The Haiyuan fault is a major seismogenic fault in north-central China where the 1920 Haiyuan earthquake of magnitude 8.5 occurred, resulting in more than 220000 deaths. The fault zone can be divided into three segment...The Haiyuan fault is a major seismogenic fault in north-central China where the 1920 Haiyuan earthquake of magnitude 8.5 occurred, resulting in more than 220000 deaths. The fault zone can be divided into three segments based on their geometric patterns and associated geomorphology. To study paleoseismology and recurrent history of devastating earthquakes along the fault, we dug 17 trenches along different segments of the fault zone. Although only 10 of them allow the paleoearthquake event to be dated, together with the 8 trenches dug previously they still provide adequate information that enables us to capture major paleoearthquakes oc- curring along the fault during the past geological time. We discovered 3 events along the eastern segment during the past 14000 a, 7 events along the middle segment during the past 9000 a, and 6 events along the western segment during the past 10000 a. These events clearly depict two temporal clusters. The first cluster occurs from 4600 to 6400 a, and the second occurs from 1000 to 2800 a, approximately. Each cluster lasts about 2000 a. Time period between these two clus- ters is also about 2000 a. Based on fault geometry, segmentation pattern, and paleoearthquake events along the Haiyuan fault we can identify three scales of earthquake rupture: rupture of one segment, cascade rupture of two segments, and cascade rupture of entire fault (three segments). Interactions of slip patches on the surface of the fault may cause rupture on one patch or ruptures of more than two to three patchs to form the complex patterns of cascade rupture events.展开更多
The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based services.Location-based social networks have become very popular as they provide end users like us with se...The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based services.Location-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our devices.However,when users utilize these services,they inevitably expose personal information such as their ID and sensitive location to the servers.Due to untrustworthy servers and malicious attackers with colossal background knowledge,users'personal information is at risk on these servers.Unfortunately,many privacy-preserving solutions for protecting trajectories have significantly decreased utility after deployment.We have come up with a new trajectory privacy protection solution that contraposes the area of interest for users.Firstly,Staying Points Detection Method based on Temporal-Spatial Restrictions(SPDM-TSR)is an interest area mining method based on temporal-spatial restrictions,which can clearly distinguish between staying and moving points.Additionally,our privacy protection mechanism focuses on the user's areas of interest rather than the entire trajectory.Furthermore,our proposed mechanism does not rely on third-party service providers and the attackers'background knowledge settings.We test our models on real datasets,and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability.展开更多
Background:Pulmonary tuberculosis(PTB,both smear positive and smear negative)is an airborne infectious disease of major public health concern in China and other parts of the world where PTB endemicity is reported.This...Background:Pulmonary tuberculosis(PTB,both smear positive and smear negative)is an airborne infectious disease of major public health concern in China and other parts of the world where PTB endemicity is reported.This study aims at identifying PTB spatio-temporal clusters and associated risk factors in Zhaotong prefecture-level city,located in southwest China,where the PTB notification rate was higher than the average rate in the entire country.Methods:Space-time scan statistics were carried out using PTB registered data in the nationwide TB online registration system from 2011 to 2015,to identify spatial clusters.PTB patients diagnosed between October 2015 and February 2016 were selected and a structured questionnaire was administered to collect a set of variables that includes socio-economic status,behavioural characteristics,local environmental and biological characteristics.Based on the discovery of detailed town-level spatio-temporal PTB clusters,we divided selected subjects into two groups including the cases that resides within and outside identified clusters.Then,logistic regression analysis was applied comparing the results of variables between the two groups.Results:A total of 1508 subjects consented and participated in the survey.Clusters for PTB cases were identified in 38 towns distributed over south-western Zhaotong.Logistic regression analysis showed that history of chronic bronchitis(OR=3.683,95%CI:2.180-6.223),living in an urban area(OR=5.876,95%CI:2.381-14.502)and using coal as the main fuel(OR=9.356,95%CI:5.620-15.576)were independently associated with clustering.While,not smoking(OR=0.340,95%CI:0.137-0.843)is the protection factor of spatial clustering.Conclusions:We found PTB specially clustered in south-western Zhaotong.The strong associated factors influencing the PTB spatial cluster including:the history of chronic bronchitis,living in the urban area,smoking and the use of coal as the main fuel for cooking and heating.Therefore,efforts should be made to curtail these associated factors.展开更多
Human activities significantly impact the environment.Understanding the patterns and distribution of these activities is crucial for ecological protection.With location-based technology advancement,big data such as lo...Human activities significantly impact the environment.Understanding the patterns and distribution of these activities is crucial for ecological protection.With location-based technology advancement,big data such as location and trajectory data can be used to analyze human activities on finer temporal and spatial scales than traditional remote sensing data.In this study,Qilian Mountain National Park(QMNP)was chosen as the research area,and Tencent location data were used to construct time series data.Time series clustering and decomposition were performed,and the spatio-temporal distribution characteristics of human activities in the study area were analyzed in conjunction with GPS trajectory data and land use data.The study found two distinct human activity patterns,Pattern A and Pattern B,in QMNP.Compared to Pattern B,Pattern A had a higher volume of location data and clear nighttime peaks.By incorporating land use and trajectory data,we conclude that Pattern A and Pattern B represent the activity patterns of the resident and tourist populations,respectively.Moreover,the study identified seasonal variations in human activities,with human activity in summer being approximately two hours longer than in winter.We also conducted an analysis of human activities in different counties within the study area.展开更多
