Update src/streamlit_app.py

src/streamlit_app.py

@@ -269,83 +269,61 @@ fig.update_layout(
 # 4. Render
 st.plotly_chart(fig, use_container_width=True)
 
-# -------------------------------- Plot 7: Bubble Map of Incident Counts by Region NO MAP --------------------------------
-st.markdown("<div class='sectionheader'>Crime Hotspots by Region NO MAP</div>", unsafe_allow_html=True)
-
-# 1. Aggregate total incidents by region and pick top 10
-region_counts = (
+# Aggregate counts by region & crime
+sun = (
     df
-      .groupby("RegionName")            # group by your text field
-      .size()                           # count rows
-      .reset_index(name="Count")        # turn it into a DataFrame with columns RegionName & Count
-)
-top_regions = region_counts.head(10)
-st.write(top_regions)
-# 2. Build the bubble chart
-fig = px.scatter(
-    top_regions,
-    x='Count',
-    y='RegionName',
-    size='Count',               # bubble area ∝ incident count
-    color='Count',              # color scale also shows volume
-    hover_name='RegionName',        # show region on hover
-    hover_data={'Count':True},
-    size_max=60,                # max bubble diameter
-    title='Top 10 Regions by Crime Volume (Bubble Chart)'
-)
-
-# 3. Tweak layout
-fig.update_layout(
-    xaxis_tickangle=-45,        # tilt x-labels so they’re legible
-    margin=dict(t=50, b=100),
-    yaxis_title='Incident Count',
-    xaxis_title=''
-)
-
-# 4. Render in Streamlit
-st.plotly_chart(fig, use_container_width=True)
-##########################################################
-# Aggregate total incidents by region and take top 10
-region_counts = (
-    df.groupby("RegionName")
+      .groupby(["RegionName","crm_cd_desc"])
       .size()
       .reset_index(name="Count")
-      .nlargest(10, "Count")
 )
 
-# 2. Compute circle‐packing layout
-circles = circlify.circlify(
-    region_counts["Count"].tolist(),
-    show_enclosure=False,
-    target_enclosure=circlify.Circle(x=0, y=0, r=0.5)
+fig = px.sunburst(
+    sun,
+    path=["RegionName","crm_cd_desc"],
+    values="Count",
+    color="Count",
+    color_continuous_scale="Agsunset",
+    title="Crime Distribution: Region → Crime Type"
 )
+fig.update_layout(margin=dict(t=50, l=25, r=25, b=25))
+st.plotly_chart(fig, use_container_width=True)
 
-# 3. Plot packed bubbles
-fig, ax = plt.subplots(figsize=(3, 3))
-ax.axis("off")
 
-# Determine plot limits so everything fits
-lim = max(
-    abs(circle.x) + circle.r
-    for circle in circles
-)
-ax.set_xlim(-lim, lim)
-ax.set_ylim(-lim, lim)
-
-# Draw each circle
-for circle, (_, row) in zip(circles, region_counts.iterrows()):
-    x, y, r = circle.x, circle.y, circle.r
-    ax.add_patch(plt.Circle((x, y), r, alpha=0.6, linewidth=2, ec="white"))
-    ax.text(
-        x, y,
-        f"{row['RegionName']}\n{row['Count']:,}",
-        ha="center", va="center", fontsize=8, color="#222"
-    )
-
-ax.set_title("Packed Bubble: Top 15 Regions by Incident Count", fontsize=12, pad=10)
-
-# 4. Render in Streamlit
-st.pyplot(fig)
+
+# -------------------------------- Plot 7: Bubble Map of Incident Counts by Region NO MAP --------------------------------
+# st.markdown("<div class='sectionheader'>Crime Hotspots by Region NO MAP</div>", unsafe_allow_html=True)
+
+# # 1. Aggregate total incidents by region and pick top 10
+# region_counts = (
+#     df
+#       .groupby("RegionName")            # group by your text field
+#       .size()                           # count rows
+#       .reset_index(name="Count")        # turn it into a DataFrame with columns RegionName & Count
+# )
+# top_regions = region_counts.head(10)
+# # 2. Build the bubble chart
+# fig = px.scatter(
+#     top_regions,
+#     x='Count',
+#     y='RegionName',
+#     size='Count',               # bubble area ∝ incident count
+#     color='Count',              # color scale also shows volume
+#     hover_name='RegionName',        # show region on hover
+#     hover_data={'Count':True},
+#     size_max=60,                # max bubble diameter
+#     title='Top 10 Regions by Crime Volume (Bubble Chart)'
+# )
+
+# # 3. Tweak layout
+# fig.update_layout(
+#     xaxis_tickangle=-45,        # tilt x-labels so they’re legible
+#     margin=dict(t=50, b=100),
+#     yaxis_title='Incident Count',
+#     xaxis_title=''
+# )
+
+# # 4. Render in Streamlit
+# st.plotly_chart(fig, use_container_width=True)
 
 # -------------------------------- Plot 2: Heat Map --------------------------------
 st.markdown("<div class='sectionheader'> HeatMap </div>", unsafe_allow_html=True)