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Assessing relevance of tweets for risk communication

International Journal of Digital Earth 2019年第7期12卷 781-801页

作者： Xiaohui Liu Bandana Kar Chaoyang Zhang David M.Cochran Resilience Research Centre Dalhousie UniversityHalifaxNSCanada Urban Dynamics Institute Computational Sciences and Engineering DivisionOak Ridge National LaboratoryOak RidgeTNUSA School of Computing University of Southern MississippiHattiesburgMSUSA Department of Geography and Geology University of Southern MississippiHattiesburgMSUSA

Although Twitter is used for emergency management activities,the relevance of tweets during a hazard event is still open to *** this study,six different computational(*** Language Processing)and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood ***,tweets containing information about the flooding events and its impacts were *** of the relationships between tweet volume and its content with precipitation amount,damage extent,and official reports revealed that relevant tweets provided information about the event and its impacts rather than any other risk information that public expects to receive via alert ***,only 14% of the geo-tagged tweets and only 0.06% of the total fire hose tweets were found to be relevant to the *** providing insight into the quality of social media data and its usefulness to emergency management activities,this study contributes to the literature on quality of big *** research in this area would focus on assessing the reliability of relevant tweets for disaster related situational awareness.

关键词： Content relevance Twitter spatiotemporal data mining emergency management risk communication

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Recognizing mixed urban functions from human activities using representation learning methods

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International Journal of Digital Earth 2023年第1期16卷 289-307页

作者： Junjie Hu Yong Gao Xuechen Wang Yu Liu Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijingPeople's Republic of China

When various urban functions are integrated into one location,they form a mixture of *** emerging big data promote an alternative way to identify mixed ***,current methods are largely unable to extract deep features in these data,resulting in low *** this study,we focused on recognizing mixed urban functions from the perspective of human activities,which are essential indicators of functional areas in a *** proposed a framework to comprehensively extract deep features of human activities in big data,including activity dynamics,mobility interactions,and activity semantics,through representation learning ***,integrating these features,we employed fuzzy clustering to identify the mixture of urban *** conducted a case study using taxiflow and social media data in Beijing,China,in whichfive urban functions and their correlations with land use were *** mixture degree of urban functions in each location was revealed,which had a negative correlation with taxi trip *** results confirmed the advantages of our method in understanding mixed urban functions by employing various representation learning methods to comprehensively depict human *** study has important implications for urban planners in understanding urban systems and developing better strategies.

关键词： Mixed urban function human activity representation learning spatiotemporal data mining

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Spatio-temporal evolution and influencing factors of geopolitical relations among Arctic countries based on news big data

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Journal of Geographical Sciences 2022年第10期32卷 2036-2052页

作者： LI Meng YUAN Wen YUAN Wu NIU Fangqu LI Hanqin HU Duanmu Institute of Geographic Sciences and Natural Resources Research CASBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China School of Computer Science&Technology BITBeijing 100081China First Research Institute of the Ministry of Public Security of PRC Beijing 100048China

Global warming has caused the Arctic Ocean ice cover to *** endangers the environment but has made traversing the Arctic channel ***,the strategic position of the Arctic has been significantly *** a near-Arctic country,China has formulated relevant policies that will be directly impacted by changes in the international relations between the eight Arctic countries(regions).A comprehensive and real-time analysis of the various characteristics of the Arctic geographical relationship is required in China,which helps formulate political,economic,and diplomatic *** global real-time open databases provide news data from major media in various *** makes it possible to monitor geographical relationships in *** paper explores key elements of the social development of eight Arctic countries(regions)over 2013-2019 based on the GDELT database and the method of labeled latent Dirichlet *** paper also constructs the national interaction network and identifies the evolution pattern for the relationships between Arctic countries(regions).The following conclusions are drawn.(1)Arctic news hotspot is now focusing on climate change/ice cap melting which is becoming the main driving factor for changes in geographical relationships in the Arctic.(2)There is a strong correlation between the number of news pieces about ice cap melting and the sea ice area.(3)With the melting of the ice caps,the social,economic,and military activities in the Arctic have been booming,and the competition for dominance is becoming increasingly *** general,there is a pattern of domination by Russia and Canada.

关键词： Arctic geographical relationship spatiotemporal data mining topic model interactive network big data

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Taxi origin and destination demand prediction based on deep learning:a review

Digital Transportation and Safety

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Digital Transportation and Safety 2023年第3期2卷 176-189页

作者： Dan Peng Mingxia Huang Zhibo Xing Department of Transportation and Geomatics Engineering Shenyang Jianzhu UniversityShenyang 110168China

Taxi demand prediction is a crucial component of intelligent transportation system *** to region-based demand prediction,origin-destination(OD)demand prediction has a wide range of potential applications,including real-time matching,idle vehicle allocation,ridesharing services,and dynamic pricing,among ***,because OD demand involves complex spatiotemporal dependence,research in this area has been limited thus *** this paper,we first review existing research from four perspectives:topology construction,temporal and spatial feature processing,and other relevant *** then elaborate on the advantages and limitations of OD prediction methods based on deep learning architecture ***,we discuss ongoing challenges in OD prediction,such as dynamics,spatiotemporal dependence,semantic differentiation,time window selection,and data sparsity problems,and summarize and compare potential solutions to each *** findings offer valuable insights for model selection in OD demand ***,we provide public datasets and open-source code,along with suggestions for future research directions.

关键词： Deep learning Taxi demand prediction Taxi OD demand prediction spatiotemporal data mining Dynamic graph

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