The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment,which leverages new applications and services.Since the trajectory strea...The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment,which leverages new applications and services.Since the trajectory streams is rapidly evolving,continuously created and cannot be stored indefinitely in memory,the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams.This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models.By processing the trajectory data in current window,the mining algorithm can capture the trend and evolution of moving object clusters pattern.Firstly,the density peaks clustering algorithm is exploited to identify clusters of different snapshots.The stable relationship between relatively few moving objects is used to improve the clustering efficiency.Then,by intersecting clusters from different snapshots,the gradual moving object clusters pattern is updated.The relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating process.Finally,experiment results on two real datasets demonstrate that our algorithm is effective and efficient.展开更多
A frequent trajectory patterns mining algorithm is proposed to learn the object activities and classify the trajectories in intelligent visual surveillance system.The distribution patterns of the trajectories were gen...A frequent trajectory patterns mining algorithm is proposed to learn the object activities and classify the trajectories in intelligent visual surveillance system.The distribution patterns of the trajectories were generated by an Apriori based frequent patterns mining algorithm and the trajectories were classified by the frequent trajectory patterns generated.In addition,a fuzzy c-means(FCM)based learning algorithm and a mean shift based clustering procedure were used to construct the representation of trajectories.The algorithm can be further used to describe activities and identify anomalies.The experiments on two real scenes show that the algorithm is effective.展开更多
The volume of trajectory data has become tremendously huge in recent years. How to effectively and efficiently maintain and compute such trajectory data has become a challenging task. In this paper, we propose a traje...The volume of trajectory data has become tremendously huge in recent years. How to effectively and efficiently maintain and compute such trajectory data has become a challenging task. In this paper, we propose a trajectory spatial and temporal compression framework, namely CLEAN. The key of spatial compression is to mine meaningful trajectory frequent patterns on road network. By treating the mined patterns as dictionary items, the long trajectories have the chance to be encoded by shorter paths, thus leading to smaller space cost. And an error-bounded temporal compression is carefully designed on top of the identified spatial patterns for much low space cost. Meanwhile, the patterns are also utilized to improve the performance of two trajectory applications, range query and clustering, without decompression overhead. Extensive experiments on real trajectory datasets validate that CLEAN significantly outperforms existing state-of-art approaches in terms of spatial-temporal compression and trajectory applications.展开更多
In order to detect the traffic pattern of moving objects in the city more accurately and quickly, a parallel algorithm for detecting traffic patterns using stay points and moving features is proposed. First, the featu...In order to detect the traffic pattern of moving objects in the city more accurately and quickly, a parallel algorithm for detecting traffic patterns using stay points and moving features is proposed. First, the features of the stay points in different traffic patterns are extracted, that is, the stay points of various traffic patterns are identified, respectively, and the clustering algorithm is used to mine the unique features of the stop points to different traffic patterns. Then, the moving features in different traffic patterns are extracted from a trajectory of a moving object, including the maximum speed, the average speed, and the stopping rate. A classifier is constructed to predict the traffic pattern of the trajectory using the stay points and moving features. Finally, a parallel algorithm based on Spark is proposed to detect traffic patterns. Experimental results show that the stay points and moving features can reflect the difference between different traffic modes to a greater extent, and the detection accuracy is higher than those of other methods. In addition, the parallel algorithm can increase the speed of identifying traffic patterns.展开更多
