Latent semantic understanding of geographical environment spatio-temporal data based on topic model

Text is an important data mode of battlefield information. Mining spatial-temporal semantic information of geographical environment from battlefield text is an important method for machine to understand battlefield environment, which is helpful to expand battlefield environment spatial cognition and...

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Autores principales:	ZHU Jie, ZHANG Hongjun, LIAO Xianglin, TIAN Jiangpeng
Formato:	article
Lenguaje:	ZH
Publicado:	Surveying and Mapping Press 2021
Materias:	topic model geographical environment spatio-temporal data semantic understanding spatial analysis Mathematical geography. Cartography GA1-1776
Acceso en línea:	https://doaj.org/article/2e3ea86b753646059c998bbd5b651f6f
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id	oai:doaj.org-article:2e3ea86b753646059c998bbd5b651f6f
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spelling	oai:doaj.org-article:2e3ea86b753646059c998bbd5b651f6f2021-11-12T02:25:59ZLatent semantic understanding of geographical environment spatio-temporal data based on topic model1001-159510.11947/j.AGCS.2021.20200380https://doaj.org/article/2e3ea86b753646059c998bbd5b651f6f2021-10-01T00:00:00Zhttp://xb.sinomaps.com/article/2021/1001-1595/2021-10-1404.htmhttps://doaj.org/toc/1001-1595Text is an important data mode of battlefield information. Mining spatial-temporal semantic information of geographical environment from battlefield text is an important method for machine to understand battlefield environment, which is helpful to expand battlefield environment spatial cognition and understanding. A method based on topic model is designed to reflect the semantic relationship between geographical spatio-temporal factors and event topics, and different topic classification with its distribution of word features are formed by the method of information extraction to mine the relevant information of topic elements; the joint distribution model of event topic and geographical spatio-temporal semantic features is established to automatically discover the correlation among time, space and event topics, thus generating the latent geographical spatio-temporal semantic topics; through the experimental verification and the application practice, we believe that the law of spatio-temporal distribution under different topics can be seek by using correlation between the event topics and location information with spatial analysis method, so as to provide the basis for the location prediction of new events and the countermeasures of seeking advantages and avoiding disadvantages, and expand the traditional thematic analysis of geographical events.ZHU JieZHANG HongjunLIAO XianglinTIAN JiangpengSurveying and Mapping Pressarticletopic modelgeographical environmentspatio-temporal datasemantic understandingspatial analysisMathematical geography. CartographyGA1-1776ZHActa Geodaetica et Cartographica Sinica, Vol 50, Iss 10, Pp 1404-1415 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	topic model geographical environment spatio-temporal data semantic understanding spatial analysis Mathematical geography. Cartography GA1-1776
spellingShingle	topic model geographical environment spatio-temporal data semantic understanding spatial analysis Mathematical geography. Cartography GA1-1776 ZHU Jie ZHANG Hongjun LIAO Xianglin TIAN Jiangpeng Latent semantic understanding of geographical environment spatio-temporal data based on topic model
description	Text is an important data mode of battlefield information. Mining spatial-temporal semantic information of geographical environment from battlefield text is an important method for machine to understand battlefield environment, which is helpful to expand battlefield environment spatial cognition and understanding. A method based on topic model is designed to reflect the semantic relationship between geographical spatio-temporal factors and event topics, and different topic classification with its distribution of word features are formed by the method of information extraction to mine the relevant information of topic elements; the joint distribution model of event topic and geographical spatio-temporal semantic features is established to automatically discover the correlation among time, space and event topics, thus generating the latent geographical spatio-temporal semantic topics; through the experimental verification and the application practice, we believe that the law of spatio-temporal distribution under different topics can be seek by using correlation between the event topics and location information with spatial analysis method, so as to provide the basis for the location prediction of new events and the countermeasures of seeking advantages and avoiding disadvantages, and expand the traditional thematic analysis of geographical events.
format	article
author	ZHU Jie ZHANG Hongjun LIAO Xianglin TIAN Jiangpeng
author_facet	ZHU Jie ZHANG Hongjun LIAO Xianglin TIAN Jiangpeng
author_sort	ZHU Jie
title	Latent semantic understanding of geographical environment spatio-temporal data based on topic model
title_short	Latent semantic understanding of geographical environment spatio-temporal data based on topic model
title_full	Latent semantic understanding of geographical environment spatio-temporal data based on topic model
title_fullStr	Latent semantic understanding of geographical environment spatio-temporal data based on topic model
title_full_unstemmed	Latent semantic understanding of geographical environment spatio-temporal data based on topic model
title_sort	latent semantic understanding of geographical environment spatio-temporal data based on topic model
publisher	Surveying and Mapping Press
publishDate	2021
url	https://doaj.org/article/2e3ea86b753646059c998bbd5b651f6f
work_keys_str_mv	AT zhujie latentsemanticunderstandingofgeographicalenvironmentspatiotemporaldatabasedontopicmodel AT zhanghongjun latentsemanticunderstandingofgeographicalenvironmentspatiotemporaldatabasedontopicmodel AT liaoxianglin latentsemanticunderstandingofgeographicalenvironmentspatiotemporaldatabasedontopicmodel AT tianjiangpeng latentsemanticunderstandingofgeographicalenvironmentspatiotemporaldatabasedontopicmodel
_version_	1718431280988684288

Latent semantic understanding of geographical environment spatio-temporal data based on topic model

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