{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T17:51:02Z","timestamp":1782496262901,"version":"3.54.5"},"reference-count":38,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:00:00Z","timestamp":1780876800000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42201507"],"award-info":[{"award-number":["42201507"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42271426"],"award-info":[{"award-number":["42271426"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["20720261007"],"award-info":[{"award-number":["20720261007"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Transactions in GIS"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Understanding the inter\u2010day stability of urban mobility flows is critical for city planning. However, current literature often relies on binary backbone extraction that overlooks the full statistical distribution of recurrence and lacks quantitative rigor in explaining its environmental drivers. Based on taxi trajectory datasets from Wuhan, China, and New York City, United States, spanning approximately half a year, our analysis first reveals that flow stability follows a characteristic U\u2010shaped distribution, comprised of vast sporadic movements and a distinct, near\u2010daily persistent core. Second, we demonstrate that this polarized structure is systematically encoded in the urban built environment, as evidenced by the high predictive accuracy of our class\u2010balanced XGBoost classification model. Third, we use SHAP to quantify the underlying mechanisms, identifying spatial impedance as the primary filter that restricts high\u2010stability flows to short\u2010range and predominantly intra\u2010district connections. Furthermore, persistent flows are functionally anchored by the simultaneous presence of key facilities, such as healthcare and transportation hubs, at both the origin and destination. Ultimately, these results demonstrate that the built environment acts as a decisive filter that drives this polarized U\u2010shaped structure, strictly enforcing the formation of a rigid, near\u2010daily core, thereby providing a systematic basis for optimizing urban spatial structure and strategic infrastructure planning.<\/jats:p>","DOI":"10.1111\/tgis.70302","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T10:11:36Z","timestamp":1780913496000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["U\u2010Shaped Stability in Urban Mobility Flows: Revealing Environmental Drivers via Explainable Machine Learning in Wuhan and New York City"],"prefix":"10.1111","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3668-0496","authenticated-orcid":false,"given":"Bronte","family":"Scheuer","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences Peking University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng\u2010Bin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Natural Resources, Faculty of Geographical Science Beijing Normal University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lun","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences Peking University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3945-1621","authenticated-orcid":false,"given":"Xiao\u2010Jian","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Public Affairs Xiamen University  Xiamen China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John P.","family":"Wilson","sequence":"additional","affiliation":[{"name":"Spatial Sciences Institute, Dornsife College of Letters, Arts and Sciences University of Southern California  Los Angeles California USA"},{"name":"Department of Civil and Environmental Engineering, Viterbi School of Engineering University of Southern California  Los Angeles California USA"},{"name":"Department of Computer Science, Viterbi School of Engineering University of Southern California  Los Angeles California USA"},{"name":"Department of Population and Public Health Sciences, Keck School of Medicine University of Southern California  Los Angeles California USA"},{"name":"Department of Sociology, Dornsife College of Letters, Arts and Sciences University of Southern California  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,8]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2016.2616404"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physrep.2018.01.001"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physrep.2010.11.002"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtrangeo.2020.102892"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2117776119"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143865"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1080\/01441647.2023.2251688"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jtrangeo.2021.103093"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1080\/12265934.2020.1799846"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/tgis.12849"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cities.2024.105033"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.68.066102"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tra.2025.104715"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1080\/13658816.2017.1346256"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13278\u2010016\u20100369\u20102"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1080\/00343404.2024.2325612"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.239"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42379\u2010019\u201000032\u20107"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3116963"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2991982"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1111\/tgis.70138"},{"key":"e_1_2_10_23_1","first-page":"4765","article-title":"A Unified Approach to Interpreting Model Predictions","volume":"30","author":"Lundberg S. 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