Review of Spatial Interpolation Methods for Environmental Data

A 2026 review from the Australian Ocean Data Network summarizes 51 comparative studies on spatial interpolation methods. It compares the performance of 62 methods and sub-methods, quantifying impacts using data from 77 cases and classifying 26 methods based on their features. The document provides a decision tree for method selection and lists relevant software packages.

Use Cases

• Selecting an interpolation method based on data availability and nature using the provided decision tree.
• Comparing the performance of non-geostatistical, geostatistical, and combined interpolation methods for environmental variables.
• Assessing the impact of factors like sample density and data variation on interpolation estimates using the quantified results from 77 cases.
• Understanding relationships and similarities among 26 classified interpolation methods through the conducted cluster analysis.
• Identifying software tools for implementing spatial interpolation methods from the provided list.

Strengths

• Summarizes findings from 51 comparative studies on method performance.
• Compares 62 methods and sub-methods and quantifies factor impacts using data from 77 cases.
• Provides a structured classification and decision tree for 26 methods based on quantified features.

Limitations

• The dataset is a review document (PDF/HTML); the underlying raw data from the 77 cases is not directly provided.
• Column-level documentation for any underlying data is absent; field semantics must be inferred from the text.
• Row count for any underlying data is unknown, which may limit suitability assessment for direct ML use.

Provenance

Source

Australian Ocean Data Network via data_gov_au

Collection Method

Literature review and analysis of existing comparative studies.

Time Range

Publication date is 2026; temporal coverage of reviewed studies is unspecified.

Freshness

Last updated 2026-04-10 20:21:27.415598; freshness should be verified.

Geography

Global, as the review encompasses methods applicable across various disciplines and locations.