Holistic understanding of wind behaviour over space,time and height is essential for harvesting wind energy application.This study presents a novel approach for mapping frequent wind profile patterns using multidimen...Holistic understanding of wind behaviour over space,time and height is essential for harvesting wind energy application.This study presents a novel approach for mapping frequent wind profile patterns using multidimensional sequential pattern mining(MDSPM).This study is illustrated with a time series of 24 years of European Centre for Medium-Range Weather Forecasts European Reanalysis-Interim gridded(0.125°×0.125°)wind data for the Netherlands every 6 h and at six height levels.The wind data were first transformed into two spatio-temporal sequence databases(for speed and direction,respectively).Then,the Linear time Closed Itemset Miner Sequence algorithm was used to extract the multidimensional sequential patterns,which were then visualized using a 3D wind rose,a circular histogram and a geographical map.These patterns were further analysed to determine their wind shear coefficients and turbulence intensities as well as their spatial overlap with current areas with wind turbines.Our analysis identified four frequent wind profile patterns.One of them highly suitable to harvest wind energy at a height of 128 m and 68.97%of the geographical area covered by this pattern already contains wind turbines.This study shows that the proposed approach is capable of efficiently extracting meaningful patterns from complex spatio-temporal datasets.展开更多
通过轨迹大数据的挖掘,揭示旅游者时空行为模式是旅游地理学的重要研究内容。本文引入时间、空间和方向相似度对基于密度的聚类算法(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)进行了改进,选择典型的红色旅...通过轨迹大数据的挖掘,揭示旅游者时空行为模式是旅游地理学的重要研究内容。本文引入时间、空间和方向相似度对基于密度的聚类算法(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)进行了改进,选择典型的红色旅游目的地合肥市为案例,对2010—2019年的红色旅游者轨迹进行分析。研究发现:(1)所构建的研究框架和方法能够有效提取轨迹大数据中隐含的旅游者的时空行为模式;(2)合肥市红色旅游以半日游为主,夏季是红色旅游旺季;(3)红色旅游有6类模式,分别为“红色+购物娱乐”、“红色+历史文化”、“红色+登山旅游”、“红色+生态休闲”、“红色+古镇旅游”、“红色+乡村旅游”,主要分布于合肥市的西北部、东南部和西南部,模式长度12.03~18.42 km,模式持续时长0.65~13.60 h;(4)所有模式中共提取出24条旅游线路,包括全红色旅游线路(58.33%)和混合线路(41.67%),平均长度为17.69 km,平均时长2.36 h;(5)合肥会议旧址作为核心吸引物,支撑了38.46%的线路的形成;(6)蓉遵高速、兰海高速、杭瑞高速和合肥绕城高速是红色旅游模式形成中最重要的交通依托。本文提出的方法可用于其他区域旅游行为模式和线路挖掘研究,研究结果可为合肥市红色旅游空间格局优化和线路规划提供依据。展开更多
User-generated social media data tagged with geographic information present messages of dynamic spatiotemporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding ...User-generated social media data tagged with geographic information present messages of dynamic spatiotemporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding of human mobility behaviors. Several trajectory data mining approaches have been proposed to benefit from these rich datasets, but fail to incorporate aspatial semantics in mining. This study investigates mining frequent moving sequences of geographic entities with transit time from geo-tagged data. Different from previous analysis of geographic feature only trajectories, this work focuses on extracting patterns with rich context semantics. We extend raw geographic trajectories generated from geo-tagged data with rich context semantic annotations, use regions-of-interest as stops to represent interesting places, enrich them with multiple aspatial semantic annotations, and propose a semantic trajectory pattern mining algorithm that returns basic and multidimensional semantic trajectory patterns. Experimental results demonstrate that semantic trajectory patterns from our method present semantically meaningful patterns and display richer semantic knowledge.展开更多
Data mining is a powerful emerging technology that helps to extract hidden information from a huge volume of historical data. This paper is concerned with finding the frequent trajectories of moving objects in spatio-...Data mining is a powerful emerging technology that helps to extract hidden information from a huge volume of historical data. This paper is concerned with finding the frequent trajectories of moving objects in spatio-temporal data by a novel method adopting the concepts of clustering and sequential pattern mining. The algorithms used logically split the trajectory span area into clusters and then apply the k-means algorithm over this clusters until the squared error minimizes. The new method applies the threshold to obtain active clusters and arranges them in descending order based on number of trajectories passing through. From these active clusters, inter cluster patterns are found by a sequential pattern mining technique. The process is repeated until all the active clusters are linked. The clusters thus linked in sequence are the frequent trajectories. A set of experiments conducted using