Isatis.neo is a geostatistical modeling and spatial data-analysis software application used to characterize spatial variability, generate quantitative estimates, evaluate uncertainty, and support decision-making in resource and environmental contexts. It helps users analyze spatial datasets, estimate variables of interest, simulate spatial phenomena, and quantify associated uncertainty using deterministic and stochastic methods grounded in geostatistical theory. The software is used in fields where decisions depend on understanding how natural or engineered variables vary in space. Common applications include mineral resource estimation, geometallurgical domaining, environmental site characterization, groundwater assessment, soil contamination mapping, subsurface property modeling, renewable energy site assessment, and geological CO₂ storage evaluation. Users import spatial data, examine its distribution and structure, compute variograms, apply kriging or simulation algorithms, and produce 2D/3D spatial representations. Isatis.neo enables the integration of multiple data sources, such as drillhole samples, geophysical measurements, field observations, and raster datasets, into consistent spatial models. Users can incorporate geological or environmental constraints, evaluate sampling strategies, generate multiple realizations to assess uncertainty, and quantify risk associated with spatial estimates. Python integration and workflow automation enable the software to be tailored to organizational procedures and integrated with broader modeling or data-processing frameworks. Industries using Isatis.neo include mining and exploration, environmental consulting, energy (oil and gas, geothermal, and renewables), civil engineering, agriculture and soil science, hydrogeology, and government agencies engaged in natural resource evaluation and environmental regulation. In research settings, the software is used in geosciences, ecology, hydrology, and spatial statistics for quantitative analysis of spatial datasets. Key features: - Comprehensive geostatistical toolbox: Includes methods for exploratory spatial data analysis, variogram modeling, kriging (univariate and multivariate), conditional simulations, trend modeling, multivariate correlations, and uncertainty quantification. - Workflow management and automation: Provides a structured environment for building reproducible modeling sequences, automating repetitive tasks, and standardizing procedures across teams or projects through batch scripting and configurable workflows. - Spatial data visualization and model inspection: Supports 2D/3D visualization of raw data, structural inputs, estimation results, and simulation outputs, allowing users to examine spatial patterns, evaluate model behavior, and compare alternative scenarios. - Python extensibility: Allows users to write custom functions, create derived variables, call external Python libraries, and integrate Isatis.neo into larger computational or data-science pipelines. - Cross-industry applicability: Utilized in mining for grade estimation and resource classification; in environmental studies for plume delineation and remediation planning; in hydrogeology for aquifer property modeling; in energy for subsurface characterization; and in agriculture for soil property mapping.