commit

app.py

@@ -68,7 +68,7 @@ def filter_data(countries, categories, min_value=None, max_value=None):
     return filtered_df
 
 def create_bar_chart(countries, categories):
-    """Create a bar chart showing value factors by country and category"""
+    """Create a bar chart showing value factors by country and specific impact category"""
     filtered_df = filter_data(countries, categories)
 
     if filtered_df.empty:
@@ -80,16 +80,25 @@ def create_bar_chart(countries, categories):
         )
         return fig
 
-    # Group by country and topic (category)
-    grouped = filtered_df.groupby(['country', 'topic'])['value'].mean().reset_index()
+    # Create a composite key for proper comparison level: category + location + impact
+    filtered_df['impact_category'] = (
+        filtered_df['category'].astype(str) + ' (' +
+        filtered_df['location'].astype(str) + ', ' +
+        filtered_df['impact'].astype(str) + ')'
+    )
+
+    # Group by country and the composite impact category
+    grouped = filtered_df.groupby(['country', 'impact_category', 'topic'])['value'].mean().reset_index()
+
     fig = px.bar(
         grouped,
         x='country',
         y='value',
-        color='topic',
-        title="Value Factors by Country and Category",
-        labels={'value': "Value Factor (USD)", 'country': "Country", 'topic': "Category"},
-        barmode='group'
+        color='impact_category',
+        title="Value Factors by Country and Specific Impact Category",
+        labels={'value': "Value Factor (USD)", 'country': "Country", 'impact_category': "Impact Category"},
+        barmode='group',
+        hover_data=['topic']
     )
 
     fig.update_layout(xaxis_tickangle=-45, height=600)
@@ -130,7 +139,7 @@ def create_map_visualization(countries, categories):
     return fig
 
 def create_comparison_chart(countries, categories):
-    """Create a comparison chart showing categories across selected countries"""
+    """Create a comparison chart showing specific impact categories across selected countries"""
     filtered_df = filter_data(countries, categories)
 
     if filtered_df.empty:
@@ -142,23 +151,32 @@ def create_comparison_chart(countries, categories):
         )
         return fig
 
-    # Group by topic (category) and country
-    grouped = filtered_df.groupby(['topic', 'country'])['value'].mean().reset_index()
+    # Create a composite key for proper comparison level: category + location + impact
+    filtered_df['impact_category'] = (
+        filtered_df['category'].astype(str) + ' (' +
+        filtered_df['location'].astype(str) + ', ' +
+        filtered_df['impact'].astype(str) + ')'
+    )
+
+    # Group by the composite impact category and country
+    grouped = filtered_df.groupby(['impact_category', 'country', 'topic'])['value'].mean().reset_index()
+
     fig = px.bar(
         grouped,
-        x='topic',
+        x='impact_category',
         y='value',
         color='country',
-        title="Category Comparison Across Countries",
-        labels={'value': "Value Factor (USD)", 'topic': "Category"},
-        barmode='group'
+        title="Specific Impact Category Comparison Across Countries",
+        labels={'value': "Value Factor (USD)", 'impact_category': "Impact Category"},
+        barmode='group',
+        hover_data=['topic']
     )
 
     fig.update_layout(xaxis_tickangle=-45, height=600)
     return fig
 
 def create_box_plot(countries, categories):
-    """Create a box plot showing distribution of value factors"""
+    """Create a box plot showing distribution of value factors by specific impact categories"""
     filtered_df = filter_data(countries, categories)
 
     if filtered_df.empty:
@@ -170,13 +188,21 @@ def create_box_plot(countries, categories):
         )
         return fig
 
+    # Create a composite key for proper comparison level: category + location + impact
+    filtered_df['impact_category'] = (
+        filtered_df['category'].astype(str) + ' (' +
+        filtered_df['location'].astype(str) + ', ' +
+        filtered_df['impact'].astype(str) + ')'
+    )
+
     fig = px.box(
         filtered_df,
-        x='topic',
+        x='impact_category',
         y='value',
         color='country',
-        title="Distribution of Value Factors",
-        labels={'value': "Value Factor (USD)", 'topic': "Category"}
+        title="Distribution of Value Factors by Specific Impact Category",
+        labels={'value': "Value Factor (USD)", 'impact_category': "Impact Category"},
+        hover_data=['topic']
     )
 
     fig.update_layout(xaxis_tickangle=-45, height=600)
@@ -223,50 +249,84 @@ with gr.Blocks(title="GVFD Navigator", theme=gr.themes.Soft()) as demo:
     Explore environmental and social impact value factors by country from the IFVI Global Value Factor Database.
 
