STRUCTURAL, ANALYTICAL, AND CONTEXTUAL ROLES OF MATHEMATICS IN LAND SCIENCE: A SYSTEMATIC REVIEW

Abstract

Land administration increasingly relies on mathematical, statistical, geometric, and computational methods, yet the literature treats these approaches in isolation and rarely examines their roles or the conditions under which they work. This study aims to map how mathematical approaches are used and discussed across land science and to identify the conditions that shape their effectiveness, with a focus on Indonesia. A systematic literature review followed PRISMA 2020. We searched Scopus, Web of Science, Google Scholar, and the SINTA and Garuda portals in January–February 2026, retrieving 784 records. After deduplication, independent double screening (Cohen’s κ = 0.81), full-text assessment, and quality appraisal with an adapted CASP checklist, 15 peer-reviewed studies published in 2009–2025 were selected by criterion-based purposive sampling and analyzed through thematic synthesis. Mathematics served structural (formal data modeling, n = 4), analytical (measurement, valuation, and survey analysis, n = 5), and contextual (evidence on institutional, regulatory, and adoption conditions, n = 6) roles. Quantified gains were reported mainly in valuation, for example, a root mean square error of 0.526 for an AHP-based model against 1.953 for multiple regression and time and resource savings of up to 80% for a neural-network model, whereas evidence on tenure and institutional outcomes was uneven. The review contributes a role-based framework and a conditional-effectiveness reading of the evidence. It implies that Indonesian cadastral digitalization should prioritize data quality, model interpretability, and practitioner competence.

Keywords

• Cadastre
• Land Administration
• Land Valuation
• Mathematical Modelling
• Systematic Literature Review