{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:55:14Z","timestamp":1760237714306,"version":"build-2065373602"},"reference-count":59,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T00:00:00Z","timestamp":1591660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41631175"],"award-info":[{"award-number":["41631175"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFB0503500"],"award-info":[{"award-number":["2017YFB0503500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Unstructured geo-text annotations volunteered by users of web map services enrich the basic geographic data. However, irrelevant geo-texts can be added to the web map, and these geo-texts reduce utility to users. Therefore, this study proposes a method to detect uncorrelated geo-text annotations based on Voronoi k-order neighborhood partition and auto-correlation statistical models. On the basis of the geo-text classification and semantic vector transformation, a quantitative description method for spatial autocorrelation was established by the Voronoi weighting method of inverse vicinity distance. The Voronoi k-order neighborhood self-growth strategy was used to detect the minimum convergence neighborhood for spatial autocorrelation. The Pearson method was used to calculate the correlation degree of the geo-text in the convergence region and then deduce the type of geo-text to be filtered. Experimental results showed that for given geo-text types in the study region, the proposed method effectively calculated the correlation between new geo-texts and the convergence region, providing an effective suggestion for preventing uncorrelated geo-text from uploading to the web map environment.<\/jats:p>","DOI":"10.3390\/ijgi9060381","type":"journal-article","created":{"date-parts":[[2020,6,10]],"date-time":"2020-06-10T05:11:46Z","timestamp":1591765906000},"page":"381","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Uncorrelated Geo-Text Inhibition Method Based on Voronoi K-Order and Spatial Correlations in Web Maps"],"prefix":"10.3390","volume":"9","author":[{"given":"Yufeng","family":"He","sequence":"first","affiliation":[{"name":"School of Geography, Nanjing Normal University, Nanjing 210023, China"},{"name":"Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information, Resource Development and Application, Nanjing 210023, China"}]},{"given":"Yehua","family":"Sheng","sequence":"additional","affiliation":[{"name":"School of Geography, Nanjing Normal University, Nanjing 210023, China"},{"name":"Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information, Resource Development and Application, Nanjing 210023, China"}]},{"given":"Yunqing","family":"Jing","sequence":"additional","affiliation":[{"name":"School of Geography, Nanjing Normal University, Nanjing 210023, China"},{"name":"Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information, Resource Development and Application, Nanjing 210023, China"}]},{"given":"Yue","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Geography, Nanjing Normal University, Nanjing 210023, China"},{"name":"Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information, Resource Development and Application, Nanjing 210023, China"}]},{"given":"Ahmad","family":"Hasnain","sequence":"additional","affiliation":[{"name":"School of Geography, Nanjing Normal University, Nanjing 210023, China"},{"name":"Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information, Resource Development and Application, Nanjing 210023, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1109\/MCOM.2014.6917406","article-title":"The ParticipAct Mobile Crowd Sensing Living Lab: The Testbed for Smart Cities","volume":"52","author":"Cardone","year":"2014","journal-title":"IEEE Commun. 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