RSK World - Geographic Heatmap Visualization Documentation | Plotly | Geographic Data Visualization | Heatmap Visualization | Python | Interactive Heatmaps | 3D Heatmaps | Animated Heatmaps | Clustering Analysis | Hotspot Detection | RSK World

Geographic Heatmap Visualization - Project Description | Plotly | Geographic Data Visualization | Interactive Heatmaps

This project creates comprehensive Geographic Heatmap Visualizations using Plotly for geographic data visualization and spatial analysis. The project includes interactive geographic heatmaps with zoom and pan functionality, density heatmaps showing data intensity across geographic regions, scatter heatmaps with individual data points, regional heatmaps for comparing geographic distributions, 3D geographic heatmaps with intensity as height, animated time-series heatmaps showing data changes over time, clustering analysis (KMeans, DBSCAN) to identify geographic clusters, hotspot detection for high-intensity areas, contour maps showing intensity levels, and multiple map projections (Mercator, Natural Earth, Orthographic, Equirectangular) for comprehensive geographic analysis. Perfect for location-based data visualization, spatial patterns, geographic analysis, and interactive heatmap applications.

The Geographic Heatmap Visualization project features 2 comprehensive Jupyter notebooks covering basic heatmaps (density, scatter, regional, projections) and advanced features (3D heatmaps, animated heatmaps, clustering, hotspot detection, contour maps). The project includes 5 Python scripts (visualizer.py for basic visualizations, advanced_features.py for advanced features, data_processor.py for data processing, data_generator.py for generating sample datasets, and example.py for complete examples). Built with Python 3.8+, Plotly 5.0+, GeoPandas 0.10+, Pandas 1.3+, NumPy 1.21+, Jupyter Notebook, and optional scikit-learn for clustering and scipy for contour maps. The project includes comprehensive data generators for city population data (200 cities), time-series data for 2025 (18,250 records), event data for 2025 (500 events), and traffic/congestion data (1,000 points).

Geographic Heatmap Visualization Screenshots | Plotly Geographic Heatmaps | Interactive Heatmap Examples

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Geographic Heatmap Visualization Core Features | Plotly Geographic Features | Interactive Heatmap Features

Density Heatmaps

Color-coded intensity visualization
Interactive zoom and pan
Customizable color scales
Adjustable density radius
Hover tooltips with data

Scatter Heatmaps

Individual data points
Size and color based on intensity
Interactive hover tooltips
Customizable point sizes
Multiple color scales

Regional Heatmaps

Regional grouping
Color-coded regions
Regional comparison
Interactive tooltips
Geographic distribution

3D Geographic Heatmaps

3D visualization
Intensity as height
Interactive rotation
Customizable camera angles
Multi-dimensional data

Animated Heatmaps

Time-based animations
Temporal analysis
Trend visualization
Interactive playback controls
Time-series data support

2 Jupyter Notebooks

Basic heatmap tutorial
Advanced features demo
Step-by-step examples
Code explanations
Interactive learning

Advanced Geographic Heatmap Features | Clustering Analysis | Hotspot Detection | Map Projections

Clustering Analysis

KMeans clustering
DBSCAN clustering
Automatic grouping
Cluster visualization
Geographic clusters

Hotspot Detection

Automatic hotspot detection
Customizable thresholds
High-intensity highlighting
Percentile-based detection

Map Projections

Mercator projection
Natural Earth projection
Orthographic projection
Equirectangular projection

Contour Maps

Contour line visualization
Intensity level mapping
Topographic-style maps
Gradient visualization
Intensity gradients

Visualization Types | Geographic Heatmap Types | Interactive Heatmap Features

Visualization Type	Description	Use Case
Density Heatmap	Color-coded density visualization showing data intensity across geographic regions	Visualize population density, event frequency, or data concentration
Scatter Heatmap	Individual data points with size and color based on intensity values	Display specific locations with intensity information
Regional Heatmap	Group data by geographic regions with different colors	Compare regional distributions and patterns
3D Heatmap	Three-dimensional visualization with intensity as the Z-axis	See geographic patterns in 3D space with interactive rotation
Animated Heatmap	Time-based animations showing how geographic data changes over time	Temporal analysis and visualizing trends across different periods
Clustered Heatmap	Machine learning-based clustering (KMeans, DBSCAN) to identify geographic clusters	Automatically group similar data points together
Hotspot Heatmap	Automatic detection and highlighting of high-intensity areas	Identify areas of high activity or concentration
Contour Heatmap	Contour lines showing intensity levels, similar to topographic maps	Visualize intensity gradients and patterns
Projections Comparison	Compare data across four different map projections side by side	Analyze data from different geographic perspectives

Technologies Used | Python Technologies | Data Science Stack | Analytics Tools

This Geographic Heatmap Visualization project is built using modern Python geographic data visualization and spatial analysis technologies. The core implementation uses Python 3.8+ as the primary programming language and Plotly 5.0+ for creating interactive geographic heatmaps. The project includes GeoPandas 0.10+ for geographic data processing, Pandas 1.3+ for data manipulation, NumPy 1.21+ for numerical computing, Jupyter Notebook for interactive development, and optional scikit-learn 1.0+ for clustering analysis and scipy 1.7+ for contour maps and interpolation. The visualization library features interactive geographic heatmaps with zoom and pan, multiple visualization types (density, scatter, regional, 3D, animated), clustering analysis (KMeans, DBSCAN), hotspot detection, contour maps, and multiple map projections for geographic data analysis, spatial patterns, and location-based visualization.

The project uses Plotly as the core visualization library for creating interactive geographic heatmaps with Python. It supports geographic data visualization through density heatmaps, scatter heatmaps, regional heatmaps, 3D geographic heatmaps, animated time-series heatmaps, clustering analysis (KMeans, DBSCAN), hotspot detection, contour maps, and multiple map projections (Mercator, Natural Earth, Orthographic, Equirectangular). The project includes comprehensive data generators for city population data (200 cities), time-series data for 2025 (18,250 records), event data for 2025 (500 events), and traffic/congestion data (1,000 points). The system includes interactive features such as zoom and pan, hover tooltips, customizable color scales, adjustable density radius, and image export capabilities (PNG, PDF, SVG) using kaleido for geographic data analysis, spatial patterns, location-based visualization, and interactive heatmap applications.

