Scholarlink

Efficient processing of large georeferenced datasets is essential for modern agricultural management. This study evaluates the performance of four spatial indexing methods—R-Tree, Quad-Tree, KD-Tree, and Grid—using 91,831 georeferenced records from Peru’s ENA 2024. Coordinates, productive variables, irrigation systems, and environmental stressors were integrated into a spatial database and analyzed in R using sf, terra, spatstat, and FNN. Grid achieved the best performance for range queries (3.66–3.70 ms, > 270 QPS), delivering 180–183× speedups over R-Tree with minimal memory usage (0.0013 MB). For KNN queries, Quad-Tree reached up to 105,000 QPS, while KD-Tree surpassed it only at k = 100. Statistical tests confirmed significant differences (Wilcoxon p = 0.0312; Kruskal–Wallis p = 0.0008). Regional analyses revealed strong agro-productive contrasts and demonstrated that Grid maintains ≤ 10 ms latency even in highly dispersed Amazonian areas. Overall, the Grid + Quad-Tree/KD-Tree combination provides a scalable, IoT-ready solution for real-time, nationwide agricultural monitoring and decision support.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Comparative Evaluation of Spatial Indexing Methods Applied to the Georeferenced Characterization of Agricultural Units and Productivity in Peru During the year 2024

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Ilma Magda Mamani Mamani

摘要

Efficient processing of large georeferenced datasets is essential for modern agricultural management. This study evaluates the performance of four spatial indexing methods—R-Tree, Quad-Tree, KD-Tree, and Grid—using 91,831 georeferenced records from Peru’s ENA 2024. Coordinates, productive variables, irrigation systems, and environmental stressors were integrated into a spatial database and analyzed in R using sf, terra, spatstat, and FNN. Grid achieved the best performance for range queries (3.66–3.70 ms, > 270 QPS), delivering 180–183× speedups over R-Tree with minimal memory usage (0.0013 MB). For KNN queries, Quad-Tree reached up to 105,000 QPS, while KD-Tree surpassed it only at k = 100. Statistical tests confirmed significant differences (Wilcoxon p = 0.0312; Kruskal–Wallis p = 0.0008). Regional analyses revealed strong agro-productive contrasts and demonstrated that Grid maintains ≤ 10 ms latency even in highly dispersed Amazonian areas. Overall, the Grid + Quad-Tree/KD-Tree combination provides a scalable, IoT-ready solution for real-time, nationwide agricultural monitoring and decision support.