An interactive cloud-native geospatial tool for exploring and visualizing California\'s protected lands with open data and generative AI.
', unsafe_allow_html = True) ''' - โ Safari/iOS not yet supported. For Safari/iOS users, try [this version](https://huggingface.co/spaces/boettiger-lab/ca-30x30-folium) with similar functionality. - ๐ Use the left sidebar to color-code the map by different attributes **(Group by)**, toggle on data layers and view summary charts **(Data Layers)**, or filter data **(Filters)**. - ๐ฌ For a more tailored experience, query our dataset of protected areas and their precomputed mean values for each of the displayed layers, using the experimental chatbot below. ''' st.divider() ##### Chatbot stuff # create sharable low-level connection, see: https://github.com/Mause/duckdb_engine import sqlalchemy connect_args = {'preload_extensions':['spatial']} eng = sqlalchemy.create_engine("duckdb:///:memory:",connect_args = connect_args) # ibis can talk to this connection and create the VIEW con = ibis.duckdb.from_connection(eng.raw_connection()) tbl = con.read_parquet(ca_parquet, "mydata") # langchain can also talk to this connection and see the table: from langchain_community.utilities import SQLDatabase db = SQLDatabase(eng, view_support=True) from pydantic import BaseModel, Field class SQLResponse(BaseModel): """Defines the structure for SQL response.""" sql_query: str = Field(description="The SQL query generated by the assistant.") explanation: str = Field(description="A detailed explanation of how the SQL query answers the input question.") from langchain.chains import create_sql_query_chain template = '''You are an expert in SQL and an assistant for mapping and analyzing California land data. Given an input question, create a syntactically correct {dialect} query to run, and then provide an explanation of how you answered the input question. For example: {{ "sql_query": "SELECT * FROM my_table WHERE condition = 'value';", "explanation": "This query retrieves all rows from my_table where the condition column equals 'value'." }} Ensure the response contains only this JSON object, with no additional text, formatting, or commentary. # Important Details - For map-related queries (e.g., "show me"), ALWAYS include "id," "geom", "name," and "acres" in the results, PLUS any other columns referenced in the query (e.g., in conditions, calculations, or subqueries). This output structure is MANDATORY for all map-related queries. - ONLY use LIMIT in your SQL queries if the user specifies a quantity (e.g., 'show me 5'). Otherwise, return all matching data without a limit. - Wrap each column name in double quotes (") to denote them as delimited identifiers. - Pay attention to use only the column names you can see in the tables below. DO NOT query for columns that do not exist. If the query mentions "biodiversity" without specifying a column, default to using "richness" (species richness). Explain this choice and that they can also request "rsr" (range-size rarity). - If the query mentions carbon without specifying a column, use "irrecoverable carbon". Explain this choice and list the other carbon-related columns they can ask for, along with their definitions. - If the query asks about the manager, use the "manager" column. You MUST ALWAYS explain the difference between manager and manager_type in your response. Clarify that "manager" refers to the name of the managing entity (e.g., an agency), while "manager_type" specifies the type of jurisdiction (e.g., Federal, State, Non Profit). Also, let the user know they can include "manager_type" in their query if they want to refine their results. - If the user's query is unclear, DO NOT make assumptions. Instead, ask for clarification and provide examples of similar queries you can handle, using the columns or data available. You MUST ONLY deliver accurate results. - If you are mapping the data, explicitly state that the data is being visualized on a map. ALWAYS include a statement encouraging the user to examine the queried data below the map, as some areas may be too small at the current zoom level. - Users may not be familiar with this data, so your explanation should be short, clear, and easily understandable. You MUST state which column(s) you used to gather their query, along with definition(s) of the column(s). Do NOT explain SQL commands. - If the prompt is unrelated to the California dataset, provide examples of relevant queries that you can answer. # Example Questions and How to Approach Them ## Example: example_user: "Show me all non-profit land." example_assistant: {{"sql_query": SELECT id, geom, name, acres FROM mydata WHERE "manager_type" = "Non Profit"; "explanation":"I selected all data where `manager_type` is 'Non Profit'." }} ## Example: example_user: "Which