Update src/streamlit_app.py

src/streamlit_app.py

@@ -228,6 +228,46 @@ criminal threats and minor burglary. Violent acts such as simple assault and rob
 relying on specific numbers. By pairing each slice with its label, the chart provides an immediate intuitive understanding of which crime types contribute most to overall 
 volume and which are comparatively rare, helping stakeholders focus on the offenses that matter most.</div>""",unsafe_allow_html=True)
 
+# -------------------------------- Plot 6: Bubble Map of Incident Counts by Region --------------------------------
+st.markdown("<div class='sectionheader'>Crime Hotspots by Region</div>", unsafe_allow_html=True)
+
+# 1. Aggregate counts and centroids
+region_stats = (
+    df
+      .groupby("RegionName")
+      .agg(
+          Count   = pd.NamedAgg(column="crm_cd_desc", aggfunc="size"),
+          Latitude  = pd.NamedAgg(column="lat", aggfunc="mean"),
+          Longitude = pd.NamedAgg(column="lon", aggfunc="mean")
+      )
+      .reset_index()
+)
+
+# 2. Build the bubble map
+fig = px.scatter_mapbox(
+    region_stats,
+    lat="Latitude",
+    lon="Longitude",
+    size="Count",                       # bubble size ~ incident volume
+    color="Count",                      # color gradient for emphasis
+    hover_name="RegionName",
+    hover_data={"Count":True, "Latitude":False, "Longitude":False},
+    size_max=30,                        # max bubble diameter
+    zoom=10,                            # adjust to focus your city
+    mapbox_style="open-street-map",
+    title="Crime Volume by Region (Bubble Map)"
+)
+
+# 3. Tidy layout
+fig.update_layout(
+    margin=dict(t=50, b=0, l=0, r=0),
+    legend_title_text="Incident Count",
+    title_x=0.5
+)
+
+# 4. Render
+st.plotly_chart(fig, use_container_width=True)
+
 # -------------------------------- Plot 2: Heat Map --------------------------------
 st.markdown("<div class='sectionheader'> HeatMap </div>", unsafe_allow_html=True)
 # Count the crime type and list out the top 10 crime type that have the most cases.