Crate oxigdal_analytics 

OxiGDAL Analytics - Advanced Geospatial Analytics

This crate provides advanced analytics capabilities for geospatial data processing, including time series analysis, spatial clustering, hotspot detection, change detection, interpolation, and advanced zonal statistics.

Features

Time Series Analysis

Analyze temporal patterns in geospatial data:

• Trend detection (Mann-Kendall test, linear regression)
• Anomaly detection (Z-score, IQR, Modified Z-score)
• Seasonal decomposition
• Gap filling and smoothing

use oxigdal_analytics::timeseries::{TrendDetector, TrendMethod};
use scirs2_core::ndarray::array;

let values = array![1.0, 2.0, 3.0, 4.0, 5.0];
let detector = TrendDetector::new(TrendMethod::MannKendall, 0.05);
let result = detector.detect(&values.view()).expect("Failed to detect trend");

assert_eq!(result.direction, 1); // Positive trend
assert!(result.significant);

Spatial Clustering

Identify clusters and outliers in spatial data:

• K-means clustering for image classification
• DBSCAN for spatial outlier detection
• Cluster validation metrics

use oxigdal_analytics::clustering::{KMeansClusterer, DbscanClusterer};
use scirs2_core::ndarray::array;

let data = array![
    [0.0, 0.0],
    [0.1, 0.1],
    [10.0, 10.0],
    [10.1, 10.1],
];

// K-means clustering
let kmeans = KMeansClusterer::new(2, 100, 1e-4);
let result = kmeans.fit(&data.view()).expect("Failed to fit K-means clustering");
assert_eq!(result.centers.nrows(), 2);

Hotspot Analysis

Detect spatial clusters of high or low values:

• Getis-Ord Gi* statistic (hot spot analysis)
• Moran’s I (global and local spatial autocorrelation)
• LISA (Local Indicators of Spatial Association)

use oxigdal_analytics::hotspot::{GetisOrdGiStar, SpatialWeights};
use scirs2_core::ndarray::array;

let values = array![1.0, 1.0, 10.0, 10.0];
let adj = array![
    [1.0, 1.0, 0.0, 0.0],
    [1.0, 1.0, 1.0, 0.0],
    [0.0, 1.0, 1.0, 1.0],
    [0.0, 0.0, 1.0, 1.0],
];

let weights = SpatialWeights::from_adjacency(adj).expect("Failed to create spatial weights from adjacency matrix");
let gi_star = GetisOrdGiStar::new(0.05);
let result = gi_star.calculate(&values.view(), &weights).expect("Failed to calculate Getis-Ord Gi* statistic");

Change Detection

Detect changes between multi-temporal images:

• Image differencing
• Change Vector Analysis (CVA)
• Principal Component Analysis (PCA)
• Automatic threshold optimization (Otsu’s method)

use oxigdal_analytics::change::{ChangeDetector, ChangeMethod};
use scirs2_core::ndarray::Array;

let before = Array::from_shape_vec((2, 2, 1), vec![1.0, 2.0, 3.0, 4.0]).expect("Failed to create before array");
let after = Array::from_shape_vec((2, 2, 1), vec![2.0, 3.0, 4.0, 5.0]).expect("Failed to create after array");

let detector = ChangeDetector::new(ChangeMethod::CVA);
let result = detector.detect(&before.view(), &after.view()).expect("Failed to detect changes");

assert_eq!(result.magnitude.dim(), (2, 2));

Interpolation

Create continuous surfaces from point data:

• Inverse Distance Weighting (IDW)
• Kriging (Ordinary and Universal)
• Variogram modeling
• Cross-validation

use oxigdal_analytics::interpolation::{IdwInterpolator, KrigingInterpolator, KrigingType, Variogram, VariogramModel};
use scirs2_core::ndarray::array;

let points = array![[0.0, 0.0], [1.0, 0.0], [0.0, 1.0]];
let values = array![1.0, 2.0, 3.0];
let targets = array![[0.5, 0.5]];

// IDW interpolation
let idw = IdwInterpolator::new(2.0);
let result = idw.interpolate(&points, &values.view(), &targets).expect("Failed to perform IDW interpolation");

Advanced Zonal Statistics

Calculate statistics for regions:

• Multiple statistics (mean, median, min, max, std, etc.)
• Weighted statistics
• Multi-band support
• Custom aggregation functions

use oxigdal_analytics::zonal::{ZonalCalculator, ZonalStatistic};
use scirs2_core::ndarray::array;

let values = array![[1.0, 2.0], [3.0, 4.0]];
let zones = array![[1, 1], [1, 1]];

let calculator = ZonalCalculator::new();
let result = calculator.calculate(&values.view(), &zones.view()).expect("Failed to calculate zonal statistics");

let zone1_stats = &result.zones[&1];
assert!(zone1_stats.contains_key(&ZonalStatistic::Mean));

COOLJAPAN Policy Compliance

This crate adheres to COOLJAPAN ecosystem policies:

• Pure Rust: 100% Pure Rust implementation, no C/Fortran dependencies
• No unwrap(): All error cases are properly handled with Result<T, E>
• SciRS2 Integration: Uses scirs2-core for scientific computing instead of ndarray directly
• Comprehensive Error Handling: Custom error types for all failure modes
• File Size Policy: All source files are kept under 2000 lines
• Latest Crates: Uses latest versions from crates.io
• Workspace Policy: Follows workspace dependency management

Performance

All algorithms are optimized for production use with:

• Efficient memory usage
• Vectorized operations where possible
• Optional parallel processing (enable parallel feature)
• Numerical stability checks

Safety

This crate is designed with safety in mind:

• No unsafe code (except what’s in dependencies)
• Comprehensive input validation
• Proper handling of edge cases (empty data, singular matrices, etc.)
• Clear error messages for debugging

Re-exports

pub use error::AnalyticsError;

pub use error::Result;

Modules

change

Change Detection Module

clustering

Spatial Clustering Module

error

Error types for OxiGDAL Analytics

hotspot

Hotspot Analysis Module

interpolation

Spatial Interpolation Module

timeseries

Time Series Analysis Module

zonal

Advanced Zonal Statistics Module

Constants

NAME

Crate name

VERSION

Crate version