Identifying Hotspots and Classifying the spatial Distribution Pattern of KarstCollapse Pillars with Moran's Index in Coal Mine

Identifying Hotspots and Classifying the spatial Distribution Pattern of KarstCollapse Pillars with Moran's Index in Coal Mine | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identifying Hotspots and Classifying the spatial Distribution Pattern of KarstCollapse Pillars with Moran's Index in Coal Mine Junsheng Yan, Zaibin Liu, Hui Yang, Wei Li, Tiantian Wang, Qian Xie, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5862516/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Studying the identification of hazardous karst collapse pillars(KCPs) is vital for ensuring the safe mining of coal resources. However, current study on identifying KCPs primarily emphasizes physical detection, overlooking the spatial aggregation patterns. In this study, we proposed a hotspot identification method for KCPs using Moran's index. and carry out experiments in Wangpo Coal Mine of Shanxi, China. The method involves evaluating the KCPs by considering their morphological characteristics and using a combination of the Analytic Hierarchy Process (AHP) and Entropy Weighting Method (EWM) for quantitative assessment. Then the spatial distribution index of the KCPs(SDI) is determined through Geographic Information System(GIS) overlay analysis and coordinate calibration. The hotspots analysis resulted in a global Moran's index value of 0.1110, indicating a positive spatial correlation of the SDI in the study area. Local Moran's index is further used to identify hotspots of KCPs. A total of 11 special KCPs were identified within the study area, including 5 high-high cluster KCPs. Finally, we analysed the geological impact of fault and fold distributions on KCPs development in these high-risk areas. The results reveal that the development patterns of KCPs at these specific points are analyzed in relation to the distribution of geological structures, especially at the intersections of faults and folds, where high-risk KCPs are most likely to develop. Morphological characteristics Overlay analysis Coordinate calibration Spatial distribution index Development patterns Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5862516","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":405614719,"identity":"d116743b-356f-4064-b1c7-5c46e08bcc02","order_by":0,"name":"Junsheng Yan","email":"","orcid":"","institution":"China Coal Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Junsheng","middleName":"","lastName":"Yan","suffix":""},{"id":405614720,"identity":"e0dea2ac-d2c0-4fdc-9df3-c9048b490d82","order_by":1,"name":"Zaibin Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYBACPmYQaXCAh4G9ASLCRkgLG1wLzwEGhgNEaYFQQLUSCRAtBAEbO4/xhx8Fd2TMJR8//Pyhojaxj/3sAYYfFdvwOIzHwLDH4BmP5ew0Y4kDZ44bs/HkJTD2nLmNV0sCj8FhHoPbOWwMB9uOybEx5BgwM7bh13LwD0jLzTNALf+O8bDxvyGoxbAZbMsNHqCWhho5NgmCtrAVM8sA/WJwBuiXM8cOGLNJvDE4iM8v/PyHN3988+eOvcHxww8/VNTUJc7vzzF88KMCtxZ0cBhMHiBaPRDUkaJ4FIyCUTAKRggAAH3FUO6wZEgPAAAAAElFTkSuQmCC","orcid":"","institution":"China Coal Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Zaibin","middleName":"","lastName":"Liu","suffix":""},{"id":405614721,"identity":"0b82d00b-49a2-4c05-906e-eb597737ebbe","order_by":2,"name":"Hui Yang","email":"","orcid":"","institution":"Xi'an CCTEG Transparent Geology Technology Co. 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However, current study on identifying KCPs primarily emphasizes physical detection, overlooking the spatial aggregation patterns. In this study, we proposed a hotspot identification method for KCPs using Moran's index. and carry out experiments in Wangpo Coal Mine of Shanxi, China. The method involves evaluating the KCPs by considering their morphological characteristics and using a combination of the Analytic Hierarchy Process (AHP) and Entropy Weighting Method (EWM) for quantitative assessment. Then the spatial distribution index of the KCPs(SDI) is determined through Geographic Information System(GIS) overlay analysis and coordinate calibration. The hotspots analysis resulted in a global Moran's index value of 0.1110, indicating a positive spatial correlation of the SDI in the study area. Local Moran's index is further used to identify hotspots of KCPs. A total of 11 special KCPs were identified within the study area, including 5 high-high cluster KCPs. Finally, we analysed the geological impact of fault and fold distributions on KCPs development in these high-risk areas. 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