• About Us
  • Disclaimer
  • Contact Us
  • Privacy Policy
Saturday, September 5, 2026
mGrowTech
No Result
View All Result
  • Technology And Software
    • Account Based Marketing
    • Channel Marketing
    • Marketing Automation
      • Al, Analytics and Automation
      • Ad Management
  • Digital Marketing
    • Social Media Management
    • Google Marketing
  • Direct Marketing
    • Brand Management
    • Marketing Attribution and Consulting
  • Mobile Marketing
  • Event Management
  • PR Solutions
  • Technology And Software
    • Account Based Marketing
    • Channel Marketing
    • Marketing Automation
      • Al, Analytics and Automation
      • Ad Management
  • Digital Marketing
    • Social Media Management
    • Google Marketing
  • Direct Marketing
    • Brand Management
    • Marketing Attribution and Consulting
  • Mobile Marketing
  • Event Management
  • PR Solutions
No Result
View All Result
mGrowTech
No Result
View All Result
Home Al, Analytics and Automation

How to Build Interactive Geospatial Dashboards Using Folium with Heatmaps, Choropleths, Time Animation, Marker Clustering, and Advanced Interactive Plugins

Josh by Josh
March 1, 2026
in Al, Analytics and Automation
0
How to Build Interactive Geospatial Dashboards Using Folium with Heatmaps, Choropleths, Time Animation, Marker Clustering, and Advanced Interactive Plugins


def create_marker_cluster_map():
   """Create a map with marker clustering for large 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,
       'value': values
   })
  
   m = folium.Map(location=[37.8, -96], zoom_start=4)
  
   marker_cluster = MarkerCluster(
       name="Location Cluster",
       overlay=True,
       control=True
   ).add_to(m)
  
   for idx, row in df_markers.iterrows():
       if row['value'] < 33:
           color="green"
       elif row['value'] < 66:
           color="orange"
       else:
           color="red"
      
       folium.Marker(
           location=[row['lat'], row['lon']],
           popup=f"Value: {row['value']}",
           tooltip=f"Location {idx}",
           icon=folium.Icon(color=color, icon='info-sign')
       ).add_to(marker_cluster)
  
   folium.LayerControl().add_to(m)
  
   title_html=""'
                <div style="position: fixed;
                            top: 10px; left: 50px; width: 350px; height: 60px;
                            background-color: white; border:2px solid grey; z-index:9999;
                            font-size:14px; padding: 10px">
                <h4 style="margin: 0;">Marker Clustering Demo</h4>
                <p style="margin: 5px 0 0 0; font-size: 12px;">5000 markers - zoom to see individual points</p>
                </div>
                '''
   m.get_root().html.add_child(folium.Element(title_html))
  
   return m


def create_time_series_map():
   """Create an animated map showing data changes over time"""
   start_date = datetime(2024, 8, 1)
   features = []
  
   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)
      
       feature = {
           'type': 'Feature',
           'geometry': {
               'type': 'Point',
               'coordinates': [lon, lat]
           },
           'properties': {
               'time': timestamp.isoformat(),
               'popup': f'Hurricane Position<br>Time: {timestamp.strftime("%Y-%m-%d %H:%M")}<br>Category: {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
               }
           }
       }
       features.append(feature)
  
   m = folium.Map(
       location=[30.0, -75.0],
       zoom_start=5,
       tiles="CartoDB Positron"
   )
  
   TimestampedGeoJson(
       {'type': 'FeatureCollection', 'features': features},
       period='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=""'
                <div style="position: fixed;
                            top: 10px; left: 50px; width: 300px; height: 80px;
                            background-color: white; border:2px solid grey; z-index:9999;
                            font-size:14px; padding: 10px">
                <h4 style="margin: 0;">Hurricane Path Animation</h4>
                <p style="margin: 5px 0 0 0; font-size: 12px;">Simulated hurricane tracking<br>
                Use controls below to play/pause</p>
                </div>
                '''
   m.get_root().html.add_child(folium.Element(title_html))
  
   return m


def create_interactive_plugins_map():
   """Create a map with multiple 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",
       position='topleft',
       draw_options={
           'polyline': True,
           'polygon': True,
           'circle': True,
           'rectangle': True,
           'marker': True,
           'circlemarker': True
       },
       edit_options={'edit': True}
   )
   m.add_child(draw)
  
   Fullscreen(
       position='topright',
       title="Expand map",
       title_cancel="Exit fullscreen",
       force_separate_button=True
   ).add_to(m)
  
   plugins.MeasureControl(
       position='bottomleft',
       primary_length_unit="kilometers",
       secondary_length_unit="miles",
       primary_area_unit="sqkilometers",
       secondary_area_unit="acres"
   ).add_to(m)
  
   plugins.MousePosition(
       position='bottomright',
       separator=" | ",
       empty_string='NaN',
       lng_first=True,
       num_digits=20,
       prefix='Coordinates:',
   ).add_to(m)
  
   plugins.LocateControl(
       auto_start=False,
       position='topleft'
   ).add_to(m)
  
