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Spatial statistics, topology, and graph theory for geospatial analysis. Use when computing Moran's I, spatial autocorrelation, geodesic distances, graph connectivity, kernel density estimation, or any mathematical operation on geographic data.
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基于 SOC 职业分类
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| name | geo-infer-math |
| description | Spatial statistics, topology, and graph theory for geospatial analysis. Use when computing Moran's I, spatial autocorrelation, geodesic distances, graph connectivity, kernel density estimation, or any mathematical operation on geographic data. |
| prerequisites | {"required":[],"recommended":[]} |
| difficulty | beginner |
| estimated_time | 30min |
| examples_dir | ../GEO-INFER-EXAMPLES/examples/ |
Foundation module with zero internal dependencies. Provides mathematical primitives consumed by all other modules.
# Spatial statistics
from geo_infer_math.core.spatial_statistics import (
MoranI, SpatialDescriptiveStats,
getis_ord_g, ripley_k, semivariogram,
spatial_descriptive_statistics, spatial_entropy,
local_indicators_spatial_association,
)
# Interpolation
from geo_infer_math.core.interpolation import (
InterpolationConfig, SpatialInterpolator,
IDWInterpolator, KrigingInterpolator, RBFInterpolator,
InterpolationManager, create_interpolation_manager,
interpolate_spatial_data, create_interpolation_grid,
)
# Optimization
from geo_infer_math.core.optimization import (
OptimizationConfig, Optimizer,
GradientDescentOptimizer, GeneticAlgorithmOptimizer,
ScipyOptimizer, MultiObjectiveOptimizer,
OptimizationManager, create_optimization_manager,
optimize_function, compare_optimization_methods,
)
# Geometry / distance
from geo_infer_math.core.geometry import (
Point, LineString, Polygon,
haversine_distance, vincenty_distance,
bearing, destination_point,
point_in_polygon, buffer_point,
line_intersection, polygon_area_spherical,
)
import numpy as np
from geo_infer_math.core.spatial_statistics import (
MoranI, getis_ord_g, ripley_k, semivariogram,
spatial_descriptive_statistics,
)
# Moran's I spatial autocorrelation
values = np.random.randn(100)
weights = np.random.rand(100, 100)
moran = MoranI(values, weights)
result = moran.compute()
print(f"Moran's I: {result.statistic}, p-value: {result.p_value}")
# Getis-Ord G* hot-spot statistic
g_stat = getis_ord_g(values, weights)
print(f"Getis-Ord G: {g_stat}")
# Ripley's K function
coords = np.random.rand(50, 2)
k_values = ripley_k(coords, distances=np.linspace(0.01, 1.0, 20))
print(f"Ripley K at first distance: {k_values[0]:.4f}")
# Semivariogram
semivar = semivariogram(values, coords)
print(f"Semivariogram computed: {len(semivar)} lag bins")
# Descriptive spatial statistics
stats = spatial_descriptive_statistics(values, coords)
print(stats)
uv run python -m pytest GEO-INFER-MATH/tests/ -v