From mapping to modeling: Spatial statistics in ethnobotanical research

Authors

  • Muh Akbar Idris IPB University
  • Ade Ariyo Yudanto IPB University
  • Mufrih Nur Huda Tri Putra Institut Teknologi Sepuluh Nopember
  • Reyuli Andespa IPB University
  • Nabila Syukri IPB University
  • Fadila Fadila Department of Mechanical and Biosystem Engineering, IPB University, Bogor, Indonesia.
  • Iin Safira IPB University
  • Zamrah Mutmainah IPB University
  • Unique Desyrre A. Resiloy IPB University

Abstract

Background: Ethnobotany is a critical repository of biocultural knowledge, essential for understanding human-environment interactions. While traditional ecological knowledge (TEK) increasingly guides advanced scientific domains, like providing blueprints for sustainable nano-materials, its integration into spatial and statistical analytics remains fragmented. Historically

reliant on qualitative ethnography, this methodological gap limits the discipline’s capacity to support predictive conservation, spatial decision-making, and policy design under accelerating environmental change.

Methods: A systematic literature review was conducted using the Scopus database, strictly following the PRISMA protocol. A screening of 336 initial records evaluated peer-reviewed articles at the intersection of indigenous knowledge, geographic information systems (GIS), and spatial statistics. Data were extracted to identify publication trends, methodological classifications, and research gaps.

Results: The screening yielded 101 eligible articles. The synthesis reveals a clear dominance of GIS applications focused on descriptive cartography (93 papers). Conversely, advanced spatial statistical tools, such as Geographically Weighted Regression (GWR), geographic detectors, and predictive modeling, are scarce and were successfully applied in only 8 studies. This exposes an analytical deficit and a lack of predictive socio-spatial modeling in current literature.

Conclusions: This study bridges ethnobotanical knowledge systems with spatial analytical frameworks. Drawing parallels from successful adoptions in material sciences and urban sustainability, we propose a transition from descriptive mapping to inferential analytics. A future research agenda leveraging Geospatial Artificial Intelligence (GeoAI) is outlined to improve the statistical rigor of ethnobotany and provide data-driven support for biocultural conservation.

Keywords: Ethnobotany; Indigenous Knowledge; Spatial Analysis; Spatial Statistics; Systematic Literature Review; GeoAI

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Published

2026-05-24

How to Cite

Idris, M. A., Yudanto, A. A., Putra, M. N. H. T., Andespa, R., Syukri, N., Fadila, F., Safira, I., Mutmainah, Z., & Resiloy, U. D. A. (2026). From mapping to modeling: Spatial statistics in ethnobotanical research. Ethnobotany Research and Applications, 34, 1–10. Retrieved from https://www.ethnobotanyjournal.org/index.php/era/article/view/8467

Issue

Vol. 34 (2026)

Section

Research

License

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