   folium.Marker(
       [40.7128, -74.0060],
       popup='<b>NYC</b><br>Try the drawing tools!',
       icon=folium.Icon(color="red", icon='info-sign')
   ).add_to(m)
  
   return m


def create_earthquake_map():
   """Create comprehensive earthquake visualization using real USGS data"""
   url="https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/2.5_month.geojson"
  
   try:
       response = requests.get(url)
       earthquake_data = response.json()
       print(f"Successfully loaded {len(earthquake_data['features'])} earthquakes")
   except Exception as e:
       print(f"Error fetching data: {e}")
       earthquake_data = {
           'features': [
               {
                   'properties': {'mag': 5.2, 'place': 'Sample Location 1', 'time': 1640000000000},
                   'geometry': {'coordinates': [-122.0, 37.0, 10]}
               },
               {
                   'properties': {'mag': 6.1, 'place': 'Sample Location 2', 'time': 1640100000000},
                   'geometry': {'coordinates': [140.0, 35.0, 20]}
               }
           ]
       }
  
   earthquakes = []
   for feature in earthquake_data['features']:
       props = feature['properties']
       coords = feature['geometry']['coordinates']
      
       earthquakes.append({
           'lat': coords[1],
           'lon': coords[0],
           'depth': coords[2],
           'magnitude': props.get('mag', 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"Total earthquakes: {len(df_eq)}")
   print(f"Magnitude range: {df_eq['magnitude'].min():.1f} - {df_eq['magnitude'].max():.1f}")
   print(f"Depth range: {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(name="Minor (< 4.0)")
   moderate = folium.FeatureGroup(name="Moderate (4.0-5.0)")
   strong = folium.FeatureGroup(name="Strong (5.0-6.0)")
   major = folium.FeatureGroup(name="Major (≥ 6.0)")
  
   for idx, eq in df_eq.iterrows():
       mag = eq['magnitude']
      
       if mag < 4.0:
           color="green"
           radius = 3
           group = minor
       elif mag < 5.0:
           color="yellow"
           radius = 6
           group = moderate
       elif mag < 6.0:
           color="orange"
           radius = 9
           group = strong
       else:
           color="red"
           radius = 12
           group = major
      
       popup_html = f"""
       <div style="font-family: Arial; width: 250px;">
           <h4 style="margin: 0; color: {color};">Magnitude {mag:.1f}</h4>
           <hr style="margin: 5px 0;">
           <p><b>Location:</b> {eq['place']}</p>
           <p><b>Depth:</b> {eq['depth']:.1f} km</p>
           <p><b>Time:</b> {eq['time'].strftime('%Y-%m-%d %H:%M:%S')}</p>
           <p><b>Coordinates:</b> {eq['lat']:.4f}, {eq['lon']:.4f}</p>
       </div>
       """
      
       folium.CircleMarker(
           location=[eq['lat'], eq['lon']],
           radius=radius,
           popup=folium.Popup(popup_html, max_width=270),
           tooltip=f"M{mag:.1f} - {eq['place']}",
           color=color,
           fill=True,
           fillColor=color,
           fillOpacity=0.7,
           weight=2
       ).add_to(group)
  
   minor.add_to(m)
   moderate.add_to(m)
   strong.add_to(m)
   major.add_to(m)
  
   heat_data = [[row['lat'], row['lon'], row['magnitude']] for idx, row in df_eq.iterrows()]
   heatmap = folium.FeatureGroup(name="Density Heatmap", show=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: 'red'}
   ).add_to(heatmap)
   heatmap.add_to(m)
  
   folium.LayerControl(position='topright', collapsed=False).add_to(m)
  
   legend_html=""'
   <div style="position: fixed;
               bottom: 50px; right: 50px; width: 200px; height: 180px;
               background-color: white; border:2px solid grey; z-index:9999;
               font-size:14px; padding: 10px; border-radius: 5px;">
       <h4 style="margin: 0 0 10px 0;">Earthquake Magnitude</h4>
       <p style="margin: 5px 0;"><span style="color: green;">●</span> Minor (< 4.0)</p>
       <p style="margin: 5px 0;"><span style="color: yellow;">●</span> Moderate (4.0-5.0)</p>
       <p style="margin: 5px 0;"><span style="color: orange;">●</span> Strong (5.0-6.0)</p>
       <p style="margin: 5px 0;"><span style="color: red;">●</span> Major (≥ 6.0)</p>
       <hr style="margin: 10px 0;">
       <p style="margin: 5px 0; font-size: 11px;">Data: USGS (Past 30 days)</p>
   </div>
   '''
   m.get_root().html.add_child(folium.Element(legend_html))
  
   title_html=""'
   <div style="position: fixed;
               top: 10px; left: 50px; width: 400px; height: 80px;
               background-color: rgba(255, 255, 255, 0.95); border:2px solid grey; z-index:9999;
               font-size:14px; padding: 10px; border-radius: 5px;">
       <h3 style="margin: 0;">🌍 Global Earthquake Monitor</h3>
       <p style="margin: 5px 0 0 0; font-size: 12px;">
           Real-time earthquake data (M ≥ 2.5)<br>
           Click markers for details | Toggle layers to explore
       </p>
   </div>
   '''
   m.get_root().html.add_child(folium.Element(title_html))
  