gap code has been impacted the most by fire?" example_assistant: {{"sql_query": SELECT "reGAP", SUM("percent_fire_10yr") AS temp FROM mydata GROUP BY "reGAP" ORDER BY temp ASC LIMIT 1; "explanation":"I used the `percent_fire_10yr` column, which shows the percentage of each area burned over the past 10 years (2013โ2022), summing it for each GAP code to find the one with the highest total fire impact." }} ## Example: example_user: "Who manages the land with the worst biodiversity and highest SVI?" example_assistant: {{"sql_query": SELECT manager,richness, svi FROM mydata GROUP BY "manager" ORDER BY richness ASC, svi DESC LIMIT 1; "explanation": "I identified the land manager with the worst biodiversity and highest Social Vulnerability Index (SVI) by analyzing the columns: `richness`, which measures species richness, and `svi`, which represents social vulnerability based on factors like socioeconomic status, household characteristics, racial & ethnic minority status, and housing & transportation. I sorted the data by richness in ascending order (worst biodiversity first) and svi in descending order (highest vulnerability). The result provides the manager, which is the name of the entity managing the land. Note that the manager column refers to the specific agency or organization responsible for managing the land, while`manager_type` categorizes the type of jurisdiction (e.g., Federal, State, Non Profit)." }} ## Example: example_user: "Show me the biggest protected area" example_assistant: {{"sql_query": SELECT "id", "geom", "name", "acres", "manager", "manager_type", "acres" FROM mydata ORDER BY "acres" DESC LIMIT 1; "explanation": "I identified the biggest protected area by sorting the data in descending order based on the `acres` column, which represents the size of each area." ## Example: example_user: "Show me the 50 most biodiverse areas found in disadvantaged communities." example_assistant: {{"sql_query": SELECT "id", "geom", "name", "acres", "richness", "percent_disadvantaged" FROM mydata WHERE "percent_disadvantaged" > 0 ORDER BY "richness" DESC LIMIT 50; "explanation": "I used the `richness` column to measure biodiversity and the `percent_disadvantaged` column to identify areas located in disadvantaged communities. The `percent_disadvantaged` value is derived from the Justice40 initiative, which identifies communities burdened by systemic inequities and vulnerabilities across multiple domains, including climate resilience, energy access, health disparities, housing affordability, pollution exposure, transportation infrastructure, water quality, and workforce opportunities. The results are sorted in descending order by biodiversity richness (highest biodiversity first), and only areas with a `percent_disadvantaged` value greater than 0 (indicating some portion of the area overlaps with a disadvantaged community) are included." }} ## Example: example_user: "Show me federally managed gap 3 lands that are in the top 5% of biodiversity richness and have experienced forest fire over at least 50% of their area" sql_query: WITH temp_tab AS ( SELECT PERCENTILE_CONT(0.95) WITHIN GROUP (ORDER BY "richness") AS temp FROM mydata ) SELECT "id", "geom", "name", "acres","richness", "reGAP" FROM mydata WHERE "reGAP" = 3 AND "percent_fire_10yr" >= 0.5 and "manager_type" = "Federal" AND "richness" > (SELECT temp FROM temp_tab); ## Example: example_user: "What is the total acreage of areas designated as easements? sql_query: SELECT SUM("acres") AS total_acres FROM mydata WHERE "easement" = "True"; # Detailed Explanation of the Columns in the California Dataset - "established": The time range which the land was acquired, either "2024" or "pre-2024". - "reGAP": The GAP status code; corresponds to the level of protection the area has. There are 4 gap codes and are defined as the following. Status 1: Permanently protected to maintain a natural state, allowing natural disturbances or mimicking them through management. Status 2: Permanently protected but may allow some uses or management practices that degrade natural communities or suppress natural disturbances. Status 3: Permanently protected from major land cover conversion but allows some extractive uses (e.g., logging, mining) and protects federally listed species. Status 4: No protection mandates; land may be converted to unnatural habitat types or its management intent is unknown. - "name": The name of a protected area. The user may use a shortened name and/or not capitalize it. For example, "redwoods" may refer to "Redwood National Park", or "klamath" refers to "Klamath National Forest". Another example, "san diego wildlife refuge" could refer to multiple areas, so you would use "WHERE LOWER("name") LIKE '%san diego%' AND LOWER("name") LIKE '%wildlife%' AND LOWER("name") LIKE '%refuge%';" in your SQL query, to ensure that it is case-insensitive and matches any record that includes our phrases, because we don't want to overlook a match. If the name isn't capitalized, you MUST ensure the search is case-insensitive by converting "name" to lowercase. The names of the largest parks are {names}. - "access_type": Level of access to the land: "Unknown Access","Restricted Access","No Public Access" and "Open Access". - "manager": The name of land manager for the area. Also referred to as the agency name. These are the manager names: {managers}. Users might use acronyms or could omit "United States" in the agency name, make sure to use the name used in the table. Some examples: "BLM" or "Bureau of Land Management" refers to the "United States Bureau of Land Management" or "CDFW" is "California Department of Fish and Wildlife". Similar to the "name" field, you can search for managers using "LIKE" in the SQL query. - "manager_type": The jurisdiction of the land manager: "Federal","State","Non Profit","Special District","Unknown","County","City","Joint","Tribal","Private","HOA". If the user says "non-profit", do not use a hyphen in your query. - "easement": Boolean value; whether or not the land is an easement. - "acres": Land acreage; measures the size of the area. - "id": unique id for each area. This is necessary for displaying queried results on a map. - "type": Physical type of area, either "Land" or "Water". - "richness": Species richness; higher values indicate better biodiversity. - "rsr": Range-size rarity; higher values indicate better rarity metrics. - "svi": Social Vulnerability Index based on 4 themes: socioeconomic status, household characteristics, racial & ethnic minority status, and housing & transportation. Higher values indicate greater vulnerability. - Themes: - "svi_socioeconomic_status": Poverty, unemployment, housing cost burden, education, and health insurance. - "svi_household_char": Age, disability, single-parent households, and language proficiency. - "svi_racial_ethnic_minority": Race and ethnicity variables. - "svi_housing_transit": Housing type, crowding, vehicles, and group quarters. - "percent_disadvantaged": Justice40-defined disadvantaged communities overburdened by climate, energy, health, housing, pollution, transportation, water, and workforce factors. Higher values indicate more disadvantage. Range is between 0 and 1. - "deforest_carbon": Carbon emissions due to deforestation. - "human_impact": A score representing the human footprint: cumulative anthropogenic impacts such as land cover change, population density, and infrastructure. - "percent_fire_10yr": The percentage of the area burned by fires from (2013-2022). Range is between 0 and 1. - "percent_rxburn_10yr": The percentage of the area affected by prescribed burns from (2013-2022). Range is between 0 and 1. Only use the following tables: {table_info}. Question: {input}''' from langchain_openai import ChatOpenAI # os.environ["OPENAI_API_KEY"] = st.secrets["LITELLM_KEY"] # llm = ChatOpenAI(model="gorilla", temperature=0, base_url="https://llm.nrp-nautilus.io/") # llm = ChatOpenAI(model = "llama3", api_key=st.secrets["LITELLM_KEY"], base_url = "https://llm.nrp-nautilus.io", temperature=0) llm = ChatOpenAI(model="gpt-4", temperature=0) managers = tbl.sql("SELECT DISTINCT manager FROM mydata;").execute() names = tbl.sql("SELECT name FROM mydata GROUP BY name HAVING SUM(acres) >10000;").execute() from langchain_core.prompts import ChatPromptTemplate prompt = ChatPromptTemplate.from_messages([ ("system", template), ("human", "{input}") ]).partial(dialect="duckdb", table_info = ca.schema(), managers = managers, names = names) structured_llm = llm.with_structured_output(SQLResponse) few_shot_structured_llm = prompt | structured_llm # @st.cache_data(ttl=600) # Cache expires every 10 minutes def run_sql(query,color_choice): """ Filter data based on an LLM-generated SQL query and return matching IDs. Args: query (str): The natural language query to filter the data. color_choice (str): The column used for plotting. """ output = few_shot_structured_llm.invoke(query) sql_query = output.sql_query explanation =output.explanation if not sql_query: # if the chatbot can't generate a SQL query. st.success(explanation) return pd.DataFrame({'id' : []}) result = tbl.sql(sql_query).execute() if result.empty : explanation = "This query did not return any results. Please try again with a different query." st.warning(explanation, icon="โ ๏ธ") st.caption("SQL Query:") st.code(sql_query,language = "sql") if 'geom' in result.columns: return result.drop('geom',axis = 1) else: return result elif ("id" and "geom" in result.columns): style = get_pmtiles_style_llm(style_options[color_choice], result["id"].tolist(),alpha) legend_d = {cat: color for cat, color