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    AI

    The best way to Construct Interactive Geospatial Dashboards Utilizing Folium with Heatmaps, Choropleths, Time Animation, Marker Clustering, and Superior Interactive Plugins

    Naveed AhmadBy Naveed Ahmad28/02/2026Updated:28/02/2026No Comments4 Mins Read
    blog banner23 69


    def create_marker_cluster_map():
       """Create a map with marker clustering for giant datasets"""
       np.random.seed(123)
       n_locations = 5000
      
       lats = np.random.uniform(25, 49, n_locations)
       lons = np.random.uniform(-125, -65, n_locations)
       values = np.random.randint(1, 100, n_locations)
      
       df_markers = pd.DataFrame({
           'lat': lats,
           'lon': lons,
           'worth': values
       })
      
       m = folium.Map(location=[37.8, -96], zoom_start=4)
      
       marker_cluster = MarkerCluster(
           title="Location Cluster",
           overlay=True,
           management=True
       ).add_to(m)
      
       for idx, row in df_markers.iterrows():
           if row['value'] < 33:
               coloration="inexperienced"
           elif row['value'] < 66:
               coloration="orange"
           else:
               coloration="purple"
          
           folium.Marker(
               location=[row['lat'], row['lon']],
               popup=f"Worth: {row['value']}",
               tooltip=f"Location {idx}",
               icon=folium.Icon(coloration=coloration, icon='info-sign')
           ).add_to(marker_cluster)
      
       folium.LayerControl().add_to(m)
      
       title_html=""'
                    

    Marker Clustering Demo

    5000 markers - zoom to see particular person factors

    ''' m.get_root().html.add_child(folium.Aspect(title_html)) return m def create_time_series_map(): """Create an animated map exhibiting information modifications over time""" start_date = datetime(2024, 8, 1) options = [] path = [ [25.0, -70.0], [26.5, -72.0], [28.0, -74.5], [29.5, -76.5], [31.0, -78.0], [32.5, -79.5], [34.0, -80.5], [35.5, -81.0] ] for i, (lat, lon) in enumerate(path): timestamp = start_date + timedelta(hours=i*6) characteristic = { 'sort': 'Function', 'geometry': { 'sort': 'Level', 'coordinates': [lon, lat] }, 'properties': { 'time': timestamp.isoformat(), 'popup': f'Hurricane Place
    Time: {timestamp.strftime("%Y-%m-%d %H:%M")}
    Class: {min(5, i//2 + 1)}', 'icon': 'circle', 'iconstyle': { 'fillColor': ['yellow', 'orange', 'red', 'darkred', 'purple'][min(4, i//2)], 'fillOpacity': 0.8, 'stroke': 'true', 'radius': 8 + i * 2 } } } options.append(characteristic) m = folium.Map( location=[30.0, -75.0], zoom_start=5, tiles="CartoDB Positron" ) TimestampedGeoJson( {'sort': 'FeatureCollection', 'options': options}, interval='PT6H', add_last_point=True, auto_play=True, loop=True, max_speed=2, loop_button=True, date_options="YYYY-MM-DD HH:mm", time_slider_drag_update=True ).add_to(m) title_html=""'

    Hurricane Path Animation

    Simulated hurricane monitoring
    Use controls under to play/pause

    ''' m.get_root().html.add_child(folium.Aspect(title_html)) return m def create_interactive_plugins_map(): """Create a map with a number of interactive plugins""" m = folium.Map( location=[40.7128, -74.0060], zoom_start=12, tiles="OpenStreetMap" ) minimap = MiniMap(toggle_display=True) m.add_child(minimap) draw = Draw( export=True, filename="drawn_shapes.geojson", place='topleft', draw_options={ 'polyline': True, 'polygon': True, 'circle': True, 'rectangle': True, 'marker': True, 'circlemarker': True }, edit_options={'edit': True} ) m.add_child(draw) Fullscreen( place='topright', title="Broaden map", title_cancel="Exit fullscreen", force_separate_button=True ).add_to(m) plugins.MeasureControl( place='bottomleft', primary_length_unit="kilometers", secondary_length_unit="miles", primary_area_unit="sqkilometers", secondary_area_unit="acres" ).add_to(m) plugins.MousePosition( place='bottomright', separator=" | ", empty_string='NaN', lng_first=True, num_digits=20, prefix='Coordinates:', ).add_to(m) plugins.LocateControl( auto_start=False, place='topleft' ).add_to(m) folium.Marker( [40.7128, -74.0060], popup='NYC
    Strive the drawing instruments!', icon=folium.Icon(coloration="purple", icon='info-sign') ).add_to(m) return m def create_earthquake_map(): """Create complete earthquake visualization utilizing actual USGS information""" url="https://earthquake.usgs.gov/earthquakes/feed/v1.0/abstract/2.5_month.geojson" attempt: response = requests.get(url) earthquake_data = response.json() print(f"Efficiently loaded {len(earthquake_data['features'])} earthquakes") besides Exception as e: print(f"Error fetching information: {e}") earthquake_data = { 'options': [ { 'properties': {'mag': 5.2, 'place': 'Sample Location 1', 'time': 1640000000000}, 'geometry': {'coordinates': [-122.0, 37.0, 10]} }, { 'properties': {'magazine': 6.1, 'place': 'Pattern Location 2', 'time': 1640100000000}, 'geometry': {'coordinates': [140.0, 35.0, 20]} } ] } earthquakes = [] for characteristic in earthquake_data['features']: props = characteristic['properties'] coords = characteristic['geometry']['coordinates'] earthquakes.append({ 'lat': coords[1], 'lon': coords[0], 'depth': coords[2], 'magnitude': props.get('magazine', 0), 'place': props.get('place', 'Unknown'), 'time': datetime.fromtimestamp(props.get('time', 0) / 1000) }) df_eq = pd.DataFrame(earthquakes) print(f"nEarthquake Statistics:") print(f"Complete earthquakes: {len(df_eq)}") print(f"Magnitude vary: {df_eq['magnitude'].min():.1f} - {df_eq['magnitude'].max():.1f}") print(f"Depth vary: {df_eq['depth'].min():.1f} - {df_eq['depth'].max():.1f} km") m = folium.Map( location=[20, 0], zoom_start=2, tiles="CartoDB dark_matter" ) minor = folium.FeatureGroup(title="Minor (< 4.0)") reasonable = folium.FeatureGroup(title="Average (4.0-5.0)") robust = folium.FeatureGroup(title="Robust (5.0-6.0)") main = folium.FeatureGroup(title="Main (≥ 6.0)") for idx, eq in df_eq.iterrows(): magazine = eq['magnitude'] if magazine < 4.0: coloration="inexperienced" radius = 3 group = minor elif magazine < 5.0: coloration="yellow" radius = 6 group = reasonable elif magazine < 6.0: coloration="orange" radius = 9 group = robust else: coloration="purple" radius = 12 group = main popup_html = f"""

