geofeaturekit 0.1.0

A Python library for extracting and analyzing urban features from OpenStreetMap data

GeoFeatureKit

A Python library for extracting and analyzing urban features from OpenStreetMap data. GeoFeatureKit helps urban planners, researchers, and developers analyze city infrastructure, amenities, and spatial patterns using standardized metrics.

Features

• Street Network Analysis: Extract and analyze street network characteristics including:

  • Street lengths and connectivity
  • Network topology and patterns
  • Intersection analysis
  • Street network density metrics
  • Dead-end detection
  • Street bearing analysis

• Points of Interest: Analyze the distribution and density of amenities, services, and other POIs:

  • Automatic categorization by type
  • Density analysis by category
  • Accessibility metrics
  • Diversity measures
  • Custom category grouping
  • Nearest neighbor analysis

• Land Use Analysis:

  • Land use mix calculations
  • Area measurements
  • Boundary analysis
  • Usage percentages
  • Mixed-use detection

• Data Quality Metrics:

  • Completeness scores
  • Data reliability measures
  • Coverage analysis
  • Source verification

Installation

# From PyPI
pip install geofeaturekit

# Or clone the repository for development
git clone https://github.com/yourusername/geofeaturekit.git
cd geofeaturekit
pip install -e .

Quick Start

from geofeaturekit import features_from_location

# Extract features for Times Square, New York
features = features_from_location(
    latitude=40.758,
    longitude=-73.9855,
    radius_meters=500,  # 500m radius around the point
    network_type='all'  # Include all street types
)

# Access specific metrics
network_metrics = features['metrics']['network_metrics']
poi_metrics = features['metrics']['poi_metrics']
land_use = features['metrics']['land_use_metrics']

Example Analysis: Times Square

Let's analyze Times Square, one of New York City's most iconic locations:

from geofeaturekit import features_from_location

# Analyze Times Square
features = features_from_location(
    latitude=40.758,
    longitude=-73.9855,
    radius_meters=500,  # 500m radius
    network_type='all'  # Include all street types
)

# The results include comprehensive metrics:
{
    "metadata": {
        "location": {
            "latitude": 40.758,
            "longitude": -73.9855
        },
        "radius_meters": 500,
        "network_type": "all",
        "area_sqm": 785398.2  # π * radius² = area of analysis
    },
    "metrics": {
        "network_metrics": {
            "basic_metrics": {
                "total_street_length_meters": 80044.7,  # Dense street network
                "total_intersections": 731,
                "total_dead_ends": 0,  # Well-connected streets
                "total_nodes": 777,
                "total_street_segments": 2313
            },
            "density_metrics": {
                "intersections_per_sqm": 0.000931,  # High intersection density
                "street_length_per_sqm": 0.101916,
                "nodes_per_sqm": 0.000989,
                "units": "per_square_meter"
            },
            "connectivity_metrics": {
                "average_connections_per_node": 3.411,  # Good connectivity
                "number_of_disconnected_networks": 1,  # Single connected network
                "number_of_street_loops": 1537,
                "streets_to_nodes_ratio": 2.977
            }
        },
        "poi_metrics": {
            "absolute_counts": {
                "total_points_of_interest": 1076,  # Very high POI count
                "counts_by_category": {
                    "total_restaurant_places": 173,
                    "total_fast_food_places": 77,
                    "total_cafe_places": 74,
                    "total_bicycle_parking_places": 71,
                    "total_bank_places": 24,
                    # ... more categories
                }
            },
            "density_metrics": {
                "points_of_interest_per_sqm": 0.00137,  # High POI density
                "density_by_category": {
                    "restaurant_places_per_sqm": 0.00022,
                    "fast_food_places_per_sqm": 0.000098,
                    "cafe_places_per_sqm": 0.000094
                }
            },
            "distribution_metrics": {
                "unique_category_count": 42,  # Diverse amenities
                "largest_category_count": 439,
                "largest_category_name": "unknown",
                "largest_category_count_percent": 40.8
            }
        },
        "pedestrian_network": {
            "intersection_spacing_meters": {
                "minimum": 0.8,
                "maximum": 286.6,
                "median": 12.1,  # Short blocks, walkable
                "standard_deviation": 51.5
            },
            "dead_end_street_count": 0,
            "total_dead_end_street_length_meters": 0.0
        },
        "land_use_metrics": {
            "area_measurements": {
                "total_area_sqm": 56.6,
                "commercial_area_sqm": 53.1,  # Predominantly commercial
                "open_space_area_sqm": 3.6
            }
        },
        "data_quality_metrics": {
            "data_completeness_percentages": {
                "percent_network_data_complete": 100.0,
                "percent_poi_data_complete": 100.0,
                "percent_land_use_data_complete": 100.0
            }
        }
    }
}

This example demonstrates several key features of Times Square's urban environment:

1. Dense Street Network:

   • 80km total street length in just 0.785 km² (500m radius)
   • 731 intersections with no dead ends
   • High connectivity (3.4 connections per node)

2. Rich Point of Interest Density:

   • 1,076 total POIs (1.37 POIs per 1000 m²)
   • 42 unique categories of amenities
   • High concentration of restaurants (173), cafes (74), and services

3. Pedestrian-Friendly Design:

   • Median intersection spacing of 12.1m
   • No dead-end streets
   • Dense network of pedestrian paths

4. Commercial District:

   • 93.8% commercial area (53.1/56.6 m²)
   • High concentration of retail and services
   • Mixed-use urban environment

5. Data Quality:

   • 100% complete network data
   • High reliability scores
   • Comprehensive POI coverage

This analysis reveals Times Square as a dense, highly connected commercial district with exceptional pedestrian accessibility and a diverse mix of amenities.

