new layers, zonal stats

app.py

@@ -53,7 +53,6 @@ ca = (con
       .cast({"crop_expansion": "int64"})
      )
 
-
 private_access_color = "#DE881E" # orange 
 public_access_color = "#3388ff" # blue
 tribal_color = "#BF40BF" # purple
@@ -78,57 +77,63 @@ from functools import reduce
 def get_summary(ca, combined_filter, column, colors=None):
     df = ca.filter(combined_filter)
 
-    if colors is not None and not colors.empty: # df used for chart. 
-        df = df.filter(_.reGAP <3) # only show gaps 1 and 2 for chart.
+    # if colors is not None and not colors.empty: # df used for chart. 
+    #     df = df.filter(_.reGAP <3) # only show gaps 1 and 2 for chart.
         
     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_richness = (_.richness * _.Acres).sum() / _.Acres.sum(),
                    mean_rsr = (_.rsr * _.Acres).sum() / _.Acres.sum(),
-                   carbon_lost = (_.deforest_carbon * _.Acres).sum() / _.Acres.sum(),
+                   all_rsr =  (_.all_species_rwr * _.Acres).sum() / _.Acres.sum(),
+                   all_richness =  (_.all_species_richness * _.Acres).sum() / _.Acres.sum(),
                    irrecoverable_carbon = (_.irrecoverable_carbon * _.Acres).sum() / _.Acres.sum(),
                    manageable_carbon = (_.manageable_carbon * _.Acres).sum() / _.Acres.sum(),
-
-                   crop_expansion = (_.crop_expansion * _.Acres).sum() / _.Acres.sum(),
-                   
+                   carbon_lost = (_.deforest_carbon * _.Acres).sum() / _.Acres.sum(),
                    human_impact =  (_.human_impact * _.Acres).sum() / _.Acres.sum(),
+                   svi =  (_.svi * _.Acres).sum() / _.Acres.sum(),
+                   svi_socioeconomic_status =  (_.svi_socioeconomic_status * _.Acres).sum() / _.Acres.sum(),
+                   svi_household_char =  (_.svi_household_char * _.Acres).sum() / _.Acres.sum(),
+                   svi_racial_ethnic_minority =  (_.svi_racial_ethnic_minority * _.Acres).sum() / _.Acres.sum(),
+                   svi_housing_transit =  (_.svi_housing_transit * _.Acres).sum() / _.Acres.sum(),
+
+                   # biodiversity_intactness_loss = (_.biodiversity_intactness_loss * _.Acres).sum() / _.Acres.sum(),
+                   # crop_reduction = (_.crop_reduction * _.Acres).sum() / _.Acres.sum(),
+                   # crop_expansion = (_.crop_expansion * _.Acres).sum() / _.Acres.sum(),
+                   # forest_loss = (_.forest_integrity_loss * _.Acres).sum() / _.Acres.sum(),
                   )
         .mutate(percent_protected=_.percent_protected.round(1))
          )
 
-
-
     if colors is not None and not colors.empty: #
         df = df.inner_join(colors, column) # chart colors 
              
-    
     df = df.cast({col: "string" for col in column})
     df = df.to_pandas()
     return df
 
 
-def summary_table(column, colors, filter_cols, filter_vals):
-    
+def summary_table(column, colors, filter_cols, filter_vals,colorby_vals):
     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 variable in table
+        filter_cols.append(column)
+        filters.append(getattr(_, column).isin(colorby_vals[column]))  
+        
     combined_filter = reduce(lambda x, y: x & y, filters)
 
     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
-    
     return df, df_tab
 
 
-
-
 def area_plot(df, column):
     base = alt.Chart(df).encode(
         alt.Theta("percent_protected:Q").stack(True),
@@ -167,7 +172,6 @@ def get_pmtiles_style(paint, alpha, cols, values):
             "id": "ca30x30",
             "source": "ca",
             "source-layer": "layer",
-
             # "source-layer": "ca202430m",
             # "source-layer": "ca2024",
             "type": "fill",
@@ -209,6 +213,12 @@ default_gap = {
     2: True,  
 }
 
+def getColorVals(style_options, style_choice): #adding "color by" values to table
+    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',
@@ -274,24 +284,24 @@ gap = {
         }
 
 
-area_type = {
-        'property': 'type',
-        'type': 'categorical',
-        'stops': [
-            ["Land", "green"],
-            ["Water", "blue"]
-        ]
-        }
+# area_type = {
+#         'property': 'type',
+#         'type': 'categorical',
+#         'stops': [
+#             ["Land", "green"],
+#             ["Water", "blue"]
+#         ]
+#         }
 
 
 style_options = {
-                    "GAP Status Code": gap,
-                    "Manager Type": manager,
-                    "Easement": easement,
-                    "Public Access": access,
-                    "Year": year,
-                    "Type": area_type
-                    }
+                "Year": year,
+                "GAP Status Code": gap,
+                "Manager Type": manager,
+                "Easement": easement,
+                "Public Access": access,
+                # "Type": area_type
+                }
 