    Magnitude {magazine:.1f}


    Location: {eq['place']}

    Depth: {eq['depth']:.1f} km

    Time: {eq['time'].strftime('%Y-%m-%d %H:%M:%S')}

    Coordinates: {eq['lat']:.4f}, {eq['lon']:.4f}

    """ folium.CircleMarker( location=[eq['lat'], eq['lon']], radius=radius, popup=folium.Popup(popup_html, max_width=270), tooltip=f"M{magazine:.1f} - {eq['place']}", coloration=coloration, fill=True, fillColor=coloration, fillOpacity=0.7, weight=2 ).add_to(group) minor.add_to(m) reasonable.add_to(m) robust.add_to(m) main.add_to(m) heat_data = [[row['lat'], row['lon'], row['magnitude']] for idx, row in df_eq.iterrows()] heatmap = folium.FeatureGroup(title="Density Heatmap", present=False) HeatMap( heat_data, min_opacity=0.3, radius=15, blur=20, gradient={0.4: 'blue', 0.6: 'cyan', 0.7: 'lime', 0.8: 'yellow', 1: 'purple'} ).add_to(heatmap) heatmap.add_to(m) folium.LayerControl(place='topright', collapsed=False).add_to(m) legend_html=""'

    Earthquake Magnitude

    ● Minor (< 4.0)

    ● Average (4.0-5.0)

    ● Robust (5.0-6.0)

    ● Main (≥ 6.0)


    Information: USGS (Previous 30 days)

    ''' m.get_root().html.add_child(folium.Aspect(legend_html)) title_html=""'

    🌍 International Earthquake Monitor

    Actual-time earthquake information (M ≥ 2.5)
    Click on markers for particulars | Toggle layers to discover

    ''' m.get_root().html.add_child(folium.Aspect(title_html)) Fullscreen(place='topright').add_to(m) return m if __name__ == "__main__": print("=" * 80) print("ADVANCED FOLIUM TUTORIAL - ALL EXAMPLES") print("=" * 80) print("nGenerating all maps...n") maps = { 'multi_tile_map': create_multi_tile_map(), 'advanced_markers_map': create_advanced_markers_map(), 'heatmap': create_heatmap(), 'choropleth_map': create_choropleth_map(), 'marker_cluster_map': create_marker_cluster_map(), 'time_series_map': create_time_series_map(), 'interactive_plugins_map': create_interactive_plugins_map(), 'earthquake_map': create_earthquake_map() } print("n" + "=" * 80) print("SAVING MAPS TO HTML FILES") print("=" * 80) for title, map_obj in maps.gadgets(): if map_obj is just not None: filename = f"{title}.html" map_obj.save(filename) print(f"✓ Saved: {filename}") else: print(f"✗ Skipped: {title} (map technology failed)") print("n" + "=" * 80) print("ALL MAPS GENERATED SUCCESSFULLY!") print("=" * 80) print("nYou can now:") print("1. Open any HTML file in your browser to view the interactive map") print("2. Entry the map objects in code utilizing the 'maps' dictionary") print("3. Show maps in Jupyter/Colab by returning the map object") print("nExample: To show the earthquake map in a pocket book, simply run:") print(" maps['earthquake_map']") print("n" + "=" * 80)



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    Naveed Ahmad

    Naveed Ahmad is a technology journalist and AI writer at ArticlesStock, covering artificial intelligence, machine learning, and emerging tech policy. Read his latest articles.

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