Global warming has caused the Arctic Ocean ice cover to shrink.This endangers the environment but has made traversing the Arctic channel possible.Therefore,the strategic position of the Arctic has been significantly i...Global warming has caused the Arctic Ocean ice cover to shrink.This endangers the environment but has made traversing the Arctic channel possible.Therefore,the strategic position of the Arctic has been significantly improved.As 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 countermeasures.Massive global real-time open databases provide news data from major media in various countries.This makes it possible to monitor geographical relationships in real-time.This 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 allocation.This 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 fierce.In general,there is a pattern of domination by Russia and Canada.展开更多
The burgeoning field of intelligent transportation systems(ITS)has been pivotal in addressing contemporary traffic challenges,significantly benefiting from the evolution of computational capabilities and sensor techno...The burgeoning field of intelligent transportation systems(ITS)has been pivotal in addressing contemporary traffic challenges,significantly benefiting from the evolution of computational capabilities and sensor technologies.This surge in technical advancement has paved the way for extensive reliance on deep-learning methodologies to exploit largescale traffic data.Such efforts are directed toward decoding the intricate spatiotemporal dynamics inherent in traffic prediction.This study delves into the realm of traffic prediction,encompassing time series,spatiotemporal,and origin-destination(OD)predictions,to dissect the nuances among various predictive methodologies.Through a meticulous examination,this paper highlights the efficacy of spatiotemporal coupling techniques in enhancing prediction accuracy.Furthermore,it scrutinizes the existing challenges and delineates open and new questions within the traffic prediction domain,thereby charting out prospective avenues for future research endeavors.展开更多
Taxi demand prediction is a crucial component of intelligent transportation system research.Compared to region-based demand prediction,origin-destination(OD)demand prediction has a wide range of potential applications...Taxi demand prediction is a crucial component of intelligent transportation system research.Compared 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 others.However,because OD demand involves complex spatiotemporal dependence,research in this area has been limited thus far.In this paper,we first review existing research from four perspectives:topology construction,temporal and spatial feature processing,and other relevant factors.We then elaborate on the advantages and limitations of OD prediction methods based on deep learning architecture theory.Next,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 challenge.These findings offer valuable insights for model selection in OD demand prediction.Finally,we provide public datasets and open-source code,along with suggestions for future research directions.展开更多
When various urban functions are integrated into one location,they form a mixture of functions.The emerging big data promote an alternative way to identify mixed functions.However,current methods are largely unable to...When various urban functions are integrated into one location,they form a mixture of functions.The emerging big data promote an alternative way to identify mixed functions.However,current methods are largely unable to extract deep features in these data,resulting in low accuracy.In this study,we focused on recognizing mixed urban functions from the perspective of human activities,which are essential indicators of functional areas in a city.We 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 methods.Then,integrating these features,we employed fuzzy clustering to identify the mixture of urban functions.We conducted a case study using taxiflow and social media data in Beijing,China,in whichfive urban functions and their correlations with land use were recognized.The mixture degree of urban functions in each location was revealed,which had a negative correlation with taxi trip distance.The results confirmed the advantages of our method in understanding mixed urban functions by employing various representation learning methods to comprehensively depict human activities.This study has important implications for urban planners in understanding urban systems and developing better strategies.展开更多
Although Twitter is used for emergency management activities,the relevance of tweets during a hazard event is still open to debate.In this study,six different computational(i.e.Natural Language Processing)and spatiote...Although Twitter is used for emergency management activities,the relevance of tweets during a hazard event is still open to debate.In this study,six different computational(i.e.Natural Language Processing)and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood event.Primarily,tweets containing information about the flooding events and its impacts were analysed.Examination 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 messages.However,only 14% of the geo-tagged tweets and only 0.06% of the total fire hose tweets were found to be relevant to the event.By 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 data.Future research in this area would focus on assessing the reliability of relevant tweets for disaster related situational awareness.展开更多
基金National Natural Science Foundation of China(42071153)The Strategic Priority Research Program of Chinese Academy of Sciences(XDA19040401)The Strategic Priority Research Program of Chinese Academy of Sciences(XDA20080100)。
文摘Global warming has caused the Arctic Ocean ice cover to shrink.This endangers the environment but has made traversing the Arctic channel possible.Therefore,the strategic position of the Arctic has been significantly improved.As 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 countermeasures.Massive global real-time open databases provide news data from major media in various countries.This makes it possible to monitor geographical relationships in real-time.This 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 allocation.This 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 fierce.In general,there is a pattern of domination by Russia and Canada.