in style_options[color_choice]['stops']} m.add_legend(legend_dict=legend_d, position='bottomleft') m.add_pmtiles(ca_pmtiles, style=style, tooltip=True, zoom_to_layer=False) m.zoom_to_bounds(result.total_bounds.tolist()) result = result.drop('geom',axis = 1) #printing to streamlit so I need to drop geom else: st.write(result) # if we aren't mapping, just print out the data with st.popover("Explanation"): st.write(explanation) st.caption("SQL Query:") st.code(sql_query,language = "sql") return result def summary_table_sql(column, colors, ids): # get df for charts + df_tab for printed table filters = [_.id.isin(ids)] 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 return df chatbot_toggles = {key: False for key in [ 'richness', 'rsr', 'irrecoverable_carbon', 'manageable_carbon', 'percent_fire_10yr', 'percent_rxburn_10yr', 'percent_disadvantaged', 'svi', 'svi_socioeconomic_status', 'svi_household_char', 'svi_racial_ethnic_minority', 'svi_housing_transit', 'deforest_carbon', 'human_impact' ]} ############# # m = leafmap.Map(style="positron") m = leafmap.Map() m.add_basemap("CartoDB.PositronNoLabels") filters = {} with st.sidebar: color_choice = st.radio("Group by:", style_options, key = "color", help = "Select a category to change map colors and chart groupings.") colorby_vals = getColorVals(style_options, color_choice) #get options for selected color_by column # alpha = st.slider("transparency", 0.0, 1.0, 0.7) alpha = 0.8 st.divider() ##### Chatbot with st.container(): with st.popover("โน๏ธ Example Queries"): ''' Mapping queries: - Show me areas open to the public that are in the top 10% of species richness. - Show me all GAP 1 and 2 lands managed by The Nature Conservancy. - Show me state land smaller than 1000 acres, with a social vulnerability index in the 90th percentile. - Show me GAP 3 and 4 lands managed by BLM in the top 5% of range-size rarity. - Show me Joshua Tree National Park. - Show me all protected lands that have experienced forest fire over at least 50% of their area. - Show me the biggest protected area in California. - Show me all land managed by the United States Forest Service. ''' ''' Exploratory data queries: - What is a GAP code? - What is the total acreage of areas designated as easements? - Which GAP code has been impacted the most by fire? - Who manages the land with the highest amount of irrecoverable carbon and highest social vulnerability index? ''' st.info('If the map appears blank, queried data may be too small to see at the default zoom level. Check the table below the map, as query results will also be displayed there.', icon="โน๏ธ") example_query = "๐ Input query here" if prompt := st.chat_input(example_query, key="chain", max_chars = 300): st.chat_message("user").write(prompt) try: with st.chat_message("assistant"): with st.spinner("Invoking query..."): out = run_sql(prompt,color_choice) if ("id" in out.columns) and (not out.empty): ids = out['id'].tolist() cols = out.columns.tolist() chatbot_toggles = { key: (True if key in cols else value) for key, value in chatbot_toggles.items() } else: ids = [] except Exception as e: error_message = f"ERROR: An unexpected error has occured with the following query:\n\n*{prompt}*\n\n which raised the following error:\n\n{type(e)}: {e}\n" st.warning("Please try again with a different query", icon="โ ๏ธ") st.write(error_message) st.stop() ## Data Layers with st.sidebar: st.markdown('', help = "Select data layers to visualize on the map. Summary charts will update based on the displayed layers.", unsafe_allow_html= True) # Biodiversity Section with st.expander("๐ฆ Biodiversity"): a_bio = st.slider("transparency", 0.0, 1.0, 0.1, key = "biodiversity") show_richness = st.toggle("Species Richness", key = "richness", value=chatbot_toggles['richness']) show_rsr = st.toggle("Range-Size Rarity", key = "rsr", value=chatbot_toggles['rsr']) 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("โ Carbon & Climate"): a_climate = st.slider("transparency", 0.0, 1.0, 0.15, key = "climate") show_irrecoverable_carbon = st.toggle("Irrecoverable Carbon", key = "irrecoverable_carbon", value=chatbot_toggles['irrecoverable_carbon']) show_manageable_carbon = st.toggle("Manageable Carbon", key = "manageable_carbon", value=chatbot_toggles['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, zoom_to_layer=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, zoom_to_layer=False) # Justice40 Section with st.expander("๐ฑ Climate & Economic Justice"): a_justice = st.slider("transparency", 0.0, 1.0, 0.07, key = "social justice") show_justice40 = st.toggle("Disadvantaged Communities (Justice40)", key = "percent_disadvantaged", value=chatbot_toggles['percent_disadvantaged']) 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("๐ก Social Vulnerability"): a_svi = st.slider("transparency", 0.0, 1.0, 0.1, key = "SVI") show_sv = st.toggle("Social Vulnerability Index (SVI)", key = "svi", value=chatbot_toggles['svi']) show_sv_socio = st.toggle("Socioeconomic Status", key = "svi_socioeconomic_status", value=chatbot_toggles['svi_socioeconomic_status']) show_sv_household = st.toggle("Household Characteristics", key = "svi_household_char", value=chatbot_toggles['svi_household_char']) show_sv_minority = st.toggle("Racial & Ethnic Minority Status", key = "svi_racial_ethnic_minority", value=chatbot_toggles['svi_racial_ethnic_minority']) show_sv_housing = st.toggle("Housing Type & Transportation", key = "svi_housing_transit", value=chatbot_toggles['svi_housing_transit']) 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) # Fire Section with st.expander("๐ฅ Fire"): a_fire = st.slider("transparency", 0.0, 1.0, 0.15, key = "fire") show_fire_10 = st.toggle("Fires (2013-2022)", key = "percent_fire_10yr", value=chatbot_toggles['percent_fire_10yr']) show_rx_10 = st.toggle("Prescribed Burns (2013-2022)", key = "percent_rxburn_10yr", value=chatbot_toggles['percent_rxburn_10yr']) 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_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) # HI Section with st.expander("๐ Human Impacts"): a_hi = st.slider("transparency", 0.0, 1.0, 0.1, key = "hi") show_carbon_lost = st.toggle("Deforested Carbon", key = "deforest_carbon", value=chatbot_toggles['deforest_carbon']) show_human_impact = st.toggle("Human Footprint", key = "human_impact", value=chatbot_toggles['human_impact']) 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, zoom_to_layer=False) st.divider() st.markdown('', help = "Apply filters to adjust what data is shown on the map.", unsafe_allow_html= True) 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 = [] if 'out' not in locals(): # 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", "Access Type": "access_type", } column = select_column[color_choice] select_colors = { "Year": year["stops"], "GAP Status Code": gap["stops"], "Manager Type": manager["stops"], "Easement": easement["stops"], "Access Type": 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) if 'out' not in locals(): df,df_tab = summary_table(column, colors, filter_cols, filter_vals, colorby_vals) else: df = summary_table_sql(column, colors, ids) total_percent = df.percent_protected.sum().round(1) # charts displayed based on color_by variable richness_chart = bar_chart(df, column, 'mean_richness', "Species Richness") rsr_chart = bar_chart(df, column, 'mean_rsr', "Range-Size Rarity") irr_carbon_chart = bar_chart(df, column, 'mean_irrecoverable_carbon', "Irrecoverable Carbon") man_carbon_chart = bar_chart(df, column, 'mean_manageable_carbon', "Manageable Carbon") fire_10_chart = bar_chart(df, column, 'mean_percent_fire_10yr', "Fires (2013-2022)") rx_10_chart = bar_chart(df, column, 'mean_percent_rxburn_10yr',"Prescribed Burns (2013-2022)") justice40_chart = bar_chart(df, column, 'mean_percent_disadvantaged', "Disadvantaged Communities (Justice40)") svi_chart = bar_chart(df, column, 'mean_svi', "Social Vulnerability Index") svi_socio_chart = bar_chart(df, column, 'mean_svi_socioeconomic_status', "SVI - Socioeconomic Status") svi_house_chart = bar_chart(df, column, 'mean_svi_household_char', "SVI - Household Characteristics") svi_minority_chart = bar_chart(df, column, 'mean_svi_racial_ethnic_minority', "SVI - Racial and Ethnic Minority") svi_transit_chart = bar_chart(df, column, 'mean_svi_housing_transit', "SVI - Housing Type and Transit") carbon_loss_chart = bar_chart(df, column, 'mean_carbon_lost', "Deforested Carbon (2002-2022)") hi_chart = bar_chart(df, column, 'mean_human_impact', "Human Footprint (2017-2021)") main = st.container() with main: map_col, stats_col = st.columns([2,1]) with map_col: m.to_streamlit(scrolling = True) if 'out' not in locals(): st.dataframe(df_tab, use_container_width = True) else: st.dataframe(out, use_container_width = True) with stats_col: with st.container(): st.markdown(f"{total_percent}% CA Covered", help = "Updates based on displayed data") st.altair_chart(area_plot(df, 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_10: # "Fires (2013-2022)" st.altair_chart(fire_10_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_justice40: # "Disadvantaged Communities (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 Californiaโs 30x30 framework, only GAP codes 1 and 2 are counted toward the conservation goal.") ''' ## Credits Authors: Cassie Buhler & Carl Boettiger, UC Berkeley License: BSD-2-clause Data: https://huggingface.co/datasets/boettiger-lab/ca-30x30 ### Data sources - CA Nature Terrestrial 30x30 Conserved Areas map layer 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 Tract. 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 '''