Short-term travel flow prediction has been the core of the intelligent transport systems(ITS). An advanced method based on fuzzy C-means(FCM) and extreme learning machine(ELM) has been discussed by analyzing predictio...Short-term travel flow prediction has been the core of the intelligent transport systems(ITS). An advanced method based on fuzzy C-means(FCM) and extreme learning machine(ELM) has been discussed by analyzing prediction model. First, this model takes advantages of ability to adapt to nonlinear systems and the fast speed of ELM algorithm. Second, with FCM-clustering function, this novel model can get the clusters and the membership in the same cluster, which means that the associated observation points have been chosen. Therefore, the spatial relations can be used by giving the weight to every observation points when the model trains and tests the ELM. Third, by analyzing the actual data in Haining City in 2016, the feasibility and advantages of FCM-ELM prediction model have been shown when compared with other prediction algorithms.展开更多
The discovery of spatio-temporal clusters in complex spatio-temporal data-sets has been a challenging issue in the domain of spatio-temporal data mining and knowledge discovery.In this paper,a novel spatio-temporal cl...The discovery of spatio-temporal clusters in complex spatio-temporal data-sets has been a challenging issue in the domain of spatio-temporal data mining and knowledge discovery.In this paper,a novel spatio-temporal clustering method based on spatio-temporal shared nearest neighbors(STSNN)is proposed to detect spatio-temporal clusters of different sizes,shapes,and densities in spatiotemporal databases with a large amount of noise.The concepts of windowed distance and shared nearest neighbor are utilized to define a novel spatiotemporal density for a spatio-temporal entity with definite mathematical meanings.Then,the density-based clustering strategy is employed to uncover spatio-temporal clusters.The spatio-temporal clustering algorithm developed in this paper is easily implemented and less sensitive to density variation among spatio-temporal entities.Experiments are undertaken on several simulated datasets to demonstrate the effectiveness and advantage of the STSNN algorithm.Also,the real-world applications on two seismic databases show that the STSNN algorithm has the ability to uncover foreshocks and aftershocks effectively.展开更多
基金supported by National Key Bas ic Research Program(Grant No.G1998040701)Joint Foundation of Earthquake Sciences(Grant No.95087421).
文摘The Haiyuan fault is a major seismogenic fault in north-central China where the 1920 Haiyuan earthquake of magnitude 8.5 occurred, resulting in more than 220000 deaths. The fault zone can be divided into three segments based on their geometric patterns and associated geomorphology. To study paleoseismology and recurrent history of devastating earthquakes along the fault, we dug 17 trenches along different segments of the fault zone. Although only 10 of them allow the paleoearthquake event to be dated, together with the 8 trenches dug previously they still provide adequate information that enables us to capture major paleoearthquakes oc- curring along the fault during the past geological time. We discovered 3 events along the eastern segment during the past 14000 a, 7 events along the middle segment during the past 9000 a, and 6 events along the western segment during the past 10000 a. These events clearly depict two temporal clusters. The first cluster occurs from 4600 to 6400 a, and the second occurs from 1000 to 2800 a, approximately. Each cluster lasts about 2000 a. Time period between these two clus- ters is also about 2000 a. Based on fault geometry, segmentation pattern, and paleoearthquake events along the Haiyuan fault we can identify three scales of earthquake rupture: rupture of one segment, cascade rupture of two segments, and cascade rupture of entire fault (three segments). Interactions of slip patches on the surface of the fault may cause rupture on one patch or ruptures of more than two to three patchs to form the complex patterns of cascade rupture events.
文摘The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based services.Location-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our devices.However,when users utilize these services,they inevitably expose personal information such as their ID and sensitive location to the servers.Due to untrustworthy servers and malicious attackers with colossal background knowledge,users'personal information is at risk on these servers.Unfortunately,many privacy-preserving solutions for protecting trajectories have significantly decreased utility after deployment.We have come up with a new trajectory privacy protection solution that contraposes the area of interest for users.Firstly,Staying Points Detection Method based on Temporal-Spatial Restrictions(SPDM-TSR)is an interest area mining method based on temporal-spatial restrictions,which can clearly distinguish between staying and moving points.Additionally,our privacy protection mechanism focuses on the user's areas of interest rather than the entire trajectory.Furthermore,our proposed mechanism does not rely on third-party service providers and the attackers'background knowledge settings.We test our models on real datasets,and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability.
基金This study was supported by the National Special Science and Technology Project for Major Infectious Diseases of China(Grant No.2012ZX10004–220,2016ZX10004222–006)the China-UK Global Health Support Programme(Grant No.GHSP-CS-OP1–01)+1 种基金The Forth Round of Three-Year Public Health Action Plan of Shanghai,China(No.15GWZK0101,GWIV-29)The funders had no role in the study design,data collection and analysis,decision to publish,or preparation of the paper.