     This visualization tool allows you to:
-    - Filter by country and impact category
+    - Filter by country and impact topic (Air Pollution, Water Pollution, etc.)
+    - Compare **specific impact categories** (e.g., PM2.5 in Urban areas for Primary Health)
     - View interactive charts and maps
     - Compare value factors across regions
     - Explore detailed data tables
 
+    **Important**: Value factors are comparable at the **category + location + impact** level within each topic.
+    For example, within "Air Pollution", individual measurements like "PM2.5 (Urban, Primary Health)" are comparable across countries.
+
     **Data Source**: [IFVI Global Value Factor Database V2](https://huggingface.co/datasets/danielrosehill/Global-Value-Factor-Database-Refactor-V2)
     """)
 
     with gr.Tabs() as exploration_mode:
-        with gr.Tab("Explore by Country"):
+        with gr.Tab("Air Pollution"):
+            gr.Markdown("""
+            ### Air Pollution Value Factors
+            Explore specific air pollution impact categories (e.g., PM2.5, NOx, SOx) across countries.
+            Each category shows values by location (Urban, Rural, etc.) and impact type (Primary Health, etc.).
+            """)
+            air_country_selector = gr.Dropdown(
+                choices=get_countries(),
+                multiselect=True,
+                label="Select Country/Countries",
+                info="Start typing to search..."
+            )
+            air_refresh_btn = gr.Button("Show Air Pollution Data", variant="primary", size="lg")
+
+        with gr.Tab("Water Pollution"):
             gr.Markdown("""
-            ### Select a Country
-            Choose one or more countries to see all their environmental value factors across different categories.
+            ### Water Pollution Value Factors
+            Explore specific water pollution impact categories across countries.
             """)
-            country_selector = gr.Dropdown(
+            water_poll_country_selector = gr.Dropdown(
                 choices=get_countries(),
                 multiselect=True,
                 label="Select Country/Countries",
                 info="Start typing to search..."
             )
-            country_category_filter = gr.CheckboxGroup(
-                choices=get_categories(),
-                label="Filter by Categories (Optional)",
-                info="Leave empty to show all categories for selected countries"
+            water_poll_refresh_btn = gr.Button("Show Water Pollution Data", variant="primary", size="lg")
+
+        with gr.Tab("Land Use"):
+            gr.Markdown("""
+            ### Land Use and Conservation Value Factors
+            Explore land use and conservation impact categories across countries.
+            """)
+            land_country_selector = gr.Dropdown(
+                choices=get_countries(),
+                multiselect=True,
+                label="Select Country/Countries",
+                info="Start typing to search..."
             )
-            country_refresh_btn = gr.Button("Show Country Data", variant="primary", size="lg")
+            land_refresh_btn = gr.Button("Show Land Use Data", variant="primary", size="lg")
 
-        with gr.Tab("Explore by Category"):
+        with gr.Tab("Waste"):
             gr.Markdown("""
-            ### Select a Category
-            Choose an environmental impact category to compare value factors across different countries.
+            ### Waste Generation Value Factors
+            Explore waste generation impact categories across countries.
             """)
-            category_selector = gr.Dropdown(
-                choices=get_categories(),
+            waste_country_selector = gr.Dropdown(
+                choices=get_countries(),
                 multiselect=True,
-                label="Select Category/Categories"
+                label="Select Country/Countries",
+                info="Start typing to search..."
             )
-            category_country_filter = gr.Dropdown(
+            waste_refresh_btn = gr.Button("Show Waste Data", variant="primary", size="lg")
+
+        with gr.Tab("Water Consumption"):
+            gr.Markdown("""
+            ### Water Consumption Value Factors
+            Explore water consumption impact categories across countries.
+            """)
+            water_cons_country_selector = gr.Dropdown(
                 choices=get_countries(),
                 multiselect=True,
-                label="Filter by Countries (Optional)",
-                info="Leave empty to show all countries for selected categories"
+                label="Select Country/Countries",
+                info="Start typing to search..."
             )
-            category_refresh_btn = gr.Button("Show Category Data", variant="primary", size="lg")
+            water_cons_refresh_btn = gr.Button("Show Water Consumption Data", variant="primary", size="lg")
 
     with gr.Row():
         with gr.Column():
@@ -442,17 +502,38 @@ with gr.Blocks(title="GVFD Navigator", theme=gr.themes.Soft()) as demo:
             get_data_table(countries, categories)
         )
 
-    # Handler for country exploration mode
-    country_refresh_btn.click(
+    # Handler for Air Pollution tab
+    air_refresh_btn.click(
+        fn=update_all,
+        inputs=[air_country_selector, gr.State(["Air Pollution"])],
+        outputs=[bar_chart, map_chart, comparison_chart, box_plot, stats_output, data_table]
+    )
+
+    # Handler for Water Pollution tab
+    water_poll_refresh_btn.click(
+        fn=update_all,
+        inputs=[water_poll_country_selector, gr.State(["Water Pollution"])],
+        outputs=[bar_chart, map_chart, comparison_chart, box_plot, stats_output, data_table]
+    )
+
+    # Handler for Land Use tab
+    land_refresh_btn.click(
+        fn=update_all,
+        inputs=[land_country_selector, gr.State(["Land Use and Conservation"])],
+        outputs=[bar_chart, map_chart, comparison_chart, box_plot, stats_output, data_table]
+    )
+
+    # Handler for Waste tab
+    waste_refresh_btn.click(
         fn=update_all,
-        inputs=[country_selector, country_category_filter],
+        inputs=[waste_country_selector, gr.State(["Waste Generation"])],
         outputs=[bar_chart, map_chart, comparison_chart, box_plot, stats_output, data_table]
     )
 
-    # Handler for category exploration mode
-    category_refresh_btn.click(
+    # Handler for Water Consumption tab
+    water_cons_refresh_btn.click(
         fn=update_all,
-        inputs=[category_country_filter, category_selector],
+        inputs=[water_cons_country_selector, gr.State(["Water Consumption"])],
         outputs=[bar_chart, map_chart, comparison_chart, box_plot, stats_output, data_table]
     )