Python 3.8+ Plotly 5.0+ GeoPandas 0.10+ Pandas 1.3+ NumPy 1.21+ Jupyter Notebook scikit-learn 1.0+ scipy 1.7+ 3D Heatmaps Animated Heatmaps

Installation & Usage Guide | How to Install Geographic Heatmap | Setup Tutorial

Installation

Install all required dependencies for the Geographic Heatmap Visualization project:

# Install all requirements
pip install -r requirements.txt

# Required packages:
# - pandas>=1.3.0
# - numpy>=1.21.0
# - plotly>=5.0.0
# - geopandas>=0.10.0
# - shapely>=1.8.0
# - scipy>=1.7.0
# - jupyter>=1.0.0
# - notebook>=6.4.0

# Optional packages for advanced features:
# - scikit-learn>=1.0.0  # For clustering analysis
# - kaleido>=0.2.1  # For image export (PNG, PDF, SVG)

# Verify installation
python check_project.py

Running Jupyter Notebooks

Start Jupyter Notebook to explore the geographic heatmap examples:

# Start Jupyter Notebook
jupyter notebook

# Or use JupyterLab
jupyter lab

# Open the notebooks:
# - geographic_heatmap.ipynb - Main tutorial with basic heatmaps
#   * Density heatmaps
#   * Scatter heatmaps
#   * Regional heatmaps
#   * Multiple map projections
#
# - advanced_features_demo.ipynb - Advanced features demonstration
#   * 3D geographic heatmaps
#   * Animated time-series heatmaps
#   * Clustering analysis (KMeans, DBSCAN)
#   * Hotspot detection
#   * Contour maps

Running Example Scripts

Run Python scripts to generate geographic heatmaps:

# Run complete example (generates all basic visualizations):
python example.py

# Run visualizer directly:
python visualizer.py

# Generate sample datasets:
python data_generator.py

# This will generate:
# - data_cities_population.csv (200 cities)
# - data_timeseries_2025.csv (18,250 records)
# - data_events_2025.csv (500 events)
# - data_traffic.csv (1,000 points)

# Verify project setup:
python check_project.py

Project Features

Explore the comprehensive geographic heatmap visualization features:

# Project Features:
# 1. Density Heatmaps - Color-coded intensity visualization with interactive zoom
# 2. Scatter Heatmaps - Individual data points with size and color based on intensity
# 3. Regional Heatmaps - Group data by geographic regions with different colors
# 4. Multiple Map Projections - Mercator, Natural Earth, Orthographic, Equirectangular
# 5. 3D Geographic Heatmaps - Three-dimensional visualization with intensity as height
# 6. Animated Time-Series Heatmaps - Time-based animations showing data changes
# 7. Clustering Analysis - KMeans and DBSCAN clustering for geographic data
# 8. Hotspot Detection - Automatic detection and highlighting of high-intensity areas
# 9. Contour Maps - Contour lines showing intensity levels
# 10. Statistical Analysis - Data aggregation and time period analysis
# 11. 2 Jupyter Notebooks - Comprehensive tutorials with examples
# 12. 5 Python Scripts - Reusable visualization modules

# All features are demonstrated in the Jupyter notebooks

Basic Usage Example

Create geographic heatmaps with Python:

# Basic Usage Example:
from visualizer import (
    create_density_heatmap,
    create_scatter_heatmap,
    create_regional_heatmap,
    create_projection_comparison
)
from data_processor import generate_sample_data
import pandas as pd

# Generate or load data
df = generate_sample_data(n_points=1000)
# Or load from CSV:
# df = pd.read_csv('your_data.csv')

# Create density heatmap
create_density_heatmap(df, output_file='heatmap_density.html')

# Create scatter heatmap
create_scatter_heatmap(df, output_file='heatmap_scatter.html')

# Create regional heatmap
create_regional_heatmap(df, output_file='heatmap_regional.html')

# Create projections comparison
create_projection_comparison(df, output_file='heatmap_projections.html')

Project Structure | Geographic Heatmap File Structure | Source Code Organization

                geographic-heatmap/

                ├── README.md                          # Main documentation

                ├── requirements.txt                   # Python dependencies

                ├── LICENSE                            # MIT License

                ├── RELEASE_NOTES_v1.0.0.md           # Release notes

                ├── CHANGELOG.md                       # Version history

                ├── CONTRIBUTING.md                    # Contribution guidelines

                ├── PROJECT_STATUS.md                  # Project status

                ├── check_project.py                   # Project verification

                ├── setup.py                           # Package setup

                │

                ├── geographic_heatmap.ipynb           # Main tutorial notebook

                │   # Basic heatmaps: density, scatter, regional, projections

                │

                ├── advanced_features_demo.ipynb       # Advanced features notebook

                │   # 3D, animated, clustering, hotspot, contour maps

                │

                ├── visualizer.py                      # Basic visualization functions

                │   # create_density_heatmap()

                │   # create_scatter_heatmap()

                │   # create_regional_heatmap()

                │   # create_projection_comparison()

                │

                ├── advanced_features.py               # Advanced visualization features

                │   # create_3d_heatmap()

                │   # create_animated_heatmap()

                │   # create_clustered_heatmap()

                │   # create_hotspot_heatmap()

                │   # create_contour_heatmap()

                │

                ├── data_processor.py                  # Data processing utilities

                │   # generate_sample_data()

                │   # GeographicDataProcessor class

                │

                ├── data_generator.py                  # Advanced data generators

                │   # generate_city_population_data()

                │   # generate_time_series_data()

                │   # generate_event_data()

                │   # generate_traffic_data()

                │

                ├── example.py                         # Complete example script

                │

                ├── verify_2025_data.py                # 2025 data verification

                │

                ├── data/                              # Data directory

                │   └── README.md                      # Data documentation

                │

                ├── images/                            # Images directory

                ├── outputs/                           # Generated HTML files

                │

                ├── data_cities_population.csv         # City data (200 cities)

                ├── data_timeseries_2025.csv           # Time-series data (18,250 records)

                ├── data_events_2025.csv               # Event data (500 events)

                ├── data_traffic.csv                   # Traffic data (1,000 points)

                │

                ├── index.html                         # Demo HTML page

                └── .gitignore                         # Git ignore file

Configuration Options | Plotly Configuration | Geographic Heatmap Customization Guide

Plotly Configuration

Customize geographic heatmap visualization settings in Python code:

