Front. Environ. Sci. Frontiers in Environmental Science Front. Environ. Sci. 2296-665X Frontiers Media S.A. 1797731 10.3389/fenvs.2026.1797731 Correction Correction: Revealing urban-rural sustainability dynamics based on multi-source data and machine learning: spatio-temporal hotspots, driving mechanisms, and non-linear thresholds in Suzhou, China Gan et al. 10.3389/fenvs.2026.1797731 Gan Wenquan 1 † Conceptualization Data curation Formal Analysis Funding acquisition Investigation Methodology Project administration Resources Software Supervision Validation Visualization Writing - original draft Writing - review and editing Wang Haoqi 2 † Data curation Formal Analysis Investigation Methodology Resources Software Validation Visualization Writing - original draft Writing - review and editing Li Pengcheng 3 Data curation Formal Analysis Investigation Methodology Resources Software Validation Visualization Writing - original draft Writing - review and editing Liu Jiana 4 * Conceptualization Funding acquisition Methodology Project administration Resources Supervision Validation Writing - original draft Writing - review and editing Chen Jinliu 4 Conceptualization Methodology Project administration Writing - original draft Writing - review and editing Department of Environmental Design, School of Art and Design, Zhejiang Sci-Tech University, Hangzhou, China Faculty of Architecture, The University of Hong Kong, Hong Kong SAR, China School of Architecture and Urban Planning, Suzhou University of Science and Technology, Suzhou, China School of Art and Design, Suzhou City University, Suzhou, China Correspondence: Jiana Liu, liujiana@szcu.edu.cn

These authors have contributed equally to this work

 16 02 2026 2026 14 1797731 28 01 2026 09 02 2026 Copyright © 2026 Gan, Wang, Li, Liu and Chen. 2026 Gan, Wang, Li, Liu and Chen https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. A Correction on Revealing urban–rural sustainability dynamics based on multi-source data and machine learning: spatio-temporal hotspots, driving mechanisms, and non-linear thresholds in Suzhou, China by Gan W, Wang H, Li P, Liu J and Chen J (2026). Front. Environ. Sci. 13:1712246. doi: 10.3389/fenvs.2025.1712246 land-use change non-linear threshold and interaction effects sustainable evaluation urban-rural sustainable index XGBoost-SHAP approach section-at-acceptance Land Use Dynamics

There was a mistake in Figures 1–4 as published. In the published article, Figures 1–4 omitted the administrative boundary of Suzhou Industrial Park (SIP). Additionally, the former “Huqiu District” should be renamed “Suzhou New District” in accordance with Suzhou’s latest administrative division.

Research framework.

Flowchart visualizing Urban-Rural Sustainable Index (URSI) Assessment process with economic, social, and ecological dimensions, multi-year spatial indicator maps, conflict analysis, predictive modeling, and outcome strategies for sustainable development and governance within metropolitan areas.

Boundaries and administrative divisions of the study area, Suzhou, China.

Map of Suzhou and surrounding regions displays county districts, built areas in pink, forest land in dark green, and farm land and parks in light green. District and city boundaries, road networks, and neighboring cities such as Wuxi, Nantong, and Shanghai are labeled. An inset map in the upper left shows Suzhou’s location within China for geographic reference.

Spatio-temporal distribution of Human Activity and Ecological Sensitivity indicators in Suzhou (2012-2024).

Grid of twenty-five thematic maps shows spatial distribution changes from 2012 to 2024 for Tianjin, China, across five metrics: economic facility, economic activity, social facility, ecological facility, and ecological quality. Each row represents one metric; each column is a year. Distinct color gradients indicate quantitative changes over time for each category.

Spatial distribution and temporal evolution of URSI-based conflict in Suzhou.

Series of five colored maps of the same region for the years 2012, 2015, 2018, 2021, and 2024, displaying a gradient legend from red to blue indicating numerical ranges, with visible transition from predominantly red areas to increased blue and yellow by 2024.

The corrected Figures 1–4 appear below.

The original article has been updated.

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Edited and reviewed by: Riccardo Buccolieri, University of Salento, Italy