real datasets shows that the proposed method is relatively five times better than the existing ones. A comparison is made with the results of other algorithms and their variation is analyzed by statistical methods. Further, tests of significance are conducted with ANOVA to find the efficient threshold value for the optimum plot of frequent trajectories. The results are analyzed and found to be superior than the existing ones. This approach may be of relevance in finding alternate paths in busy networks （ congestion control）, finding the frequent paths of migratory birds, or even to predict the next level of pattern characteristics in case of time series data with minor alterations and finding the frequent path of balls in certain games.展开更多
With the widespread adoption of location- aware technology, obtaining long-sequence, massive and high-accuracy spatiotemporal trajectory data of individuals has become increasingly popular in various geographic studie...With the widespread adoption of location- aware technology, obtaining long-sequence, massive and high-accuracy spatiotemporal trajectory data of individuals has become increasingly popular in various geographic studies. Trajectory data of taxis, one of the most widely used inner-city travel modes, contain rich information about both road network traffic and travel behavior of passengers. Such data can be used to study the microscopic activity patterns of individuals as well as the macro system of urban spatial structures. This paper focuses on trajectories obtained from GPS-enabled taxis and their applications for mining urban commuting patterns. A novel approach is proposed to discover spatiotemporal patterns of household travel from the taxi trajectory dataset with a large number of point locations. The approach involves three critical steps： spatial clustering of taxi origin-destination （OD） based on urban traffic grids to discover potentially meaningful places, identifying thresh- old values from statistics of the OD clusters to extract urban jobs-housing structures, and visualization of analytic results to understand the spatial distribution and temporal trends of the revealed urban structures and implied household commuting behavior. A case study with a taxi trajectory dataset in Shanghai, China is presented to demonstrate and evaluate the proposed method.展开更多
The ecological environment of the Yellow River Basin has become more fragile under the combined action of natural and manmade activities.However,the change mechanisms of ecological vulnerability in different sub-regio...The ecological environment of the Yellow River Basin has become more fragile under the combined action of natural and manmade activities.However,the change mechanisms of ecological vulnerability in different sub-regions and periods vary,and the reasons for this variability are yet to be explained.Thus,in this study,we proposed a new remote sensing ecological vulnerability index by considering moisture,heat,greenness,dryness,land degradation,and social economy indicators and then analyzed and disclosed the spatial and temporal change patterns of ecological vulnerability of the Yellow River Basin,China from 2000 to 2022 and its driving mechanisms.The results showed that the newly proposed remote sensing ecological vulnerability index had a high accuracy,at 86.36%,which indicated a higher applicability in the Yellow River Basin.From 2000 to 2022,the average remote sensing ecological vulnerability index of the Yellow River Basin was 1.03,denoting moderate vulnerability level.The intensive vulnerability area was the most widely distributed,which was mostly located in the northern part of Shaanxi Province and the eastern part of Shanxi Province.From 2000 to 2022,the ecological vulnerability in the Yellow showed an overall stable trend,while that of the central and eastern regions showed an obvious trend of improvement.The gravity center of ecological vulnerability migrated southwest,indicating that the aggravation of ecological vulnerability in the southwestern regions was more severe than in the northeastern regions of the basin.The dominant single factor of changes in ecological vulnerability shifted from normalized difference vegetation index(NDVI)to temperature from 2000 to 2022,and the interaction factors shifted from temperature∩NDVI to temperature∩precipitation,which indicated that the global climate change exerted a more significant impact on regional ecosystems.The above results could provide decision support for the ecological protection and restoration of the Yellow River Basin.展开更多
基金This work is supported by the National Natural Science Foundationof China under Grants No. 41471371.