# Plotly Configuration for Geographic Heatmaps

import plotly.graph_objects as go
import plotly.express as px

# Default configuration in visualizer.py functions:
# - colorscale: 'Viridis' (can be 'Plasma', 'Inferno', 'Cividis', etc.)
# - radius: 20 (density radius, adjust for smoothness)
# - mapbox_style: 'open-street-map' (can be 'carto-positron', 'carto-darkmatter', etc.)
# - height: 700 (figure height in pixels)
# - zoom: 1.5 (map zoom level, 1-20)

# Customize in function calls:
from visualizer import create_density_heatmap

create_density_heatmap(
    df,
    colorscale='Plasma',      # Change color scale
    radius=30,                # Adjust density radius
    mapbox_style='carto-positron',  # Change map style
    title='Custom Heatmap'   # Custom title
)

# Modify default settings in visualizer.py:
# Edit function defaults to change global behavior

Configuration Tips:

COLOR_SCALE: Choose from 'Viridis', 'Plasma', 'Inferno', 'Cividis', 'Hot', 'Cool', 'Blues', 'Reds', etc. Default: 'Viridis'
RADIUS: Adjust density radius (10-50). Smaller = more detail, Larger = smoother. Default: 20
MAP_STYLE: Choose map base layer: 'open-street-map', 'carto-positron', 'carto-darkmatter', 'stamen-terrain', etc.
ZOOM: Set map zoom level (1-20). Lower = wider view, Higher = closer view. Default: 1.5
HEIGHT: Set figure height in pixels. Default: 700. Adjust for different screen sizes
EXPORT_FORMAT: HTML (interactive), PNG, PDF, or SVG (requires kaleido package)

Geographic Data Format Requirements

Works with various geographic data formats. Required structure:

# Supported data formats:
# - Pandas DataFrame (recommended)
# - CSV files (via pd.read_csv())
# - NumPy arrays (with proper column mapping)

# Required DataFrame structure:
import pandas as pd
import numpy as np

# Minimum required columns:
data = {
    'latitude': np.random.uniform(-90, 90, 1000),    # Required: -90 to 90
    'longitude': np.random.uniform(-180, 180, 1000),  # Required: -180 to 180
    'intensity': np.random.uniform(0, 100, 1000)      # Required: numeric values
}
df = pd.DataFrame(data)

# Optional columns:
df['region'] = np.random.choice(['North', 'South', 'East', 'West'], 1000)
df['date'] = pd.date_range('2025-01-01', periods=1000, freq='D')
df['city'] = [f'City_{i}' for i in range(1000)]

# Works with:
# - Geographic coordinates (latitude, longitude)
# - Intensity/density values (numeric)
# - Optional: region, date, city, name columns

Customizing Heatmap Appearance

Modify heatmap configurations in visualizer.py or advanced_features.py:

# Heatmap customization in Python:

from visualizer import create_density_heatmap
import plotly.graph_objects as go

# Create heatmap
fig = create_density_heatmap(df)

# Customize layout:
fig.update_layout(
    title={
        'text': 'Custom Geographic Heatmap',
        'x': 0.5,
        'font': {'size': 24, 'color': '#1e293b'}
    },
    height=800,  # Adjust height
    margin=dict(l=0, r=0, t=50, b=0)
)

# Customize map:
fig.update_layout(
    mapbox=dict(
        zoom=2,  # Adjust zoom (1-20)
        center=dict(lat=40.0, lon=-100.0)  # Set center point
    )
)

# Customize colorbar:
fig.update_traces(
    colorbar=dict(
        title="Intensity",
        titleside="right",
        titlefont=dict(size=16),
        len=0.5,
        y=0.5
    )
)

# Save customized heatmap
fig.write_html('custom_heatmap.html')

Adding Custom Heatmap Visualizations

Add new geographic heatmap visualizations using Plotly:

# Add new heatmap function to visualizer.py or advanced_features.py:

import plotly.graph_objects as go
import pandas as pd

def create_custom_heatmap(df, lat_col='latitude', lon_col='longitude', 
                          intensity_col='intensity', **kwargs):
    """Create a custom geographic heatmap visualization."""
    
    fig = go.Figure()
    
    # Add density heatmap trace
    fig.add_trace(go.Densitymapbox(
        lat=df[lat_col],
        lon=df[lon_col],
        z=df[intensity_col],
        radius=kwargs.get('radius', 20),
        colorscale=kwargs.get('colorscale', 'Viridis'),
        showscale=True,
        colorbar=dict(title="Intensity")
    ))
    
    # Calculate center point
    center_lat = df[lat_col].mean()
    center_lon = df[lon_col].mean()
    
    # Update layout
    fig.update_layout(
        title=kwargs.get('title', 'Custom Geographic Heatmap'),
        mapbox=dict(
            style=kwargs.get('mapbox_style', 'open-street-map'),
            center=dict(lat=center_lat, lon=center_lon),
            zoom=kwargs.get('zoom', 1.5)
        ),
        height=kwargs.get('height', 700)
    )
    
    # Save if output file provided
    if kwargs.get('output_file'):
        fig.write_html(kwargs['output_file'])
        print(f"Heatmap saved as '{kwargs['output_file']}'")
    
    return fig

# Use in your code:
from visualizer import create_custom_heatmap

fig = create_custom_heatmap(df, 
                           title='My Custom Heatmap',
                           output_file='custom_heatmap.html')

Project Architecture | Geographic Heatmap Architecture | System Architecture | Technical Architecture

Geographic Heatmap Architecture

1. Plotly Visualization Framework:

Built on Plotly for interactive visualizations
Uses Plotly Graph Objects for advanced customization
Plotly Express for quick heatmap creation
Interactive HTML output with zoom, pan, and hover
Mapbox integration for geographic maps

2. Data Processing Pipeline:

Pandas DataFrame for geographic data manipulation
CSV file loading and parsing
Coordinate validation (latitude/longitude)
Data aggregation and grouping by regions
Time-series data processing for animations

3. Visualization Components:

Densitymapbox for density heatmaps
Scattermapbox for scatter heatmaps
Scattergeo for map projections
3D scatter plots for 3D heatmaps
Animation frames for time-series heatmaps

Module Structure

The project is organized into focused modules:

# Module Structure:
# visualizer.py - Basic visualization functions
from visualizer import (
    create_density_heatmap,      # Density heatmap visualization
    create_scatter_heatmap,       # Scatter plot heatmap
    create_regional_heatmap,      # Regional distribution heatmap
    create_projection_comparison   # Multiple map projections
)