 
 
@@ -321,6 +331,29 @@ justice40_style = {
 
 
 
+    
+sv_pmtiles = "https://data.source.coop/cboettig/social-vulnerability/svi2020_us_county.pmtiles"
+def get_sv_style(column):
+    return  {
+            "layers": [
+                {
+                    "id": "SVI",
+                    "source": "Social Vulnerability Index",
+                    "source-layer": "SVI2020_US_county",
+                    "filter": ["match", ["get", "STATE"], "California", True, False],
+                    "type": "fill",
+                    "paint": {
+                        "fill-color": 
+                            ["interpolate", ["linear"], ["get", column],
+                    0, "#FFE6EE",
+                    1, "#850101"] 
+                            
+                        }
+                    }
+            ]
+    }
+
+
 
 st.set_page_config(layout="wide", page_title="CA Protected Areas Explorer", page_icon=":globe:")
 
@@ -333,9 +366,11 @@ m = leafmap.Map(style="positron")
 filters = {}
 
 with st.sidebar:
-    color_choice = st.radio("Color by:", style_options)
+    color_choice = st.radio("Color by:", style_options)      
+    colorby_vals = getColorVals(style_options, color_choice)
+    
     alpha = st.slider("transparency", 0.0, 1.0, 0.5)
-    # st.text("Filters:")
+    st.divider()
     "Filters:"
     for label in style_options:
         with st.expander(label): 
@@ -357,85 +392,108 @@ with st.sidebar:
     style = get_pmtiles_style(style_options[color_choice], alpha, filter_cols, filter_vals)
     m.add_pmtiles(ca_pmtiles, style=style, visible=True, name="CA", opacity=alpha, tooltip=True)
 
+    st.divider()
     "Data Layers:"
 
-    with st.expander("Justice40"):
-        justice40_toggle = st.toggle("Justice40")
-        if justice40_toggle:
-            m.add_pmtiles(justice40, style=justice40_style, visible=True, name="Justice40", opacity=alpha, tooltip=True)
-
     with st.expander("🦜 Biodiversity"):
+        alpha_bio = st.slider("transparency", 0.0, 1.0, 0.4, key = "biodiversity")
+
         show_richness = st.toggle("Species Richness", False)
         if show_richness:
-            m.add_tile_layer(url = "https://huggingface.co/datasets/boettiger-lab/ca-30x30/resolve/main/species-richness-ca/{z}/{x}/{y}.png",
+            m.add_tile_layer(
+                url = "https://huggingface.co/datasets/boettiger-lab/ca-30x30/resolve/main/species-richness-ca/{z}/{x}/{y}.png",
                 name="MOBI Species Richness",
-                opacity=0.4)
+                opacity=alpha_bio
+            )
+            
         show_rsr = st.toggle("Range-Size Rarity")
         if show_rsr:           
-            m.add_tile_layer(url="https://huggingface.co/datasets/boettiger-lab/ca-30x30/resolve/main/range-size-rarity/{z}/{x}/{y}.png",
+            m.add_tile_layer(
+                url="https://huggingface.co/datasets/boettiger-lab/ca-30x30/resolve/main/range-size-rarity/{z}/{x}/{y}.png",
                 name="MOBI Range-Size Rarity",
-                opacity=0.9)
-
-    
+                opacity=alpha_bio)
+         
     with st.expander("⛅ Carbon & Climate"):
-        show_carbon_lost = st.toggle("Carbon Lost (2002-2022)")
-        if show_carbon_lost:
-            m.add_tile_layer(url="https://huggingface.co/datasets/boettiger-lab/ca-30x30/resolve/main/deforest-carbon-ca/{z}/{x}/{y}.png",
-                name="Carbon Lost (2002-2022)",
-                opacity=0.9)
-        
-        
+        alpha_climate = st.slider("transparency", 0.0, 1.0, 0.3, key = "climate")
+
         show_irrecoverable_carbon = st.toggle("Irrecoverable Carbon")
         if show_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 = 0.5, fit_bounds=False)
-            
+            m.add_cog_layer("https://huggingface.co/datasets/boettiger-lab/ca-30x30/resolve/main/ca_irrecoverable_c_2018_cog.tif",
+                            palette="reds", name="Irrecoverable Carbon", transparent_bg=True, opacity = alpha_climate, fit_bounds=False, bidx=[1])
+
         show_manageable_carbon = st.toggle("Manageable Carbon")
         if show_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 = 0.5, fit_bounds=False)
+           m.add_cog_layer("https://huggingface.co/datasets/boettiger-lab/ca-30x30/resolve/main/ca_manageable_c_2018_cog.tif",
+                            palette="purples", name="Manageable Carbon", transparent_bg=True, opacity = alpha_climate, fit_bounds=False)
+          
+    with st.expander("Climate and Economic Justice"):
+        alpha_justice40 = st.slider("transparency", 0.0, 1.0, 0.3, key = "social justice")
+
+        show_justice40 = st.toggle("Justice40")
+        if show_justice40:
+            m.add_pmtiles(justice40, style=justice40_style, visible=True, name="Justice40", opacity=alpha_justice40, tooltip=False, fit_bounds = False)
+
+    with st.expander("Social Vulnerability"):
+        alpha_justice = st.slider("transparency", 0.0, 1.0, 0.3, key = "SVI")
+
+        show_sv = st.toggle("Social Vulnerability Index (SVI)")
+        if show_sv:
+            m.add_pmtiles(sv_pmtiles, style = get_sv_style("RPL_THEMES") ,visible=True, opacity=alpha_justice, tooltip=False, fit_bounds = False)
+
+        show_sv_socio = st.toggle("Socioeconomic Status")
+        if show_sv_socio:
+            m.add_pmtiles(sv_pmtiles, style = get_sv_style("RPL_THEME1") ,visible=True, opacity=alpha_justice, tooltip=False, fit_bounds = False)
+            
+        show_sv_household = st.toggle("Household Characteristics")
+        if show_sv_household:
+            m.add_pmtiles(sv_pmtiles, style = get_sv_style("RPL_THEME2") ,visible=True, opacity=alpha_justice, tooltip=False, fit_bounds = False)
 