Example Output

Here's a sample of what you'll get (truncated for readability):

{
    "metadata": {
        "location": {
            "latitude": 40.758,
            "longitude": -73.9855
        },
        "radius_meters": 500,
        "network_type": "all",
        "area_sqm": 785398.2  # π * radius²
    },
    "metrics": {
        "network_metrics": {
            "basic_metrics": {
                "total_street_length_meters": 80044.7,
                "total_intersections": 731,
                "total_dead_ends": 0,
                "total_nodes": 777
            },
            "density_metrics": {
                "intersections_per_sqm": 0.000931,
                "street_length_per_sqm": 0.101916,
                "nodes_per_sqm": 0.000989,
                "units": "per_square_meter"
            }
        },
        "poi_metrics": {
            "absolute_counts": {
                "total_points_of_interest": 1076,
                "counts_by_category": {
                    "restaurant_places": 173,
                    "cafe_places": 74,
                    "bank_places": 24
                    # ... more categories
                }
            },
            "density_metrics": {
                "points_of_interest_per_sqm": 0.00137,
                "density_by_category": {
                    "restaurant_places_per_sqm": 0.00022,
                    "cafe_places_per_sqm": 0.000094
                    # ... more categories
                }
            }
        }
    }
}

Common Use Cases

1. Neighborhood Comparison

from geofeaturekit import features_from_location

# Compare two neighborhoods
locations = [
    (40.7829, -73.9654, "Central Park"),  # Central Park
    (40.7527, -73.9772, "Grand Central")  # Grand Central
]

comparisons = {}
for lat, lon, name in locations:
    features = features_from_location(
        latitude=lat,
        longitude=lon,
        radius_meters=500
    )
    comparisons[name] = features['metrics']

# Access specific metrics for comparison
for name, metrics in comparisons.items():
    poi_density = metrics['poi_metrics']['density_metrics']['points_of_interest_per_sqm']
    print(f"{name} POI Density: {poi_density:.6f} per m²")

2. Custom Analysis Area

from geofeaturekit import features_from_polygon
import geopandas as gpd

# Load a custom polygon (e.g., neighborhood boundary)
area = gpd.read_file("my_neighborhood.geojson")
features = features_from_polygon(
    area.geometry[0],
    network_type='drive'  # Focus on driveable streets
)

3. Batch Processing

from geofeaturekit import features_from_location
import pandas as pd

# Process multiple locations
locations_df = pd.read_csv("locations.csv")
results = []

for _, row in locations_df.iterrows():
    features = features_from_location(
        latitude=row['lat'],
        longitude=row['lon'],
        radius_meters=row['radius']
    )
    results.append({
        'location_id': row['id'],
        'features': features
    })

Output Metrics

All metrics follow SI (International System of Units) standards.

Network Metrics

Basic metrics:

• Total street length (meters)
• Number of intersections
• Number of nodes and edges
• Network connectivity measures

Density metrics (all per square meter):

• Street density (meters per m²)
• Intersection density (per m²)
• Node density (per m²)

Advanced metrics:

• Street network patterns
• Urban form characteristics
• Accessibility measures
• Street bearing distribution
• Dead-end analysis
• Network connectivity scores

POI Metrics

Basic metrics:

• Total POI count
• Category distribution
• Unique amenity types
• Category hierarchies

Density measures (all per square meter):

• Total POI density (per m²)
• Category-specific densities (per m²)
• Service concentration metrics

Diversity metrics:

• Category mix
• Service variety
• Land use mix
• Entropy measures
• Specialization indices

Area Measurements

All area measurements are in square meters (m²), following SI standards. This includes:

• Total area coverage
• Land use areas
• Service catchment areas
• Accessibility zones
• Building footprints
• Open space measurements

Data Quality Indicators

The library provides quality metrics for:

• Data completeness (0-100%)
• Source reliability scores
• Temporal accuracy
• Spatial precision
• Coverage consistency

Configuration

You can customize the analysis using configuration parameters:

from geofeaturekit import set_config

set_config({
    'poi_categories': ['restaurant', 'cafe', 'retail'],  # Focus on specific POIs
    'network_types': ['drive', 'walk'],  # Street network types to include
    'min_intersection_spacing': 10,  # Minimum meters between intersections
    'custom_projections': True,  # Use custom projections for accurate areas
})

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

The project includes several third-party packages with their own licenses - see the NOTICE.md file for details.