🤖 AI Summary
To address resource misallocation arising from Euclidean-distance-based Voronoi service area partitioning in complex terrain, this paper proposes the first probabilistic misallocation risk assessment framework. The method models the ratio of travel distance to Euclidean distance using a lognormal distribution, integrates local Voronoi geometric structure to derive misallocation probability, and incorporates spatial stratification and sensitivity analysis to capture spatial heterogeneity—requiring only 30–100 samples for rapid calibration. Empirical evaluation in Extremadura, Spain, identifies misallocation in 15.4% of municipalities, with theoretical prediction intervals closely matching observed outcomes. The algorithm exhibits O(n) time complexity and achieves 95% consistency with high-fidelity spatial models. This work establishes the first formal quantification and context-adaptive evaluation of Voronoi-based service allocation risk.
📝 Abstract
Voronoi tessellations are standard in spatial planning for assigning service areas based on Euclidean proximity, underpinning regulatory frameworks like the proximity principle in waste management. However, in regions with complex topography, Euclidean distance poorly approximates functional accessibility, causing misallocations that undermine efficiency and equity. This paper develops a probabilistic framework to quantify misallocation risk by modeling travel distances as random scaling of Euclidean distances and deriving incorrect assignment probability as a function of local Voronoi geometry. Using plant-municipality observations (n=383) in Extremadura, Spain (41,635 km2), we demonstrate that the Log-Normal distribution provides best relative fit among alternatives (K-S statistic=0.110). Validation reveals 15.4% of municipalities are misallocated, consistent with the theoretical prediction interval (52-65 municipalities at 95% confidence). Our framework achieves 95% agreement with complex spatial models at O(n) complexity. Poor absolute fit of global distributions (p-values<0.01) reflects diverse topography (elevation 200-2,400m), motivating spatial stratification. Sensitivity analysis validates the fitted dispersion parameter (s=0.093) for predicting observed misallocation. We provide a calibration protocol requiring only 30-100 pilot samples per zone, enabling rapid risk assessment without full network analysis. This establishes the first probabilistic framework for Voronoi misallocation risk with practical guidelines emphasizing spatial heterogeneity and context-dependent calibration.