# advanced_features.py - Advanced visualization features
from advanced_features import (
    create_3d_heatmap,            # 3D geographic heatmap
    create_animated_heatmap,       # Animated time-series heatmap
    create_clustered_heatmap,     # Clustering analysis heatmap
    create_hotspot_heatmap,       # Hotspot detection heatmap
    create_contour_heatmap        # Contour map visualization
)

# data_processor.py - Data processing utilities
from data_processor import (
    generate_sample_data,         # Generate sample geographic data
    GeographicDataProcessor       # Data processing class
)

# data_generator.py - Advanced data generators
from data_generator import (
    generate_city_population_data,  # City population data
    generate_time_series_data,      # Time-series data for 2025
    generate_event_data,            # Event data for 2025
    generate_traffic_data           # Traffic/congestion data
)

Data Format and Processing

How geographic data is processed for visualization:

# Data Format Requirements:
# CSV file with columns: latitude, longitude, intensity, region (optional)

# Example data structure:
# latitude,longitude,intensity,region
# 40.7128,-74.0060,85.5,North America
# 51.5074,-0.1278,72.3,Europe
# 35.6762,139.6503,91.2,Asia

# Data Processing Flow:
import pandas as pd
from data_processor import GeographicDataProcessor

# Load data
processor = GeographicDataProcessor()
df = processor.load_data('your_data.csv')

# Validate coordinates
df = processor.validate_coordinates(df)

# Process data
df = processor.aggregate_by_region(df, region_col='region')

# Create visualization
from visualizer import create_density_heatmap
fig = create_density_heatmap(df, output_file='heatmap.html')

Visualization Types and Usage

Different geographic heatmap types and their use cases:

Density Heatmap: Color-coded intensity visualization using go.Densitymapbox()
Scatter Heatmap: Individual data points using px.scatter_mapbox()
Regional Heatmap: Grouped by regions using px.scatter_mapbox() with color by region
3D Heatmap: Three-dimensional visualization using go.Scatter3d()
Animated Heatmap: Time-based animations using px.scatter_geo() with animation_frame
Clustered Heatmap: Machine learning clustering using sklearn.cluster and Plotly
Hotspot Heatmap: High-intensity detection using percentile thresholds
Contour Heatmap: Contour lines using scipy.interpolate and Plotly
Projections Comparison: Multiple projections using go.Scattergeo() with different projection types

Advanced Features Usage | Geographic Heatmap Usage Guide | How to Use Geographic Heatmaps

Using Basic Heatmap Functions

How to create different types of geographic heatmaps:

# Basic Heatmap Usage Examples:

from visualizer import (
    create_density_heatmap,
    create_scatter_heatmap,
    create_regional_heatmap,
    create_projection_comparison
)
from data_processor import generate_sample_data
import pandas as pd

# Load or generate data
df = generate_sample_data(n_points=1000)
# Or load from CSV:
# df = pd.read_csv('your_data.csv')

# 1. Create Density Heatmap:
create_density_heatmap(
    df,
    colorscale='Viridis',
    radius=20,
    output_file='heatmap_density.html'
)

# 2. Create Scatter Heatmap:
create_scatter_heatmap(
    df,
    hover_cols=['region'],
    colorscale='Plasma',
    output_file='heatmap_scatter.html'
)

# 3. Create Regional Heatmap:
create_regional_heatmap(
    df,
    region_col='region',
    output_file='heatmap_regional.html'
)

# 4. Create Projections Comparison:
create_projection_comparison(
    df,
    region_col='region',
    output_file='heatmap_projections.html'
)

Using Advanced Features

Create advanced geographic heatmap visualizations:

# Advanced Features Usage:

from advanced_features import (
    create_3d_heatmap,
    create_animated_heatmap,
    create_clustered_heatmap,
    create_hotspot_heatmap,
    create_contour_heatmap,
    detect_hotspots
)

# 1. Create 3D Geographic Heatmap:
create_3d_heatmap(
    df,
    output_file='heatmap_3d.html'
)

# 2. Create Animated Heatmap (requires time column):
# Load time-series data
df_timeseries = pd.read_csv('data_timeseries_2025.csv')
df_timeseries['date'] = pd.to_datetime(df_timeseries['date'])

create_animated_heatmap(
    df_timeseries,
    time_col='date',
    output_file='heatmap_animated.html'
)

# 3. Create Clustered Heatmap:
fig, df_clustered = create_clustered_heatmap(
    df,
    n_clusters=5,
    method='kmeans',
    output_file='heatmap_clustered.html'
)

# 4. Detect and Visualize Hotspots:
df_hotspots = detect_hotspots(df, threshold_percentile=90)
create_hotspot_heatmap(
    df,
    threshold_percentile=90,
    output_file='heatmap_hotspots.html'
)

# 5. Create Contour Heatmap:
create_contour_heatmap(
    df,
    output_file='heatmap_contour.html'
)

Understanding Heatmap Types

When to use different heatmap types for geographic analysis:

# Heatmap Type Usage Guide:

# 1. Density Heatmap
#    - Use: Visualize data intensity across geographic regions
#    - Shows: Color-coded density overlay on map
#    - Best for: Population density, event frequency, data concentration
#    - Example: City population density, traffic congestion

# 2. Scatter Heatmap
#    - Use: Display individual data points with intensity information
#    - Shows: Points with size and color based on intensity
#    - Best for: Specific locations, point-based data
#    - Example: Store locations, event locations, sensor data

# 3. Regional Heatmap
#    - Use: Compare data across geographic regions
#    - Shows: Color-coded regions with different intensities
#    - Best for: Regional comparison, administrative boundaries
#    - Example: Sales by region, population by country

# 4. 3D Heatmap
#    - Use: Three-dimensional visualization of geographic data
#    - Shows: Intensity as height (Z-axis)
#    - Best for: Multi-dimensional geographic patterns
#    - Example: Elevation data, intensity gradients

# 5. Animated Heatmap
#    - Use: Show changes in geographic data over time
#    - Shows: Time-based animation of heatmap
#    - Best for: Temporal analysis, trend visualization
#    - Example: Population growth, event spread, traffic patterns

# 6. Clustered Heatmap
#    - Use: Identify geographic clusters in data
#    - Shows: Machine learning-based clustering visualization
#    - Best for: Pattern detection, grouping similar locations
#    - Example: Customer clusters, event clusters

# 7. Hotspot Heatmap
#    - Use: Identify high-intensity areas automatically
#    - Shows: Highlighted hotspots above threshold
#    - Best for: Anomaly detection, high-activity areas
#    - Example: Crime hotspots, traffic hotspots

