Cross-Language Classification of Open-Source Geospatial Libraries for Natural Resource Management: A Systematic Review and Framework
TL;DR
The reviewed literature indicates strong representation of Python in integrative analytical workflows, the foundational role of C and C++ libraries in computational geometry and data processing, the importance of JVM-based frameworks for distributed geospatial analytics, and the emerging applicability of Rust and Julia in high-performance spatial computing contexts.
Abstract
Geographical Information Systems (GIS) have traditionally been implemented through desktop environments such as QGIS. The increasing availability of open-source geospatial libraries across multiple programming languages enables spatial data analysis through programmatic workflows. This study systematically examines twelve programming-language environments (C/C++, Python, R, Julia, Java, Scala, JavaScript, TypeScript, Go, Rust, Kotlin and Swift), together with SQL/Spatial SQL (treated here as a query and data-management paradigm), and their associated open-source geospatial libraries. Functionality, interoperability, and ecosystem maturity are evaluated through a structured synthesis of the literature. After systematically screening 1,245 records, 370 sources met the inclusion criteria and were included in the analysis. Of these, 210 focused on Python/R, 85 on C/C++/Java, 45 on JavaScript/TypeScript, and 30 on emerging languages (Rust, Julia, Go). The Language-Oriented Geospatial Libraries Ecosystem (LOGLE) framework was proposed as a structured, language-oriented classification that organises libraries according to programming language and primary GIS function. Identified functional categories include analytical processing, core computational support, distributed geospatial processing, statistical modelling, web-based visualisation, performance optimisation, scientific computing, infrastructure services and mobile GIS applications. The reviewed literature indicates strong representation of Python in integrative analytical workflows, the foundational role of C and C++ libraries in computational geometry and data processing, the importance of JVM-based frameworks for distributed geospatial analytics, and the emerging applicability of Rust and Julia in high-performance spatial computing contexts. The LOGLE framework organises libraries by programming language, functional role, and system layer. Building on existing single-language surveys and OSGeo ecosystem reviews, it consolidates fragmented documentation into a coherent classification. The framework is conceptually scoped and is intended to support future empirical validation and benchmarking studies.