{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T13:07:34Z","timestamp":1786972054340,"version":"build-2736575974"},"reference-count":24,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,1,23]],"date-time":"2017-01-23T00:00:00Z","timestamp":1485129600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chinese Natural Science Foundation Project","award":["41271449"],"award-info":[{"award-number":["41271449"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>In the fields of geographic information systems (GIS) and remote sensing (RS), the clustering algorithm has been widely used for image segmentation, pattern recognition, and cartographic generalization. Although clustering analysis plays a key role in geospatial modelling, traditional clustering methods are limited due to computational complexity, noise resistant ability and robustness. Furthermore, traditional methods are more focused on the adjacent spatial context, which makes it hard for the clustering methods to be applied to multi-density discrete objects. In this paper, a new method, cell-dividing hierarchical clustering (CDHC), is proposed based on convex hull retraction. The main steps are as follows. First, a convex hull structure is constructed to describe the global spatial context of geospatial objects. Then, the retracting structure of each borderline is established in sequence by setting the initial parameter. The objects are split into two clusters (i.e., \u201csub-clusters\u201d) if the retracting structure intersects with the borderlines. Finally, clusters are repeatedly split and the initial parameter is updated until the terminate condition is satisfied. The experimental results show that CDHC separates the multi-density objects from noise sufficiently and also reduces complexity compared to the traditional agglomerative hierarchical clustering algorithm.<\/jats:p>","DOI":"10.3390\/ijgi6010030","type":"journal-article","created":{"date-parts":[[2017,1,23]],"date-time":"2017-01-23T10:40:33Z","timestamp":1485168033000},"page":"30","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Novel Divisive Hierarchical Clustering Algorithm for Geospatial Analysis"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7002-9480","authenticated-orcid":false,"given":"Shaoning","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Resources and Environment Engineering, Wuhan University of Science and Technology, Wuhan 430081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2127","DOI":"10.1002\/ijc.29955","article-title":"Space-time clustering of childhood cancers in Switzerland: A nationwide study","volume":"138","author":"Kreis","year":"2016","journal-title":"Int. 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