# 8. Contour Heatmap
#    - Use: Visualize intensity gradients
#    - Shows: Contour lines similar to topographic maps
#    - Best for: Gradient visualization, intensity levels
#    - Example: Temperature gradients, pollution levels

# 9. Projections Comparison
#    - Use: Compare data across different map projections
#    - Shows: Side-by-side comparison of projections
#    - Best for: Understanding projection effects, analysis
#    - Example: Global data visualization, comparison studies

Data Preparation and Customization

Prepare and customize your geographic data for visualization:

# Data Preparation Examples:

import pandas as pd
from data_processor import GeographicDataProcessor

# 1. Load and Validate Data:
processor = GeographicDataProcessor()
df = processor.load_data('your_data.csv')

# Validate coordinates (latitude: -90 to 90, longitude: -180 to 180)
df = processor.validate_coordinates(df)

# 2. Filter Data by Region:
df_north = df[df['region'] == 'North America']
df_europe = df[df['region'] == 'Europe']

# 3. Filter Data by Intensity:
df_high_intensity = df[df['intensity'] > df['intensity'].quantile(0.9)]

# 4. Filter Data by Date Range (for time-series):
df['date'] = pd.to_datetime(df['date'])
df_2025 = df[(df['date'] >= '2025-01-01') & (df['date'] <= '2025-12-31')]

# 5. Aggregate Data by Region:
df_aggregated = processor.aggregate_by_region(
    df,
    region_col='region',
    agg_func='mean'  # or 'sum', 'max', 'min'
)

# 6. Customize Color Scales:
# Available: 'Viridis', 'Plasma', 'Inferno', 'Cividis', 'Hot', 'Cool', etc.
create_density_heatmap(df, colorscale='Plasma')

# 7. Adjust Density Radius:
# Larger radius = smoother heatmap (default: 20)
create_density_heatmap(df, radius=30)

# 8. Change Map Style:
# Available: 'open-street-map', 'carto-positron', 'carto-darkmatter', etc.
create_density_heatmap(df, mapbox_style='carto-positron')

Exporting Heatmaps

Export heatmaps to different formats:

# Export Heatmap Examples:

from visualizer import create_density_heatmap
from advanced_features import export_to_image

# 1. Export as HTML (interactive, default):
fig = create_density_heatmap(df, output_file='heatmap.html')
# Opens in browser with full interactivity

# 2. Export as PNG (requires kaleido):
# Install: pip install kaleido
fig = create_density_heatmap(df)
export_to_image(fig, 'heatmap.png', format='png', width=1920, height=1080)

# 3. Export as PDF (requires kaleido):
export_to_image(fig, 'heatmap.pdf', format='pdf')

# 4. Export as SVG (requires kaleido):
export_to_image(fig, 'heatmap.svg', format='svg')

# 5. Export with Custom Settings:
export_to_image(
    fig,
    'heatmap_custom.png',
    format='png',
    width=1920,
    height=1080,
    scale=2  # Higher scale = better quality
)

Complete Geographic Heatmap Workflow | Step-by-Step Guide | Heatmap Tutorial

Step-by-Step Geographic Heatmap Setup

Step 1: Install Dependencies

# Install all required packages
pip install -r requirements.txt

# Required packages:
# - pandas>=1.3.0
# - numpy>=1.21.0
# - plotly>=5.0.0
# - geopandas>=0.10.0
# - shapely>=1.8.0
# - scipy>=1.7.0
# - jupyter>=1.0.0
# - notebook>=6.4.0

# Optional packages for advanced features:
# - scikit-learn>=1.0.0  # For clustering analysis
# - kaleido>=0.2.1  # For image export (PNG, PDF, SVG)

# Verify installation
python check_project.py

Step 2: Prepare Data

# Option 1: Generate sample data
python data_generator.py
# This generates:
# - data_cities_population.csv (200 cities)
# - data_timeseries_2025.csv (18,250 records)
# - data_events_2025.csv (500 events)
# - data_traffic.csv (1,000 points)

# Option 2: Use your own data
# Prepare CSV file with columns: latitude, longitude, intensity, region (optional)
# Place file in project directory

# Option 3: Generate basic sample data
from data_processor import generate_sample_data
df = generate_sample_data(n_points=1000)
df.to_csv('sample_geographic_data.csv', index=False)

# Data format:
# latitude,longitude,intensity,region
# 40.7128,-74.0060,85.5,North America
# 51.5074,-0.1278,72.3,Europe

Step 3: Create Basic Heatmaps

# Run complete example (generates all basic visualizations):
python example.py

# Or run visualizer directly:
python visualizer.py

# This creates:
# - geographic_heatmap_density.html
# - geographic_heatmap_scatter.html
# - geographic_heatmap_regional.html
# - geographic_heatmap_projections.html

# Open HTML files in browser to view interactive heatmaps

Step 4: Explore Jupyter Notebooks

Open geographic_heatmap.ipynb for basic heatmap tutorials
Open advanced_features_demo.ipynb for advanced features
Run cells step-by-step to learn each visualization type
Modify code examples to use your own data
Export visualizations as HTML, PNG, PDF, or SVG

Step 5: Customize Visualizations

# Customize heatmaps in Python code:
from visualizer import create_density_heatmap

# Change color scale
create_density_heatmap(df, colorscale='Plasma')

# Adjust density radius (larger = smoother)
create_density_heatmap(df, radius=30)

# Change map style
create_density_heatmap(df, mapbox_style='carto-positron')

# Modify visualizer.py for default settings
# Modify advanced_features.py for advanced configurations

Geographic Heatmap Customization Examples | Customization Guide | Code Examples

Customizing Color Scales

Change color schemes for geographic heatmaps:

# Customize Color Scales:

from visualizer import create_density_heatmap

# Available color scales:
# 'Viridis', 'Plasma', 'Inferno', 'Cividis', 'Hot', 'Cool', 
# 'Blues', 'Greens', 'Reds', 'YlOrRd', 'RdYlBu', etc.

