Point Pattern

Overview

The point pattern analysis module in memories-dev provides tools for analyzing spatial point patterns in Earth observation data. The implementation is based on the actual code in memories/utils/processors/vector_processor.py.

Core Implementation

The main point pattern analysis functionality is implemented in the VectorProcessor class:

class VectorProcessor:
    """Processor for vector data."""

    def calculate_density(
        self,
        gdf: gpd.GeoDataFrame,
        resolution: float,
        bbox: Union[Tuple[float, float, float, float], Polygon],
        kernel_size: int = 5
    ) -> np.ndarray:

Args:
gdf: GeoDataFrame to analyze resolution: Output resolution in meters bbox: Bounding box or Polygon kernel_size: Size of the smoothing kernel

Returns:
Density array

Density Analysis

The density calculation uses a Gaussian smoothing approach:

# Rasterize first
raster = self.rasterize_layer(gdf, resolution, bbox)

# Apply Gaussian smoothing
kernel = np.ones((kernel_size, kernel_size)) / (kernel_size * kernel_size)
density = cv2.filter2D(raster.astype(np.float32), -1, kernel)

Spatial Analysis Tools

Area-based statistics calculation:

def calculate_area_statistics(
    gdf: gpd.GeoDataFrame,
    value_column: str = None
) -> Dict[str, float]:

Args:
gdf: Input GeoDataFrame value_column: Optional column for weighted statistics

Returns:
Dictionary of statistics

‘mean_area’: gdf.geometry.area.mean(), ‘count’: len(gdf)

}

if value_column and value_column in gdf.columns:

stats.update({

‘weighted_mean’: np.average(
gdf.geometry.area, weights=gdf[value_column]

)

})

return stats

Configuration

Analysis parameters are defined in analysis_config.py:

URBAN_CONFIG = {
    'building_density_threshold': 0.4,
    'road_density_threshold': 0.2,
    'min_building_size': 50,  # square meters
    'buffer_distance': 100,  # meters
    'cluster_distance': 50  # meters
}

Usage Example

Here’s how to use the point pattern analysis in your code:

from memories.utils.processors.vector_processor import VectorProcessor

# Initialize processor
processor = VectorProcessor()

# Calculate density
density = processor.calculate_density(
    gdf=vector_data,
    resolution=10.0,  # 10 meter resolution
    bbox=area_bounds,
    kernel_size=5
)

# Calculate statistics
stats = calculate_area_statistics(
    gdf=vector_data,
    value_column='importance'
)

Integration with Property Analysis

The point pattern analysis is used in property analysis:

async def analyze_urban_patterns(
    self,
    bounds: Bounds,
    layers: List[str] = ['buildings', 'roads']
) -> Dict[str, Any]:

# Initialize vector processor if needed if self.vector_processor is None:

self.vector_processor = VectorTileProcessor(bounds=bounds, layers=layers)

# Get vector data vector_data = self.vector_processor.process_tile(bounds)

# Calculate urban metrics building_density = len(vector_data) / (

(bounds.east - bounds.west) * (bounds.north - bounds.south)

)

return {
‘building_density’: building_density, ‘building_count’: len(vector_data), ‘bounds’: bounds

}

Performance Considerations

Memory Usage - Vector data is processed in tiles - Efficient spatial indexing for large datasets
Computational Efficiency - Uses OpenCV for fast density calculations - Parallel processing for large areas
Optimization Settings .. code-block:: python

PROCESSING_CONFIG = {
‘tile_size’: 256, ‘overlap’: 32, ‘batch_size’: 8, ‘num_workers’: 4, ‘use_gpu’: True

}

Future Developments

Planned enhancements to the point pattern analysis module: 1. Implementation of advanced spatial statistics 2. Enhanced clustering algorithms 3. Integration with machine learning for pattern recognition 4. Support for temporal point pattern analysis