2.5. Spatial Analysis

2.5.1. Overview

Spatial analysis in memories-dev provides tools and methods for analyzing geographic data and spatial relationships.

2.5.2. Core Concepts

2.5.2.1. Spatial Data Types

Understanding different spatial data types:

from memories.spatial import (
    Point, LineString, Polygon,
    MultiPoint, MultiLineString, MultiPolygon
)

# Create spatial objects
point = Point(37.7749, -122.4194)
line = LineString([(0, 0), (1, 1), (2, 2)])
polygon = Polygon([
    (0, 0), (0, 1), (1, 1), (1, 0), (0, 0)
])

2.5.2.2. Coordinate Systems

Working with different coordinate systems:

from memories.spatial.crs import CoordinateSystem

# Create coordinate system
crs = CoordinateSystem(
    source="EPSG:4326",  # WGS84
    target="EPSG:3857"   # Web Mercator
)

# Transform coordinates
transformed = crs.transform(
    coordinates=[(lon1, lat1), (lon2, lat2)],
    source_crs="EPSG:4326",
    target_crs="EPSG:3857"
)

2.5.3. Spatial Operations

2.5.3.1. Geometric Operations

Basic geometric operations:

from memories.spatial.geometry import GeometryOperations

# Initialize geometry operations
geo_ops = GeometryOperations()

# Calculate area
area = geo_ops.calculate_area(polygon)

# Calculate distance
distance = geo_ops.calculate_distance(point1, point2)

# Check intersection
intersects = geo_ops.intersects(polygon1, polygon2)

# Create buffer
buffer = geo_ops.buffer(
    geometry=point,
    distance=1000,  # meters
    resolution=16
)

2.5.3.2. Spatial Analysis

Advanced spatial analysis:

from memories.spatial.analysis import SpatialAnalyzer

# Initialize analyzer
analyzer = SpatialAnalyzer(
    method="kriging",
    parameters={
        "variogram_model": "spherical",
        "nlags": 6,
        "weight": True
    }
)

# Perform spatial interpolation
result = await analyzer.interpolate(
    points=sample_points,
    values=measurements,
    grid_size=(100, 100),
    bounds=bounding_box
)

2.5.3.3. Clustering

Spatial clustering methods:

from memories.spatial.clustering import SpatialClusterer

# Initialize clusterer
clusterer = SpatialClusterer(
    method="dbscan",
    parameters={
        "eps": 0.5,
        "min_samples": 5,
        "metric": "haversine"
    }
)

# Perform clustering
clusters = clusterer.fit(
    points=locations,
    weights=importance_scores
)

2.5.3.4. Pattern Analysis

Analyzing spatial patterns:

from memories.spatial.patterns import PatternAnalyzer

# Initialize pattern analyzer
pattern = PatternAnalyzer(
    methods=["moran_i", "getis_ord", "ripley_k"],
    significance_level=0.05
)

# Analyze patterns
results = pattern.analyze(
    points=locations,
    values=measurements,
    bounds=study_area
)

2.5.4. Visualization

Creating spatial visualizations:

from memories.spatial.visualization import SpatialPlotter

# Initialize plotter
plotter = SpatialPlotter(
    backend="folium",
    style={
        "tiles": "CartoDB positron",
        "width": "100%",
        "height": "600px"
    }
)

# Create interactive map
map_view = plotter.create_map(
    center=[37.7749, -122.4194],
    zoom=12
)

# Add layers
plotter.add_heatmap(
    points=locations,
    intensities=values,
    radius=25
)

plotter.add_choropleth(
    polygons=regions,
    values=region_data,
    color_scale="YlOrRd"
)

2.5.5. Advanced Topics

Spatial Statistics - Global statistics - Local statistics - Space-time statistics - Geostatistics
Network Analysis - Routing - Network topology - Flow analysis - Accessibility
Terrain Analysis - Elevation analysis - Slope and aspect - Viewshed analysis - Watershed delineation
Remote Sensing - Image processing - Feature extraction - Change detection - Classification

2.5.6. Best Practices

Data Quality - Validate geometries - Check coordinate systems - Handle edge cases - Clean input data
Performance - Use spatial indexing - Optimize queries - Cache results - Parallel processing
Accuracy - Validate results - Consider uncertainty - Use appropriate methods - Document assumptions
Visualization - Choose appropriate projections - Use clear symbology - Add context information - Consider user interaction