Spatiotemporal module ===================== .. automodule:: environmentaltools.spatiotemporal :no-index: This module provides comprehensive tools for spatiotemporal analysis of environmental data, with specialized functions for: * **Bayesian Maximum Entropy (BME)** estimation and uncertainty quantification * **Spatiotemporal covariance** modeling and fitting * **Threshold-based indicators** for risk and impact assessment * **Multi-criteria decision analysis** for spatial prioritization * **Raster analysis** for binary matrix generation and preprocessing Bayesian Maximum Entropy ------------------------- The BME framework provides optimal spatiotemporal estimation by combining prior knowledge with observational data (both exact and probabilistic). It's particularly useful for environmental applications where data uncertainty must be quantified. BME Estimation Functions ~~~~~~~~~~~~~~~~~~~~~~~~~ Core functions for performing BME spatiotemporal estimation. .. currentmodule:: environmentaltools.spatiotemporal.bme .. autosummary:: :toctree: _autosummary compute_bme_moments estimate_local_mean_bme perform_cross_validation Support Functions ^^^^^^^^^^^^^^^^^ .. autosummary:: :toctree: _autosummary calculate_moments integrate_moment_vector apply_data_smoothing Spatiotemporal Covariance ~~~~~~~~~~~~~~~~~~~~~~~~~~ Functions for computing empirical covariances and fitting theoretical spatiotemporal covariance models (exponential, non-separable, directional). .. currentmodule:: environmentaltools.spatiotemporal.covariance .. autosummary:: :toctree: _autosummary compute_spatiotemporal_covariance fit_covariance_model Advanced Covariance Functions ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. autosummary:: :toctree: _autosummary compute_directional_covariance calculate_theoretical_covariance Utility Functions ~~~~~~~~~~~~~~~~~ Helper functions for data preparation, neighborhood selection, and coordinate transformations. .. currentmodule:: environmentaltools.spatiotemporal.bme .. autosummary:: :toctree: _autosummary select_neighbours estimate_bme_regression create_design_matrix smooth_data create_spatiotemporal_matrix coordinates_to_distance coordinates_to_distance_angle find_pairs_by_distance Threshold-Based Indicators --------------------------- Functions for computing spatial indicators based on threshold exceedances. These indicators are useful for flood risk assessment, pollution exposure analysis, and environmental impact studies. **Key Indicators:** * **RAEH** - Ratio of Area Exceeding thresHold: Fraction of spatial domain exceeding threshold * **MEW** - Mean Exceedance over Whole domain: Mean exceedance normalized by total area * **MEDW** - Mean Excess Difference over Whole domain: Mean excess above threshold over total area * **WMEW** - Weighted Mean Exceedance over exceedance area: Conditional mean given exceedance * **WMDW** - Weighted Mean excess Difference: Conditional mean excess given exceedance * **AEAN** - Area Exceeding to Area Non-exceeding: Ratio of exceedance to non-exceedance areas .. currentmodule:: environmentaltools.spatiotemporal.indicators Basic Indicators ~~~~~~~~~~~~~~~~ .. autosummary:: :toctree: _autosummary fractional_exceedance_area mean_exceedance_over_total_area mean_excess_over_total_area mean_exceedance_over_exceedance_area mean_excess_over_exceedance_area exceedance_to_nonexceedance_ratio Spatiotemporal Extent Indicators ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autosummary:: :toctree: _autosummary mean_presence_boundary maximum_influence_extent threshold_exceedance_frequency permanently_affected_zone mean_representative_value Extreme Value and Risk Indicators ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autosummary:: :toctree: _autosummary return_period_extreme_value environmental_risk functional_area_loss critical_boundary_retreat Spatial Dynamics Indicators ~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. autosummary:: :toctree: _autosummary spatial_change_rate directional_influence environmental_convergence Neighborhood Analysis ~~~~~~~~~~~~~~~~~~~~~ .. autosummary:: :toctree: _autosummary neighbourhood_mean neighbourhood_gradient_influence neighbourhood_polarization local_persistence Multivariate Analysis ~~~~~~~~~~~~~~~~~~~~~ .. autosummary:: :toctree: _autosummary multivariate_neighbourhood_synergy spatiotemporal_coupling multivariate_threshold_exceedance directional_coevolution multivariate_persistence Multi-Criteria Decision Analysis --------------------------------- TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method for spatial prioritization. Useful for site selection, restoration planning, and resource allocation based on multiple environmental criteria. **Features:** * Multiple weighting schemes (equal, entropy, analytical hierarchy process) * Comprehensive visualization (ranking maps, isolines, bar charts) * Sensitivity analysis across weighting methods * Statistical summaries and publication-ready outputs .. currentmodule:: environmentaltools.spatiotemporal.multicriteria .. autosummary:: :toctree: _autosummary run_topsis_mcda create_weights_visualization create_topsis_maps Raster Analysis --------------- Functions for processing spatiotemporal raster data, including configuration management, binary matrix generation for threshold exceedances, input validation, and NetCDF output creation. **Capabilities:** * Configuration file validation and loading * Temporal difference analysis for change detection * Input validation and preprocessing * Post-treatment data preparation and refinement * Binary matrix generation for threshold analysis * Temporal aggregation (annual, seasonal) * NetCDF format output with metadata .. currentmodule:: environmentaltools.spatiotemporal.raster .. autosummary:: :toctree: _autosummary largest_region_from_field preprocess check_inputs compute_contours analysis obtain_geometry save_layer save_contours save_arrays_to_netcdf load_results