+        show_sv_minority = st.toggle("Racial & Ethnic Minority Status")
+        if show_sv_minority:
+            m.add_pmtiles(sv_pmtiles, style = get_sv_style("RPL_THEME3") ,visible=True, opacity=alpha_justice, tooltip=False, fit_bounds = False)
+
+        show_sv_housing = st.toggle("Housing Type & Transportation")
+        if show_sv_housing:
+            m.add_pmtiles(sv_pmtiles, style = get_sv_style("RPL_THEME4") ,visible=True, opacity=alpha_justice, tooltip=False, fit_bounds = False)
+
+            
     with st.expander("🚜 Human Impacts"):
+        alpha_hi = st.slider("transparency", 0.0, 1.0, 0.5, key = "hi")
+
+        show_carbon_lost = st.toggle("Carbon Lost (2002-2022)")
+        if show_carbon_lost:
+            m.add_tile_layer(
+                url="https://huggingface.co/datasets/boettiger-lab/ca-30x30/resolve/main/deforest-carbon-ca/{z}/{x}/{y}.png",
+                name="Carbon Lost (2002-2022)",
+                opacity = alpha_hi)
+
         show_human_impact = st.toggle("Human Impact")
         if show_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 = 0.5, fit_bounds=False)
-
-        
-        show_crop_expansion = st.toggle("Cropland Expansion")
-        
-        if show_crop_expansion:
-            m.add_cog_layer("https://data.source.coop/vizzuality/lg-land-carbon-data/natcrop_expansion_100m_cog.tif",
-                            name="Cropland Expansion", transparent_bg=True, opacity = 0.5, fit_bounds=False)           
-        show_biodiversity_inactness_loss = st.toggle("Biodiversity Intactness Loss")
-        if show_biodiversity_inactness_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 = 0.5, fit_bounds=False)
+            m.add_cog_layer(
+                url = "https://huggingface.co/datasets/boettiger-lab/ca-30x30/resolve/main/ca_human_impact_cog.tif", name="Human Impact", transparent_bg=True, opacity = alpha_hi, fit_bounds=False)
 
