Richness": "manager_group",
                "Mean RSR": "manager_group",
                 "custom": "gap_code"}
column = select_column[style_choice]

# Map radio buttons to corresponding color-scheme:
select_colors = {
                "GAP Status Code": gap["stops"],
                "IUCN Status Code": iucn["stops"],
                "Manager Type": manager["stops"],
                "Fee/Easement": easement["stops"],
                "Public Access": access["stops"],
                "Mean Richness": manager["stops"],
                "Mean RSR": manager["stops"],
                "custom": manager["stops"]}
colors = (ibis
          .memtable(select_colors[style_choice], columns = [column, "color"])
          .to_pandas()
         )


main = st.container()

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

    with map_col:
        m.to_streamlit(height=700)


    @st.cache_resource
    def ibis_connection(parquet):
        return ibis.read_parquet(parquet)
    pad_data = ibis_connection(parquet)

    @st.cache_data()
    def summary_table(column = column, colors = colors):
        df = (pad_data
            .rename(area = "area_square_meters")
            .group_by(_[column])
            .aggregate(percent_protected =  100 * _.area.sum() / us_lower_48_area_m2,
                        mean_richness = (_.richness * _.area).sum() / _.area.sum(),
                        mean_rsr = (_.rsr * _.area).sum() / _.area.sum(),
                        carbon_lost = (_.deforest_carbon * _.area).sum() / _.area.sum(),
                        crop_expansion = (_.crop_expansion * _.area).sum() / _.area.sum(),
                        human_impact =  (_.human_impact * _.area).sum() / _.area.sum(),
                        )
            .mutate(percent_protected = _.percent_protected.round())
            .inner_join(colors, column)
            )
        df = df.to_pandas()
        df[column] = df[column].astype(str)
        return df

    df = summary_table(column, colors)
    total_percent = df.percent_protected.sum()


    base = alt.Chart(df).encode(
        alt.Theta("percent_protected:Q").stack(True),
        alt.Color("color:N").scale(None).legend(None)
    )

    area_chart = (
    base.mark_arc(innerRadius=40, outerRadius=70)
    ).properties(width=180, height=180)


    richness_chart = bar_chart(df, column, 'mean_richness')
    rsr_chart = bar_chart(df, column, 'mean_rsr')
    carbon_lost = bar_chart(df, column, 'carbon_lost')
    crop_expansion = bar_chart(df, column, 'crop_expansion')
    human_impact = bar_chart(df, column, 'human_impact')

    with stats_col:
        with st.container():
            col1, col2, col3 = st.columns(3)
            with col1:
                f"{total_percent}% Continental US Covered"
                st.altair_chart(area_chart, use_container_width=False)

            with col2:
                "Species Richness"
                st.altair_chart(richness_chart, use_container_width=True)

            with col3:
                "Range-Size Rarity"
                st.altair_chart(rsr_chart, use_container_width=True)

        with st.container():
            col1b, col2b, col3b = st.columns(3)
            with col1b:
                "Carbon Lost ('02-'22)"
                st.altair_chart(carbon_lost, use_container_width=True)

            with col2b:
                "Crop expansion"
                st.altair_chart(crop_expansion, use_container_width=True)

            with col3b:
                "Human Impact"
                st.altair_chart(human_impact, use_container_width=True)


st.divider()

footer = st.container()


with footer:
    '''
    ## Custom queries

    Input custom python code below to interactively explore the data. 

    '''

    col2_1, col2_2 = st.columns(2)



    with col2_1:
        query = st.text_area(
            label = "Python code:",
            value = sample_q, 
            height = 300)

    with col2_2:
        "Output table:"
        df = eval(query)
        st.write(df.to_pandas())

    st.divider()

    '''
    ## Credits

    Author: Carl Boettiger, UC Berkeley
    License: BSD-2-clause

    ### Data sources

    - US Protected Areas Database v3 by USGS, data hosted on https://beta.source.coop/cboettig/us-pad-3. Citation: https://doi.org/10.5066/P9Q9LQ4B, 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
    - Fire polygons by USGS, reprocessed to PMTiles on https://beta.source.coop/cboettig/fire/. License: Public Domain.
    - 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

    ### Software

    Proudly built with a free and Open Source software stack: Streamlit (reactive application), HuggingFace (application hosting), Source.Coop (data hosting),
    using cloud-native data serializations in COG, PMTiles, and GeoParquet. Coded in pure python using leafmap and duckdb. Map styling with [MapLibre](https://maplibre.org/).
    '''