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GeoJitter: a flexible toolkit for region-aware node jittering in spatial networks.

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Titel: GeoJitter: a flexible toolkit for region-aware node jittering in spatial networks.
Autoren: Fiore, Nina, Neumann, Sebastian, Moores, Geoffrey
Quelle: Discover Networks; 1/28/2026, Vol. 2 Issue 1, p1-17, 17p
Schlagwörter: PYTHON programming language, GEOGRAPHIC information systems, DATA privacy, DATA analysis, VISUALIZATION
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. [ABSTRACT FROM AUTHOR]
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Beschreibung
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. [ABSTRACT FROM AUTHOR]
ISSN:30049792
DOI:10.1007/s44354-025-00014-x