-        show_forest_integrity_loss = st.toggle("Forest Integrity Loss")
-        if show_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 = 0.5, fit_bounds=False)
 
 
 select_column = {
+                "Year": "established",
                 "GAP Status Code": "reGAP",
                 "Manager Type": "manager_type",
                 "Easement": "Easement",
-                "Year": "established",
                 "Public Access": "access_type",
-                "Type": "type"}
+                # "Type": "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"],
-                "Year": year["stops"],
-                "GAP Status Code": gap["stops"],
-                "Type": area_type["stops"]}
+                # "Type": area_type["stops"]
+                }
 
 colors = (ibis
           .memtable(select_colors[color_choice], columns = [column, "color"])
@@ -443,28 +501,46 @@ colors = (ibis
          )
 
 main = st.container()
-    
+df,df_tab = summary_table(column, colors, filter_cols, filter_vals, colorby_vals)
+
 with main:
     map_col, stats_col = st.columns([2,1])
     with map_col:
         to_streamlit(m, height=700)
-    df,df_tab = summary_table(column, colors, filter_cols, filter_vals)
+        st.dataframe(df_tab, use_container_width = True)
+    svi = bar_chart(df, column, 'svi')
+    svi_socioeconomic_status = bar_chart(df, column, 'svi_socioeconomic_status')
+    svi_household_char = bar_chart(df, column, 'svi_household_char')
+    svi_racial_ethnic_minority = bar_chart(df, column, 'svi_racial_ethnic_minority')
+    svi_housing_transit = bar_chart(df, column, 'svi_housing_transit')
 
     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')
+    # all_rsr = bar_chart(df, column, 'all_rsr')
+    # all_richness = bar_chart(df, column, 'all_richness')
     irrecoverable_carbon = bar_chart(df, column, 'irrecoverable_carbon')
+    manageable_carbon = bar_chart(df, column, 'manageable_carbon')
+    carbon_lost = bar_chart(df, column, 'carbon_lost')
     human_impact = bar_chart(df, column, 'human_impact')
 
+    # crop_expansion = bar_chart(df, column, 'crop_expansion')
+    # biodiversity_intactness_loss = bar_chart(df, column, biodiversity_intactness_loss')
+    # crop_reduction = bar_chart(df, column, 'crop_reduction')
+    # forest_loss = bar_chart(df, column, 'forest_loss')
+    
+
+
+
+
     total_percent = df.percent_protected.sum().round(1)
     with stats_col:
         with st.container():
             f"{total_percent}% CA Covered"
             st.altair_chart(area_plot(df, column), use_container_width=True)
-            st.dataframe(df_tab)
+            
 
         with st.container():
+
             if show_richness:
                 "Species Richness"
                 st.altair_chart(richness_chart, use_container_width=True)
@@ -473,17 +549,38 @@ with main:
                 "Range-Size Rarity"
                 st.altair_chart(rsr_chart, use_container_width=True)
 
-            if show_carbon_lost:
-                "Carbon Lost ('02-'22)"
-                st.altair_chart(carbon_lost, use_container_width=True)
-
             if show_irrecoverable_carbon:
                 "Irrecoverable Carbon"
                 st.altair_chart(irrecoverable_carbon, use_container_width=True)
 
-            if show_crop_expansion:
-                "Crop Expansion"
-                st.altair_chart(crop_expansion, use_container_width=True)
+            if show_manageable_carbon:
+                "Manageable Carbon"
+                st.altair_chart(manageable_carbon, use_container_width=True)
+
+            if show_sv:
+                "Social Vulnerability Index"
+                st.altair_chart(svi, use_container_width=True)
+                
+            if show_sv_socio:
+                "SVI - Socioeconomic Status"
+                st.altair_chart(svi_socioeconomic_status, use_container_width=True)
+            
+            if show_sv_household:
+                "SVI - Household Characteristics"
+                st.altair_chart(svi_household_char, use_container_width=True)
+            
+            if show_sv_minority:
+                "SVI - Racial and Ethnic Minority"
+                st.altair_chart(svi_racial_ethnic_minority, use_container_width=True)
+            
+            if show_sv_housing:
+                "SVI - Housing Type and Transit"
+                st.altair_chart(svi_housing_transit, use_container_width=True)
+
+            
+            if show_carbon_lost:
+                "Carbon Lost ('02-'22)"
+                st.altair_chart(carbon_lost, use_container_width=True)
 
             if show_human_impact:
                 "Human Impact"
@@ -505,6 +602,10 @@ License: BSD-2-clause
 ### 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
 
+- 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
+
 - Imperiled Species Richness and Range-Size-Rarity from NatureServe (2022). Data: https://beta.source.coop/repositories/cboettig/mobi. License CC-BY-NC-ND
 
 - Carbon-loss and farming impact 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
@@ -513,7 +614,6 @@ License: BSD-2-clause
 
 - 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
 
-- Climate and Economic Justice Screening Tool, US Council on Environmental Quality, Justice40, data: https://beta.source.coop/repositories/cboettig/justice40/description/, License: Public Domain
 
 
 '''

requirements.txt

@@ -9,4 +9,5 @@ rasterio==1.3.10
 shapely==2.0.4
 shiny==0.10.2
 geoarrow-types 
-geoarrow-pandas
+geoarrow-pandas
+leafmap==0.38.12