# Example 1: Use Plasma color scale
create_density_heatmap(df, colorscale='Plasma')

# Example 2: Use custom color scale
create_density_heatmap(df, colorscale='Hot')

# Example 3: Use sequential color scale
create_density_heatmap(df, colorscale='Blues')

# Example 4: Use diverging color scale
create_density_heatmap(df, colorscale='RdYlBu')

Adjusting Density Radius

Control the smoothness and coverage of density heatmaps:

# Adjust Density Radius:

from visualizer import create_density_heatmap

# Smaller radius (10-15): More detailed, sharper boundaries
create_density_heatmap(df, radius=10)

# Default radius (20): Balanced detail and smoothness
create_density_heatmap(df, radius=20)

# Larger radius (30-50): Smoother, more generalized
create_density_heatmap(df, radius=30)

# Very large radius (50+): Very smooth, less detail
create_density_heatmap(df, radius=50)

# Tip: Adjust based on data density and desired visualization style

Changing Map Styles

Use different map base layers for your heatmaps:

# Change Map Styles:

from visualizer import create_density_heatmap

# Available map styles:
# 'open-street-map' - OpenStreetMap (default)
# 'carto-positron' - Light, minimal style
# 'carto-darkmatter' - Dark style
# 'stamen-terrain' - Terrain style
# 'stamen-toner' - High contrast black and white
# 'stamen-watercolor' - Watercolor style

# Example 1: Light, minimal style
create_density_heatmap(df, mapbox_style='carto-positron')

# Example 2: Dark style
create_density_heatmap(df, mapbox_style='carto-darkmatter')

# Example 3: Terrain style
create_density_heatmap(df, mapbox_style='stamen-terrain')

# Example 4: High contrast
create_density_heatmap(df, mapbox_style='stamen-toner')

Modifying Data Source

Load geographic data from different sources:

# Load Geographic Data from Different Sources:

import pandas as pd
from visualizer import create_density_heatmap

# Option 1: Load from CSV file
df = pd.read_csv('your_geographic_data.csv')

# Option 2: Load from Excel
df = pd.read_excel('your_geographic_data.xlsx')

# Option 3: Load from database
import sqlite3
conn = sqlite3.connect('geographic_data.db')
df = pd.read_sql_query("SELECT latitude, longitude, intensity FROM locations", conn)
conn.close()

# Option 4: Load from API
import requests
response = requests.get('https://api.example.com/locations')
data = response.json()
df = pd.DataFrame(data)

# Option 5: Generate sample data
from data_processor import generate_sample_data
df = generate_sample_data(n_points=1000)

# Option 6: Use built-in data generators
from data_generator import generate_city_population_data
df = generate_city_population_data(n_cities=200)

# Then create heatmap
create_density_heatmap(df, output_file='heatmap.html')

Customizing Heatmap Layout

Modify heatmap appearance and layout settings:

# Customize Heatmap Layout:

from visualizer import create_density_heatmap
import plotly.graph_objects as go

# Create heatmap
fig = create_density_heatmap(df)

# Customize layout
fig.update_layout(
    title={
        'text': 'Custom Geographic Heatmap',
        'x': 0.5,
        'font': {'size': 24, 'color': '#1e293b'}
    },
    height=800,  # Adjust height
    margin=dict(l=0, r=0, t=50, b=0),
    mapbox=dict(
        zoom=2,  # Adjust zoom level (1-20)
        center=dict(lat=40.0, lon=-100.0)  # Set center point
    )
)

# Update colorbar
fig.update_traces(
    colorbar=dict(
        title="Intensity",
        titleside="right",
        titlefont=dict(size=16),
        len=0.5,
        y=0.5
    )
)

# Save
fig.write_html('custom_heatmap.html')

Dataset Information | Data Format | CSV Format | Data Requirements

Data Format Requirements

The project requires CSV format for geographic data:

Required columns: latitude, longitude, intensity
Optional columns: region, date (for time-series), city, name
Latitude range: -90 to 90 (degrees)
Longitude range: -180 to 180 (degrees)
Intensity: Numeric values representing data intensity/density
Automatic coordinate validation and data processing
Support for time-series data with date column

Sample Data Format

Your geographic data CSV file should follow this structure:

# CSV file structure (geographic_data.csv):
latitude,longitude,intensity,region
40.7128,-74.0060,85.5,North America
51.5074,-0.1278,72.3,Europe
35.6762,139.6503,91.2,Asia
-23.5505,-46.6333,68.7,South America
30.0444,31.2357,55.9,Africa

# Column descriptions:
# - latitude: Latitude coordinate (-90 to 90 degrees)
# - longitude: Longitude coordinate (-180 to 180 degrees)
# - intensity: Data intensity/density value (numeric)
# - region: Geographic region name (optional, text)

# For time-series data, add date column:
# latitude,longitude,intensity,date,region
# 40.7128,-74.0060,85.5,2025-01-01,North America
# 40.7128,-74.0060,87.2,2025-01-02,North America
# 51.5074,-0.1278,72.3,2025-01-01,Europe

Generating Sample Data

Use the included data generators to create sample geographic datasets:

# Generate sample data
python data_generator.py

# This will generate:
# - data_cities_population.csv (200 cities with population data)
# - data_timeseries_2025.csv (18,250 records for 50 locations in 2025)
# - data_events_2025.csv (500 events for 2025)
# - data_traffic.csv (1,000 traffic/congestion data points)

# Or generate basic sample data:
from data_processor import generate_sample_data
df = generate_sample_data(n_points=1000)
df.to_csv('sample_geographic_data.csv', index=False)

# Customize data generation:
# Edit data_generator.py to modify:
# - Number of cities/locations
# - Date ranges
# - Intensity ranges
# - Geographic regions

Using Your Own Geographic Data

Use your own geographic data for visualization:

# Steps to use your own geographic data:

# 1. Prepare your CSV file
#    - Required: latitude, longitude, intensity columns
#    - Optional: region, date, city, name columns
#    - Latitude: -90 to 90 degrees
#    - Longitude: -180 to 180 degrees
#    - Intensity: Numeric values

# 2. Load and validate data
import pandas as pd
from data_processor import GeographicDataProcessor

processor = GeographicDataProcessor()
df = processor.load_data('your_data.csv')
df = processor.validate_coordinates(df)

# 3. Create heatmap
from visualizer import create_density_heatmap
create_density_heatmap(df, output_file='your_heatmap.html')

# 4. Verify data format
#    - Check coordinate ranges
#    - Ensure no missing values in required columns
#    - Verify intensity values are numeric
#    - Validate date format if using time-series data

# 5. Use with all visualization types
#    - All heatmap types work with your data
#    - Advanced features (clustering, hotspots) work automatically
#    - Time-series features work if date column is present

Troubleshooting & Best Practices | Common Issues | Performance Optimization | Best Practices

