fastGeoToolkit Python

A novel high-performance geospatial analysis framework with advanced route density mapping algorithms, powered by Rust for maximum performance in Python.

Installation

pip install fastgeotoolkit

Optional Dependencies

For enhanced functionality, install with optional dependencies:

# For visualization
pip install fastgeotoolkit[examples]  # includes matplotlib, folium, etc.

# For development
pip install fastgeotoolkit[dev]       # includes testing and linting tools

# Everything
pip install fastgeotoolkit[all]

Quick Start

Basic Usage

import fastgeotoolkit as fgt

# Load and process GPX files
heatmap = fgt.load_gpx_file("track.gpx")
print(f"Generated heatmap with {len(heatmap.tracks)} tracks")
print(f"Maximum frequency: {heatmap.max_frequency}")

# Access individual tracks with frequency data
for i, track in enumerate(heatmap.tracks):
    print(f"Track {i}: {track.frequency}x frequency, {len(track.coordinates)} points")

Route Density Analysis

import fastgeotoolkit as fgt

# Process multiple GPX files for route density analysis
gpx_files = ["route1.gpx", "route2.gpx", "route3.gpx"]
heatmap = fgt.load_multiple_gpx_files(gpx_files)

# Analyze route popularity
stats = fgt.calculate_heatmap_statistics(heatmap)
print(f"Total tracks: {stats['total_tracks']}")
print(f"Average frequency: {stats['mean_frequency']:.1f}")
print(f"Frequency distribution: {stats['frequency_distribution']}")

# Find most popular routes
popular_routes = [
    track for track in heatmap.tracks 
    if track.frequency == heatmap.max_frequency
]
print(f"Found {len(popular_routes)} most popular routes")

Interactive Visualization

import fastgeotoolkit as fgt

# Create interactive map with Folium
heatmap = fgt.load_gpx_file("tracks.gpx")
map_viz = fgt.create_folium_map(heatmap)
map_viz.save("heatmap.html")

# Create matplotlib visualization
fgt.visualize_heatmap(heatmap, figsize=(15, 10))

Advanced GPS Processing

import fastgeotoolkit as fgt

# Decode polylines (Google Maps format)
coordinates = fgt.decode_polyline("_p~iF~ps|U_ulLnnqC_mqNvxq`@")
print(f"Decoded {len(coordinates)} points")

# Calculate track statistics
stats = fgt.calculate_track_statistics(coordinates)
print(f"Distance: {stats.distance_km:.2f} km")
print(f"Bounding box: {stats.bounding_box}")

# Validate coordinates
validation = fgt.validate_coordinates(coordinates)
print(f"Valid coordinates: {validation.valid_count}/{validation.total_count}")
for issue in validation.issues:
    print(f"  - {issue}")

Core Features

Route Density Mapping

Advanced segment-based analysis for route popularity:

import fastgeotoolkit as fgt

# Multiple GPS tracks from the same area
tracks = [
    [(40.7128, -74.0060), (40.7589, -73.9851)],  # NYC to Times Square
    [(40.7128, -74.0060), (40.7589, -73.9851)],  # Same route (increases frequency)
    [(40.7505, -73.9934), (40.7831, -73.9712)],  # Empire State to Central Park
]

# Process with our novel frequency algorithm
result = fgt.process_polylines([fgt.coordinates_to_polyline(track) for track in tracks])

# Analyze route overlap and frequency
for track in result.tracks:
    popularity = "High" if track.frequency > result.max_frequency * 0.7 else "Medium" if track.frequency > result.max_frequency * 0.3 else "Low"
    print(f"Route popularity: {popularity} (frequency: {track.frequency})")

Track Analysis & Statistics

Comprehensive geospatial analysis:

import fastgeotoolkit as fgt

coordinates = [(40.7128, -74.0060), (40.7589, -73.9851), (40.7831, -73.9712)]

# Detailed statistics
stats = fgt.calculate_track_statistics(coordinates)
print(f"Distance: {stats.distance_km:.2f} km")
print(f"Points: {stats.point_count}")
print(f"Bounds: {stats.bounding_box}")

