Abstract GeoJitter is an open-source Python package for region-aware randomization of node locations in spatial networks, designed to preserve network structure while mitigating privacy risks and supporting spatial visualization of incomplete or uncertain data. Existing open-source randomization techniques rarely incorporate geospatial boundaries, limiting their applicability for spatial analysis; GeoJitter addresses this gap and provides tiling solutions when region data is unavailable. Performance was compared against Radius and K-Nearest Neighbor randomization across 100 trials on Brightkite and Gowalla networks; structural metrics remained stable with the most disparity in average path length, which changed by 0.14–0.28 (versus 0.06–0.09 for benchmarks). Changes in betweenness were near zero (− 0.0001 overall), clustering coefficient shifted by approximately 0.009, and modularity by − 0.015. These results demonstrate comparability to established methods while introducing a flexible, region-aware approach for privacy-sensitive spatial analysis.
Fiore et al. (Wed,) studied this question.