Update src/streamlit_app.py

src/streamlit_app.py

@@ -263,27 +263,47 @@ years = sorted(df['year'].unique())
 year_dropdown = st.selectbox("Year: ", years)
 crime_dropdown = st.selectbox("Crime Type: ", top_10_crimes)
 
-
-# Create the map.
-def crime_map(year, crime):
-    df_filtered = df[(df['year'] == year) & (df['crm_cd_desc'] == crime)].sample(n=300, random_state=1)
-    gdf_points = gpd.GeoDataFrame(
-        df_filtered,
-        geometry=gpd.points_from_xy(df_filtered['lon'], df_filtered['lat']),
-        crs="EPSG:4326"
-    )
+# Create the new folium map to make the map more interactive.
+new_map = folium.Map(location=[df_filtered['lat'].mean(), df_filtered['lon'].mean()], zoom_start=10)
+
+# Add county boundary
+folium.GeoJson(geojson_data, name="County Boundaries").add_to(new_map)
+
+# # Create the map.
+# def crime_map(year, crime):
+#     df_filtered = df[(df['year'] == year) & (df['crm_cd_desc'] == crime)].sample(n=300, random_state=1)
+#     gdf_points = gpd.GeoDataFrame(
+#         df_filtered,
+#         geometry=gpd.points_from_xy(df_filtered['lon'], df_filtered['lat']),
+#         crs="EPSG:4326"
+#     )
     
-    fig, ax = plt.subplots(figsize=(10, 10))
-    gdf_counties.plot(ax=ax, color='lightgray', edgecolor='white')
-    gdf_points.plot(ax=ax, color='red', markersize=10, alpha=0.6)
-    ax.set_title(f"{crime} - {year}")
-    ax.set_xlabel("Longitude")
-    ax.set_ylabel("Latitude")
-    plt.grid(True)
-    st.pyplot(fig)
-
-# Call the function with selected values
-crime_map(year_dropdown, crime_dropdown)
+#     fig, ax = plt.subplots(figsize=(10, 10))
+#     gdf_counties.plot(ax=ax, color='lightgray', edgecolor='white')
+#     gdf_points.plot(ax=ax, color='red', markersize=10, alpha=0.6)
+#     ax.set_title(f"{crime} - {year}")
+#     ax.set_xlabel("Longitude")
+#     ax.set_ylabel("Latitude")
+#     plt.grid(True)
+#     st.pyplot(fig)
+
+# # Call the function with selected values
+# crime_map(year_dropdown, crime_dropdown)
+
+# Add crime points
+# Method comes from: https://folium.streamlit.app/.
+for _, row in df_filtered.iterrows():
+    folium.CircleMarker(
+        location=[row['lat'], row['lon']],
+        radius=3,
+        color='red',
+        fill=True,
+        fill_opacity=0.6,
+        popup=row['crm_cd_desc']
+    ).add_to(new_map)
+
+# Display the new map.
+st_folium(new_map, width=700, height=500)
 
 st.markdown(""" 
 This visualization shows the distribution of different years versus crime types in geospatial space by overlaying crime event points on a map of county boundaries. We chose a static map combined with GeoPandas and Matplotlib in order to clearly present county boundaries relative to crime events in space, suitable for reporting or analyzing output. The background of the map is coded with the shape of the county boundaries in gray, and the foreground is coded with red dots to indicate specific crime events, with the latitude and longitude position of each point coding its geographic coordinates.