# Find intersections between multiple tracks
tracks = [track1, track2, track3]
intersections = fgt.find_track_intersections(tracks, tolerance=0.001)
for intersection in intersections:
    print(f"Intersection at {intersection.coordinate} between tracks {intersection.track_indices}")

# Calculate geographic coverage
coverage = fgt.calculate_coverage_area(tracks)
print(f"Coverage area: {coverage.area_km2:.2f} km²")

Track Manipulation

Powerful track processing capabilities:

import fastgeotoolkit as fgt

# Simplify tracks (reduce point density while preserving shape)
simplified = fgt.simplify_coordinates(dense_track, tolerance=0.001)
print(f"Reduced from {len(dense_track)} to {len(simplified)} points")

# Split tracks by gaps
split_tracks = fgt.split_track_by_gaps(track_with_gaps, max_gap_km=5.0)
print(f"Split into {len(split_tracks)} continuous segments")

# Filter by geographic bounds
bounds = (40.7, -74.1, 40.8, -73.9)  # NYC area
filtered = fgt.filter_coordinates_by_bounds(coordinates, bounds)

# Merge similar tracks
merged = fgt.merge_nearby_tracks(similar_tracks, distance_threshold=0.5)

📝 Format Conversion

Convert between popular GPS formats:

import fastgeotoolkit as fgt

# Convert to GeoJSON
geojson = fgt.coordinates_to_geojson(coordinates, {
    "name": "My Route",
    "sport": "cycling",
    "date": "2024-01-15"
})

# Export to GPX
gpx_content = fgt.export_to_gpx([track1, track2], {
    "creator": "fastGeoToolkit",
    "version": "1.1"
})

# Encode as polyline
polyline = fgt.coordinates_to_polyline(coordinates)
print(f"Encoded polyline: {polyline}")

NumPy Integration

Seamless integration with NumPy arrays:

import fastgeotoolkit as fgt
import numpy as np

# Convert between NumPy arrays and coordinate lists
coords_array = np.array([[40.7128, -74.0060], [40.7589, -73.9851]])
coord_list = fgt.numpy_to_coordinates(coords_array)

# Process NumPy data
stats = fgt.calculate_track_statistics(coord_list)
simplified = fgt.simplify_coordinates(coord_list, tolerance=0.001)

# Convert back to NumPy
result_array = fgt.coordinates_to_numpy(simplified)

Performance

fastGeoToolkit leverages Rust's performance for demanding geospatial tasks:

• 10-100x faster than pure Python implementations
• Memory efficient processing of large GPS datasets
• Parallel processing for multi-track analysis
• Optimized algorithms for route density calculation

Benchmarks

import fastgeotoolkit as fgt
import time

# Load large dataset
start = time.time()
heatmap = fgt.load_multiple_gpx_files(large_gpx_file_list)
processing_time = time.time() - start

print(f"Processed {len(heatmap.tracks)} tracks in {processing_time:.2f} seconds")
print(f"Performance: {len(heatmap.tracks)/processing_time:.0f} tracks/second")

Advanced Examples

Route Popularity Analysis

import fastgeotoolkit as fgt

# Load cycling route data
heatmap = fgt.load_multiple_gpx_files(["route1.gpx", "route2.gpx", "route3.gpx"])

# Identify popular segments
popular_segments = [
    track for track in heatmap.tracks 
    if track.frequency >= heatmap.max_frequency * 0.8
]

print(f"Found {len(popular_segments)} highly popular route segments")

# Create frequency distribution analysis
stats = fgt.calculate_heatmap_statistics(heatmap)
for freq, count in stats['frequency_distribution'].items():
    percentage = (count / stats['total_tracks']) * 100
    print(f"Frequency {freq}: {count} routes ({percentage:.1f}%)")