Common Issues

Port Already in Use: Change port in .streamlit/config.toml (default: 8501). Or stop the process using the port: lsof -ti:8501 | xargs kill
Data File Not Found: Ensure your CSV file exists with required columns (latitude, longitude, intensity). Use data_generator.py to create sample data
Import Errors: Verify all dependencies installed: pip install -r requirements.txt. Check Python version (3.8+)
Coordinate Errors: Ensure latitude is between -90 and 90, longitude is between -180 and 180. Use validate_coordinates() function
Heatmap Not Rendering: Check browser console for JavaScript errors. Verify Plotly is loaded correctly
Slow Performance: Reduce data size, use density heatmaps instead of scatter for large datasets, or sample data
Memory Issues: Reduce number of data points, use data aggregation, or process data in chunks
Export Not Working: Ensure kaleido is installed for image export: pip install kaleido
Map Not Loading: Check internet connection (for Mapbox tiles). Verify mapbox_style parameter is correct
Clustering Errors: Ensure scikit-learn is installed: pip install scikit-learn
Animation Not Working: Verify date column exists and is in datetime format. Check time_col parameter
3D Heatmap Issues: Ensure all required columns are present. Check browser supports WebGL
HTML Files Not Opening: Open HTML files in modern browser (Chrome, Firefox, Edge). Check file path is correct
Color Scale Issues: Verify colorscale name is valid. Use 'Viridis', 'Plasma', 'Inferno', etc.

Performance Optimization Tips

Data Size: Sample large datasets or use data aggregation to reduce number of points
Heatmap Type: Use density heatmaps for large datasets (faster than scatter heatmaps)
Radius Adjustment: Increase radius for smoother heatmaps, decrease for more detail
Caching: Cache processed data results to avoid repeated calculations
Data Preprocessing: Pre-process and validate data before creating visualizations
Coordinate Validation: Validate coordinates early to avoid processing errors
HTML File Size: Large HTML files may load slowly. Consider exporting as images for sharing
Browser Performance: Use modern browsers for best performance. Clear cache if issues occur

Best Practices

Data Quality: Ensure data is clean, coordinates are valid, and intensity values are numeric
Coordinate Format: Always validate latitude (-90 to 90) and longitude (-180 to 180) ranges
Intensity Values: Ensure intensity column contains numeric values. Handle missing values appropriately
Data Size: For large datasets (10K+ points), consider sampling or aggregation
Color Scales: Choose appropriate color scales for your data type (sequential, diverging, etc.)
Error Handling: Add error handling in visualization functions to prevent crashes
Data Validation: Validate data format and types before processing
Export Formats: Use HTML for interactive sharing, PNG/PDF for reports and presentations
Map Styles: Choose map styles that complement your data visualization
Documentation: Document custom modifications and data sources
Testing: Test with different data sizes and coordinate ranges
Sharing: HTML files are standalone and portable. Share via email, cloud storage, or web hosting

Use Cases and Applications

Population Density Visualization: Visualize city population density across regions
Event Mapping: Map natural disasters, events, or incidents geographically
Traffic Analysis: Analyze traffic congestion and patterns across locations
Time-Series Geographic Data: Track changes in geographic data over time
Hotspot Detection: Identify high-intensity areas automatically
Clustering Analysis: Group similar geographic locations using machine learning
Regional Comparison: Compare data across different geographic regions
Spatial Pattern Analysis: Identify patterns and trends in geographic data
Location-Based Analytics: Analyze location-based data for business insights
Geographic Reporting: Generate geographic reports and presentations

Performance Benchmarks

Expected performance for different geographic data sizes:

Data Size	Points	Load Time	Heatmap Render	Memory Usage
Small	100 - 1K	< 1 second	< 1 second	< 50 MB
Medium	1K - 10K	1-2 seconds	1-3 seconds	50-150 MB
Large	10K - 100K	2-5 seconds	3-8 seconds	150-400 MB
Very Large	100K+	5-15 seconds	8-20 seconds	400+ MB

Note: Performance depends on hardware, data complexity, and heatmap type. Density heatmaps are faster than scatter heatmaps. Consider data sampling for very large datasets.

System Requirements

Recommended system requirements for optimal performance:

Component	Minimum	Recommended	Optimal
Python	3.8	3.9+	3.10+
RAM	4 GB	8 GB	16 GB+
CPU	2 cores	4 cores	8+ cores
Storage	100 MB	500 MB	1 GB+
Browser	Chrome 90+	Chrome 100+	Latest

Note: Geographic heatmaps run in browser. No GPU required. Performance scales with data size. HTML files are interactive and can be shared easily.

Real-World Examples & Use Cases | Geographic Heatmap Use Cases | Spatial Analysis Examples

Example 1: City Population Density Visualization

Visualize city population data across regions:

# 1. Generate or load city population data
from data_generator import generate_city_population_data
df = generate_city_population_data(n_cities=200)
# Or load: df = pd.read_csv('data_cities_population.csv')

# 2. Create density heatmap
from visualizer import create_density_heatmap
create_density_heatmap(
    df,
    lat_col='latitude',
    lon_col='longitude',
    intensity_col='population',
    colorscale='Viridis',
    output_file='city_population_heatmap.html'
)

# 3. Create regional heatmap
from visualizer import create_regional_heatmap
create_regional_heatmap(
    df,
    region_col='region',
    intensity_col='population',
    output_file='city_population_regional.html'
)

# 4. Analyze patterns
# - Identify high-population clusters
# - Compare regional distributions
# - Visualize population density gradients

Example 2: Time-Series Event Tracking

Track events over time with animated heatmaps:

# Use Case: Track events over time (2025 data)

# 1. Load time-series event data
from data_generator import generate_event_data
df_events = generate_event_data(n_events=500)
# Or load: df = pd.read_csv('data_events_2025.csv')

# 2. Create animated heatmap
from advanced_features import create_animated_heatmap
create_animated_heatmap(
    df_events,
    lat_col='latitude',
    lon_col='longitude',
    intensity_col='intensity',
    time_col='date',
    output_file='events_animated_2025.html'
)

# 3. Analyze temporal patterns
# - View event spread over time
# - Identify peak event periods
# - Track geographic event distribution
# - Export frames for presentations

Example 3: Traffic Congestion Analysis

Analyze traffic patterns and congestion hotspots:

# Use Case: Traffic congestion analysis

# 1. Load traffic data
from data_generator import generate_traffic_data
df_traffic = generate_traffic_data(n_points=1000)
# Or load: df = pd.read_csv('data_traffic.csv')