文摘Background:Pulmonary tuberculosis(PTB,both smear positive and smear negative)is an airborne infectious disease of major public health concern in China and other parts of the world where PTB endemicity is reported.This study aims at identifying PTB spatio-temporal clusters and associated risk factors in Zhaotong prefecture-level city,located in southwest China,where the PTB notification rate was higher than the average rate in the entire country.Methods:Space-time scan statistics were carried out using PTB registered data in the nationwide TB online registration system from 2011 to 2015,to identify spatial clusters.PTB patients diagnosed between October 2015 and February 2016 were selected and a structured questionnaire was administered to collect a set of variables that includes socio-economic status,behavioural characteristics,local environmental and biological characteristics.Based on the discovery of detailed town-level spatio-temporal PTB clusters,we divided selected subjects into two groups including the cases that resides within and outside identified clusters.Then,logistic regression analysis was applied comparing the results of variables between the two groups.Results:A total of 1508 subjects consented and participated in the survey.Clusters for PTB cases were identified in 38 towns distributed over south-western Zhaotong.Logistic regression analysis showed that history of chronic bronchitis(OR=3.683,95%CI:2.180-6.223),living in an urban area(OR=5.876,95%CI:2.381-14.502)and using coal as the main fuel(OR=9.356,95%CI:5.620-15.576)were independently associated with clustering.While,not smoking(OR=0.340,95%CI:0.137-0.843)is the protection factor of spatial clustering.Conclusions:We found PTB specially clustered in south-western Zhaotong.The strong associated factors influencing the PTB spatial cluster including:the history of chronic bronchitis,living in the urban area,smoking and the use of coal as the main fuel for cooking and heating.Therefore,efforts should be made to curtail these associated factors.
基金supported by the National Key R&D Program of China(grant number 2019YFC0507401)the National Natural Science Foundation of China(grant number 42371325).
文摘Human activities significantly impact the environment.Understanding the patterns and distribution of these activities is crucial for ecological protection.With location-based technology advancement,big data such as location and trajectory data can be used to analyze human activities on finer temporal and spatial scales than traditional remote sensing data.In this study,Qilian Mountain National Park(QMNP)was chosen as the research area,and Tencent location data were used to construct time series data.Time series clustering and decomposition were performed,and the spatio-temporal distribution characteristics of human activities in the study area were analyzed in conjunction with GPS trajectory data and land use data.The study found two distinct human activity patterns,Pattern A and Pattern B,in QMNP.Compared to Pattern B,Pattern A had a higher volume of location data and clear nighttime peaks.By incorporating land use and trajectory data,we conclude that Pattern A and Pattern B represent the activity patterns of the resident and tourist populations,respectively.Moreover,the study identified seasonal variations in human activities,with human activity in summer being approximately two hours longer than in winter.We also conducted an analysis of human activities in different counties within the study area.
基金Project(2016YFB0100906)supported by the National Key R&D Program in ChinaProject(2014BAG03B01)supported by the National Science and Technology Support plan Project China+1 种基金Project(61673232)supported by the National Natural Science Foundation of ChinaProjects(Dl S11090028000,D171100006417003)supported by Beijing Municipal Science and Technology Program,China
文摘Short-term travel flow prediction has been the core of the intelligent transport systems(ITS). An advanced method based on fuzzy C-means(FCM) and extreme learning machine(ELM) has been discussed by analyzing prediction model. First, this model takes advantages of ability to adapt to nonlinear systems and the fast speed of ELM algorithm. Second, with FCM-clustering function, this novel model can get the clusters and the membership in the same cluster, which means that the associated observation points have been chosen. Therefore, the spatial relations can be used by giving the weight to every observation points when the model trains and tests the ELM. Third, by analyzing the actual data in Haining City in 2016, the feasibility and advantages of FCM-ELM prediction model have been shown when compared with other prediction algorithms.
基金The work described was supported by the Major State Basic Research Development Program of China(973 Program),No.2012CB719906Program for New Century Excellent Talents in University(NCET),No.NCET-10-0831National Natural Science Foundation of China(NSFC),No.40871180.
文摘The discovery of spatio-temporal clusters in complex spatio-temporal data-sets has been a challenging issue in the domain of spatio-temporal data mining and knowledge discovery.In this paper,a novel spatio-temporal clustering method based on spatio-temporal shared nearest neighbors(STSNN)is proposed to detect spatio-temporal clusters of different sizes,shapes,and densities in spatiotemporal databases with a large amount of noise.The concepts of windowed distance and shared nearest neighbor are utilized to define a novel spatiotemporal density for a spatio-temporal entity with definite mathematical meanings.Then,the density-based clustering strategy is employed to uncover spatio-temporal clusters.The spatio-temporal clustering algorithm developed in this paper is easily implemented and less sensitive to density variation among spatio-temporal entities.Experiments are undertaken on several simulated datasets to demonstrate the effectiveness and advantage of the STSNN algorithm.Also,the real-world applications on two seismic databases show that the STSNN algorithm has the ability to uncover foreshocks and aftershocks effectively.