Interactive Web Visualization

import fastgeotoolkit as fgt

# Create web-ready heatmap
heatmap = fgt.load_gpx_file("mountain_bike_trails.gpx")

# Generate interactive map
map_viz = fgt.create_folium_map(
    heatmap, 
    zoom_start=14,
    colormap='viridis'
)

# Add custom styling and save
map_viz.save("trail_heatmap.html")
print("Interactive map saved to trail_heatmap.html")

Batch Processing Pipeline

import fastgeotoolkit as fgt
from pathlib import Path

# Process all GPX files in a directory
gpx_directory = Path("./gpx_data/")
gpx_files = list(gpx_directory.glob("*.gpx"))

# Batch process with progress tracking
print(f"Processing {len(gpx_files)} GPX files...")
heatmap = fgt.load_multiple_gpx_files(gpx_files)

# Export results in multiple formats
fgt.save_heatmap_to_geojson(heatmap, "output.geojson")
gpx_output = fgt.export_to_gpx([track.coordinates for track in heatmap.tracks])
with open("combined_routes.gpx", "w") as f:
    f.write(gpx_output)

print("Batch processing complete!")

API Reference

Core Functions

• load_gpx_file(path) - Load single GPX file
• load_multiple_gpx_files(paths) - Load multiple GPX files
• process_gpx_files(file_data_list) - Process GPX binary data
• decode_polyline(encoded) - Decode Google polyline format
• process_polylines(polylines) - Process multiple polylines

Analysis Functions

• calculate_track_statistics(coords) - Track distance, bounds, point count
• validate_coordinates(coords) - Validate GPS coordinates
• find_track_intersections(tracks, tolerance) - Find intersection points
• calculate_coverage_area(tracks) - Geographic coverage analysis
• cluster_tracks_by_similarity(tracks, threshold) - Group similar routes

Manipulation Functions

• simplify_coordinates(coords, tolerance) - Reduce point density
• split_track_by_gaps(coords, max_gap_km) - Split by spatial gaps
• merge_nearby_tracks(tracks, threshold) - Merge similar tracks
• filter_coordinates_by_bounds(coords, bounds) - Geographic filtering
• resample_track(coords, target_points) - Resample to target density

Conversion Functions

• coordinates_to_geojson(coords, properties) - Export to GeoJSON
• coordinates_to_polyline(coords) - Encode as polyline
• export_to_gpx(tracks, metadata) - Export to GPX format
• get_file_info(file_data) - File format information

Utility Functions

• save_heatmap_to_geojson(heatmap, path) - Save heatmap as GeoJSON
• create_folium_map(heatmap) - Create interactive map
• visualize_heatmap(heatmap) - Create matplotlib visualization
• calculate_heatmap_statistics(heatmap) - Comprehensive statistics
• numpy_to_coordinates(array) - NumPy array conversion
• coordinates_to_numpy(coords) - Convert to NumPy array

Type Hints

fastGeoToolkit provides comprehensive type hints for better development experience:

from fastgeotoolkit import (
    Coordinate,
    HeatmapResult,
    HeatmapTrack,
    ValidationResult,
    TrackStatistics,
    FileInfo
)

def process_route(coords: List[Coordinate]) -> TrackStatistics:
    return fgt.calculate_track_statistics(coords)

Requirements

• Python 3.8+
• NumPy (included in requirements)

Optional Dependencies

• matplotlib - For visualization
• folium - For interactive maps
• geopandas - For advanced geospatial operations
• pandas - For data analysis

License

MIT License - see LICENSE file for details.

Performance Tips

1. Use batch processing for multiple files with load_multiple_gpx_files()
2. Simplify tracks before analysis to reduce computation time
3. Set appropriate tolerances for intersection detection
4. Use NumPy arrays for large coordinate datasets
5. Cache results of expensive operations when processing similar data

Contributing

Contributions are welcome! Please see our Contributing Guide for details.

Citation

If you use fastGeoToolkit in research, please cite:

@software{fastgeotoolkit2024,
  title={fastGeoToolkit: A Novel High-Performance Geospatial Analysis Framework with Advanced Route Density Mapping},
  author={a0a7},
  year={2024},
  url={https://github.com/a0a7/fastgeotoolkit},
  version={0.1.3}
}