# 2. Create density heatmap
from visualizer import create_density_heatmap
create_density_heatmap(
    df_traffic,
    intensity_col='congestion',
    colorscale='Reds',  # Red for high congestion
    radius=25,
    output_file='traffic_congestion_heatmap.html'
)

# 3. Detect traffic hotspots
from advanced_features import detect_hotspots, create_hotspot_heatmap
df_hotspots = detect_hotspots(df_traffic, threshold_percentile=90)
create_hotspot_heatmap(
    df_traffic,
    threshold_percentile=90,
    output_file='traffic_hotspots.html'
)

# 4. Analyze patterns
# - Identify high-congestion areas
# - Plan route optimization
# - Visualize traffic density

Example 4: Geographic Clustering Analysis

Identify geographic clusters using machine learning:

# Use Case: Geographic clustering analysis

# 1. Load geographic data
from data_processor import generate_sample_data
df = generate_sample_data(n_points=1000)

# 2. Create clustered heatmap
from advanced_features import create_clustered_heatmap
fig, df_clustered = create_clustered_heatmap(
    df,
    n_clusters=5,
    method='kmeans',  # or 'dbscan'
    output_file='clustered_heatmap.html'
)

# 3. Analyze clusters
# - Identify geographic clusters
# - Group similar locations
# - Analyze cluster characteristics
# - Export cluster data for further analysis

# 4. Use cases:
# - Customer segmentation by location
# - Event clustering
# - Resource allocation planning
# - Geographic pattern identification

Example 5: Multi-Projection Comparison

Compare data across different map projections:

# Use Case: Compare map projections

# 1. Load geographic data
from data_processor import generate_sample_data
df = generate_sample_data(n_points=1000)

# 2. Create projections comparison
from visualizer import create_projection_comparison
create_projection_comparison(
    df,
    region_col='region',
    output_file='projections_comparison.html'
)

# 3. Compare projections:
# - Mercator: Preserves angles, distorts area
# - Natural Earth: Balanced distortion
# - Orthographic: Globe-like view
# - Equirectangular: Simple rectangular projection

# 4. Use cases:
# - Global data visualization
# - Understanding projection effects
# - Choosing best projection for analysis
# - Educational purposes

Integration Examples | Database Integration | API Integration | Web Integration

Integration with Database

Load geographic data from SQL database:

# Load geographic data from SQL database

import sqlite3
import pandas as pd
from visualizer import create_density_heatmap

def load_geographic_data_from_db():
    """Load geographic data from SQL database."""
    conn = sqlite3.connect('geographic_data.db')
    query = """
        SELECT 
            latitude,
            longitude,
            intensity,
            region,
            date
        FROM locations
        ORDER BY date
    """
    df = pd.read_sql_query(query, conn)
    conn.close()
    
    # Convert date if present
    if 'date' in df.columns:
        df['date'] = pd.to_datetime(df['date'])
    
    return df

# Load and create heatmap
df = load_geographic_data_from_db()
create_density_heatmap(df, output_file='heatmap_from_db.html')

# For MySQL/PostgreSQL:
# import mysql.connector
# conn = mysql.connector.connect(
#     host='localhost',
#     user='username',
#     password='password',
#     database='geographic_db'
# )

Integration with REST API

Load geographic data from REST API endpoint:

# Load geographic data from REST API

import requests
import pandas as pd
from visualizer import create_density_heatmap

def load_geographic_data_from_api():
    """Load geographic data from REST API."""
    response = requests.get(
        'https://api.example.com/locations',
        headers={'Authorization': 'Bearer YOUR_TOKEN'}
    )
    data = response.json()
    df = pd.DataFrame(data['locations'])
    
    # Ensure required columns exist
    if 'date' in df.columns:
        df['date'] = pd.to_datetime(df['date'])
    
    return df

# Load and create heatmap
df = load_geographic_data_from_api()
create_density_heatmap(df, output_file='heatmap_from_api.html')

# For real-time updates, refresh data periodically:
import time
while True:
    df = load_geographic_data_from_api()
    create_density_heatmap(df, output_file='heatmap_live.html')
    time.sleep(300)  # Update every 5 minutes

Embedding Heatmaps in Web Pages

Embed interactive heatmaps in existing websites:

# Embed Heatmap in Web Page:

# Option 1: Direct HTML embedding
# The generated HTML files are standalone and can be embedded:

<iframe src="path/to/geographic_heatmap_density.html" 
        width="100%" 
        height="700px" 
        frameborder="0">
</iframe>

# Option 2: Use Plotly's HTML output
from visualizer import create_density_heatmap
fig = create_density_heatmap(df)

# Get HTML div and script
html_div = fig.to_html(include_plotlyjs='cdn', div_id='heatmap-div')

# Embed in your HTML page:
# <div id="heatmap-div"></div>
# <script>...plotly.js code...</script>

# Option 3: Serve HTML files via web server
# Place HTML files in web server directory
# Access via: https://yourdomain.com/heatmaps/geographic_heatmap_density.html

# Option 4: Use Flask/Django to serve heatmaps
from flask import Flask, send_file
app = Flask(__name__)

@app.route('/heatmap')
def show_heatmap():
    create_density_heatmap(df, output_file='static/heatmap.html')
    return send_file('static/heatmap.html')

Sharing Heatmaps

Share interactive heatmaps with others:

# Sharing Heatmap Options:

# 1. Share HTML files directly
# - HTML files are standalone and portable
# - Can be opened in any browser
# - No server required
# - Share via email, cloud storage, etc.

# 2. Upload to web hosting
# - Upload HTML files to web server
# - Share URL with team members
# - Files remain interactive

# 3. Export as images (for reports)
from advanced_features import export_to_image
fig = create_density_heatmap(df)
export_to_image(fig, 'heatmap.png', format='png', width=1920, height=1080)
# Use in presentations, reports, documents

# 4. Use Plotly Chart Studio (optional)
# Upload to Plotly Chart Studio for cloud hosting
# fig.write_html('heatmap.html')
# Then upload to chart-studio.plotly.com

# 5. GitHub Pages
# - Push HTML files to GitHub repository
# - Enable GitHub Pages
# - Share public URL

Contact Information | Support | Get Help | Contact RSK World

Get in Touch

Developer: Molla Samser
Designer & Tester: Rima Khatun

rskworld.in

help@rskworld.in support@rskworld.in

+91 93305 39277

License | Open Source License | Project License

This project is for educational purposes only. See LICENSE file for more details.

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