removed 3rd visualization

app.py

@@ -92,41 +92,4 @@ Future Improvements:
 - Adding filters for building usage or region could enhance the analysis.
 """)
 
-# Remove rows where 'Year Constructed' is missing (NaN)
-filtered_data = data.dropna(subset=['Year Constructed'])
-
-# Visualization 2: Relationship Between Year Constructed and Square Footage
-# This scatter plot explores the relationship between the year a building was constructed and its square footage.
-scatter_plot = (
-    alt.Chart(filtered_data)
-    .mark_circle(size=60)
-    .encode(
-        x=alt.X(
-            "Year Constructed:Q",
-            title="Year Constructed",
-            scale=alt.Scale(domain=[1700, filtered_data['Year Constructed'].max()])
-        ),
-        y=alt.Y("Square Footage:Q", title="Square Footage"),
-        color=alt.Color("Bldg Status:N", title="Building Status"),
-        tooltip=["Agency Name", "Location Name", "Year Constructed", "Square Footage", "Bldg Status"]
-    )
-    .properties(
-        title="Relationship Between Year Constructed and Square Footage",
-        width=550,
-        height=300
-    )
-)
-
-# Explanation for Visualization 2
-st.subheader("Visualization 2: Year Constructed vs. Square Footage")
-st.altair_chart(scatter_plot, use_container_width=True)
-st.text("""
-This scatter plot highlights the relationship between the year a building was constructed and its square footage. 
-Design choices include:
-- The x-axis starts at 1700 to focus on relevant construction years.
-- Points are color-coded by building status to differentiate operational buildings from others.
-- Tooltips provide additional information about each building for interactive exploration.
-Future Improvements:
-- Adding filtering options by county or usage type could make the analysis more granular.
-""")