   Fullscreen(position='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 name, map_obj in maps.items():
       if map_obj is not None:
           filename = f"{name}.html"
           map_obj.save(filename)
           print(f"✓ Saved: {filename}")
       else:
           print(f"✗ Skipped: {name} (map generation 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. Access the map objects in code using the 'maps' dictionary")
   print("3. Display maps in Jupyter/Colab by returning the map object")
   print("\nExample: To display the earthquake map in a notebook, just run:")
   print("  maps['earthquake_map']")
   print("\n" + "=" * 80)



Source_link

READ ALSO

Adaption Labs Introduces ‘Invent a Dataset’: Training Data Generated From a Task Description, Not a Seed Corpus

Retrieval vs. Memory in Agentic AI System

Related Posts

Adaption Labs Introduces ‘Invent a Dataset’: Training Data Generated From a Task Description, Not a Seed Corpus
Al, Analytics and Automation

Adaption Labs Introduces ‘Invent a Dataset’: Training Data Generated From a Task Description, Not a Seed Corpus

September 5, 2026
Al, Analytics and Automation

Retrieval vs. Memory in Agentic AI System

September 5, 2026
OpenAI Commits $1B to Frontline Cyber Defense, Launches MS-ISAC Pilot – Unite.AI
Al, Analytics and Automation

OpenAI Commits $1B to Frontline Cyber Defense, Launches MS-ISAC Pilot – Unite.AI

September 5, 2026
MIT Quantum Initiative launches postdoctoral fellowship program | MIT News
Al, Analytics and Automation

MIT Quantum Initiative launches postdoctoral fellowship program | MIT News

September 4, 2026
Google DeepMind’s WeatherNext 3 Trains on Weather Station Observations to Deliver 5 km Global Forecasts, Refreshed Every Hour
Al, Analytics and Automation

Google DeepMind’s WeatherNext 3 Trains on Weather Station Observations to Deliver 5 km Global Forecasts, Refreshed Every Hour

September 4, 2026
Al, Analytics and Automation

Understanding the Role of Latent Space in Machine Learning Models

September 4, 2026
Next Post
Vibe coding with overeager AI: Lessons learned from treating Google AI Studio like a teammate

Vibe coding with overeager AI: Lessons learned from treating Google AI Studio like a teammate

POPULAR NEWS

Trump ends trade talks with Canada over a digital services tax

Trump ends trade talks with Canada over a digital services tax

June 28, 2025
15 Trending Songs on TikTok in 2025 (+ How to Use Them)

15 Trending Songs on TikTok in 2025 (+ How to Use Them)

June 18, 2025
Communication Effectiveness Skills For Business Leaders

Communication Effectiveness Skills For Business Leaders

June 10, 2025
Comparing the Top 7 Large Language Models LLMs/Systems for Coding in 2025

Comparing the Top 7 Large Language Models LLMs/Systems for Coding in 2025

November 4, 2025
App Development Cost in Singapore: Pricing Breakdown & Insights

App Development Cost in Singapore: Pricing Breakdown & Insights

June 22, 2025

EDITOR'S PICK

Build a Low-Footprint AI Coding Assistant with Mistral Devstral

Build a Low-Footprint AI Coding Assistant with Mistral Devstral

June 25, 2025
Android’s new ‘Call Reason’ flags important calls before you pick up

Android’s new ‘Call Reason’ flags important calls before you pick up

December 4, 2025

How a New York Life VP is preparing teams for the AI era

May 16, 2026

Indonesian Coffees Recognized in French Gourmet Competition

March 23, 2025

About

We bring you the best Premium WordPress Themes that perfect for news, magazine, personal blog, etc. Check our landing page for details.

Follow us

Categories

  • Account Based Marketing
  • Ad Management
  • Al, Analytics and Automation
  • Brand Management
  • Channel Marketing
  • Digital Marketing
  • Direct Marketing
  • Event Management
  • Google Marketing
  • Marketing Attribution and Consulting
  • Marketing Automation
  • Mobile Marketing
  • PR Solutions
  • Social Media Management
  • Technology And Software
  • Uncategorized

Recent Posts

  • The Wine AI Visibility Index 2026: Named vs. Cited
  • GeoGuessr Daily Challenge Answer Today for September 5, 2026
  • Adaption Labs Introduces ‘Invent a Dataset’: Training Data Generated From a Task Description, Not a Seed Corpus
  • 7 Best Virtual Desktop Infrastructure (VDI) Software (2026): My Picks
  • About Us
  • Disclaimer
  • Contact Us
  • Privacy Policy
No Result
View All Result
  • Technology And Software
    • Account Based Marketing
    • Channel Marketing
    • Marketing Automation
      • Al, Analytics and Automation
      • Ad Management
  • Digital Marketing
    • Social Media Management
    • Google Marketing
  • Direct Marketing
    • Brand Management
    • Marketing Attribution and Consulting
  • Mobile Marketing
  • Event Management
  • PR Solutions