基金supported by the National Natural Science Foundation of China(62273057)。
文摘The burgeoning field of intelligent transportation systems(ITS)has been pivotal in addressing contemporary traffic challenges,significantly benefiting from the evolution of computational capabilities and sensor technologies.This surge in technical advancement has paved the way for extensive reliance on deep-learning methodologies to exploit largescale traffic data.Such efforts are directed toward decoding the intricate spatiotemporal dynamics inherent in traffic prediction.This study delves into the realm of traffic prediction,encompassing time series,spatiotemporal,and origin-destination(OD)predictions,to dissect the nuances among various predictive methodologies.Through a meticulous examination,this paper highlights the efficacy of spatiotemporal coupling techniques in enhancing prediction accuracy.Furthermore,it scrutinizes the existing challenges and delineates open and new questions within the traffic prediction domain,thereby charting out prospective avenues for future research endeavors.
基金supported by 2022 Shenyang Philosophy and Social Science Planning under grant SY202201Z,Liaoning Provincial Department of Education Project under grant LJKZ0588.
文摘Taxi demand prediction is a crucial component of intelligent transportation system research.Compared 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 others.However,because OD demand involves complex spatiotemporal dependence,research in this area has been limited thus far.In this paper,we first review existing research from four perspectives:topology construction,temporal and spatial feature processing,and other relevant factors.We then elaborate on the advantages and limitations of OD prediction methods based on deep learning architecture theory.Next,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 challenge.These findings offer valuable insights for model selection in OD demand prediction.Finally,we provide public datasets and open-source code,along with suggestions for future research directions.
基金supported by the National Natural Science Foundation of China[grant number 41971331].
文摘When various urban functions are integrated into one location,they form a mixture of functions.The emerging big data promote an alternative way to identify mixed functions.However,current methods are largely unable to extract deep features in these data,resulting in low accuracy.In this study,we focused on recognizing mixed urban functions from the perspective of human activities,which are essential indicators of functional areas in a city.We 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 methods.Then,integrating these features,we employed fuzzy clustering to identify the mixture of urban functions.We conducted a case study using taxiflow and social media data in Beijing,China,in whichfive urban functions and their correlations with land use were recognized.The mixture degree of urban functions in each location was revealed,which had a negative correlation with taxi trip distance.The results confirmed the advantages of our method in understanding mixed urban functions by employing various representation learning methods to comprehensively depict human activities.This study has important implications for urban planners in understanding urban systems and developing better strategies.
基金funded partially by the National Science Foundation[grant no CMMI-1335187]the Department of Homeland Security Contract[grant no HSHQDC-12-C-00057]the 2014,2015,and 2016 Arthell Kelley Scholarships from the Department of Geography and Geology at The University of Southern Mississippi.
文摘Although Twitter is used for emergency management activities,the relevance of tweets during a hazard event is still open to debate.In this study,six different computational(i.e.Natural Language Processing)and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood event.Primarily,tweets containing information about the flooding events and its impacts were analysed.Examination 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 messages.However,only 14% of the geo-tagged tweets and only 0.06% of the total fire hose tweets were found to be relevant to the event.By 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 data.Future research in this area would focus on assessing the reliability of relevant tweets for disaster related situational awareness.
