Geo Visualizer

Geo Visualizer

Create interactive HTML maps from geographic data using Folium.

Features

• Markers: Plot points with custom icons, popups, and tooltips
• Heatmaps: Visualize density/​intensity data
• Choropleth: Color regions by data values
• Routes/​Lines: Draw paths between points
• Circles/​Areas: Show radius-based coverage
• Layer Control: Toggle multiple layers
• Clustering: Auto-cluster dense markers

Quick Start

from geo_visualizer import GeoVisualizer

# Simple marker map
viz = GeoVisualizer()
viz.add_markers([
    {"lat": 40.7128, "lon": -74.0060, "name": "New York"},
    {"lat": 34.0522, "lon": -118.2437, "name": "Los Angeles"}
])
viz.save("cities.html")

# From CSV
viz = GeoVisualizer()
viz.from_csv("locations.csv", lat_col="latitude", lon_col="longitude")
viz.save("map.html")

CLI Usage

# Plot markers from CSV
python geo_visualizer.py --input locations.csv --lat latitude --lon longitude --output map.html

# Add heatmap
python geo_visualizer.py --input data.csv --lat lat --lon lng --heatmap --output heat.html

# With clustering
python geo_visualizer.py --input stores.csv --lat lat --lon lon --cluster --output stores.html

# Choropleth map
python geo_visualizer.py --geojson states.geojson --data stats.csv --key state --value population --output choropleth.html

API Reference

GeoVisualizer Class

class GeoVisualizer:
    def __init__(self, center=None, zoom=10, tiles="OpenStreetMap")

    # Data loading
    def from_csv(self, filepath, lat_col, lon_col, **kwargs) -> 'GeoVisualizer'
    def from_dataframe(self, df, lat_col, lon_col, **kwargs) -> 'GeoVisualizer'
    def from_geojson(self, filepath) -> 'GeoVisualizer'

    # Markers
    def add_marker(self, lat, lon, popup=None, tooltip=None, icon=None, color="blue")
    def add_markers(self, locations: list, name_col=None, popup_cols=None)
    def cluster_markers(self, enabled=True) -> 'GeoVisualizer'

    # Layers
    def add_heatmap(self, points=None, weight_col=None, radius=15) -> 'GeoVisualizer'
    def add_choropleth(self, geojson, data, key_on, value_col, **kwargs) -> 'GeoVisualizer'
    def add_route(self, points, color="blue", weight=3) -> 'GeoVisualizer'
    def add_circle(self, lat, lon, radius_m, color="blue", fill=True)

    # Output
    def save(self, filepath) -> str
    def get_html(self) -> str
    def fit_bounds(self) -> 'GeoVisualizer'

Marker Options

# Custom icons
viz.add_marker(lat, lon, icon="fa-coffee", color="red")

# With popup content
viz.add_marker(lat, lon, popup="<b>Store #123</​b><br>Open 9-5")

# From CSV with popup columns
viz.from_csv("stores.csv", lat_col="lat", lon_col="lon")
viz.add_markers(viz.data, popup_cols=["name", "address", "phone"])

Heatmap Options

# Basic heatmap
viz.add_heatmap()

# Weighted heatmap (e.g., by sales volume)
viz.add_heatmap(weight_col="sales", radius=20, blur=15, max_zoom=12)

Choropleth Maps

# Color regions by data
viz.add_choropleth(
    geojson="us-states.geojson",
    data=state_data,
    key_on="feature.properties.name",  # GeoJSON property
    value_col="population",
    fill_color="YlOrRd",  # Color scale
    legend_name="Population"
)

Tile Layers

Available base maps:

• OpenStreetMap (default)
• CartoDB positron (light, minimal)
• CartoDB dark_matter (dark theme)
• Stamen Terrain (terrain features)
• Stamen Toner (high contrast B&W)

viz = GeoVisualizer(tiles="CartoDB positron")

Example Workflows

Store Locator Map

viz = GeoVisualizer()
viz.from_csv("stores.csv", lat_col="lat", lon_col="lon")
viz.add_markers(viz.data, popup_cols=["name", "address", "hours"])
viz.cluster_markers(True)
viz.fit_bounds()
viz.save("store_locator.html")

Sales Heatmap

viz = GeoVisualizer(tiles="CartoDB dark_matter")
viz.from_csv("sales.csv", lat_col="lat", lon_col="lon")
viz.add_heatmap(weight_col="revenue", radius=25)
viz.save("sales_heat.html")

Delivery Route

viz = GeoVisualizer()
stops = [(40.7, -74.0), (40.8, -73.9), (40.75, -73.95)]
viz.add_route(stops, color="blue", weight=4)
for i, (lat, lon) in enumerate(stops):
    viz.add_marker(lat, lon, popup=f"Stop {i+1}")
viz.save("route.html")

Output

• HTML: Interactive map viewable in any browser
• Auto-fit: Automatically zooms to show all data
• Responsive: Works on mobile devices

Dependencies

• folium>=0.14.0
• pandas>=2.0.0
• branca>=0.6.0