文摘The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment,which leverages new applications and services.Since the trajectory streams is rapidly evolving,continuously created and cannot be stored indefinitely in memory,the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams.This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models.By processing the trajectory data in current window,the mining algorithm can capture the trend and evolution of moving object clusters pattern.Firstly,the density peaks clustering algorithm is exploited to identify clusters of different snapshots.The stable relationship between relatively few moving objects is used to improve the clustering efficiency.Then,by intersecting clusters from different snapshots,the gradual moving object clusters pattern is updated.The relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating process.Finally,experiment results on two real datasets demonstrate that our algorithm is effective and efficient.
基金National High-Tech Research and Development Plan of China(No.2003AA1Z2130)Science and Technology Project of Zhejiang Province of China(No.2005C1100102)
文摘A frequent trajectory patterns mining algorithm is proposed to learn the object activities and classify the trajectories in intelligent visual surveillance system.The distribution patterns of the trajectories were generated by an Apriori based frequent patterns mining algorithm and the trajectories were classified by the frequent trajectory patterns generated.In addition,a fuzzy c-means(FCM)based learning algorithm and a mean shift based clustering procedure were used to construct the representation of trajectories.The algorithm can be further used to describe activities and identify anomalies.The experiments on two real scenes show that the algorithm is effective.
基金National Natural Science Foundation of China (Grant No. 61772371,No. 61972286)
文摘The volume of trajectory data has become tremendously huge in recent years. How to effectively and efficiently maintain and compute such trajectory data has become a challenging task. In this paper, we propose a trajectory spatial and temporal compression framework, namely CLEAN. The key of spatial compression is to mine meaningful trajectory frequent patterns on road network. By treating the mined patterns as dictionary items, the long trajectories have the chance to be encoded by shorter paths, thus leading to smaller space cost. And an error-bounded temporal compression is carefully designed on top of the identified spatial patterns for much low space cost. Meanwhile, the patterns are also utilized to improve the performance of two trajectory applications, range query and clustering, without decompression overhead. Extensive experiments on real trajectory datasets validate that CLEAN significantly outperforms existing state-of-art approaches in terms of spatial-temporal compression and trajectory applications.
基金The National Natural Science Foundation of China(No.41471371)
文摘In order to detect the traffic pattern of moving objects in the city more accurately and quickly, a parallel algorithm for detecting traffic patterns using stay points and moving features is proposed. First, the features of the stay points in different traffic patterns are extracted, that is, the stay points of various traffic patterns are identified, respectively, and the clustering algorithm is used to mine the unique features of the stop points to different traffic patterns. Then, the moving features in different traffic patterns are extracted from a trajectory of a moving object, including the maximum speed, the average speed, and the stopping rate. A classifier is constructed to predict the traffic pattern of the trajectory using the stay points and moving features. Finally, a parallel algorithm based on Spark is proposed to detect traffic patterns. Experimental results show that the stay points and moving features can reflect the difference between different traffic modes to a greater extent, and the detection accuracy is higher than those of other methods. In addition, the parallel algorithm can increase the speed of identifying traffic patterns.
基金This work was supported by the Malaysian Ministry of Education(SLAI)and Universiti Teknologi Malaysia(UTM).
文摘Holistic understanding of wind behaviour over space,time and height is essential for harvesting wind energy application.This study presents a novel approach for mapping frequent wind profile patterns using multidimensional sequential pattern mining(MDSPM).This study is illustrated with a time series of 24 years of European Centre for Medium-Range Weather Forecasts European Reanalysis-Interim gridded(0.125°×0.125°)wind data for the Netherlands every 6 h and at six height levels.The wind data were first transformed into two spatio-temporal sequence databases(for speed and direction,respectively).Then,the Linear time Closed Itemset Miner Sequence algorithm was used to extract the multidimensional sequential patterns,which were then visualized using a 3D wind rose,a circular histogram and a geographical map.These patterns were further analysed to determine their wind shear coefficients and turbulence intensities as well as their spatial overlap with current areas with wind turbines.Our analysis identified four frequent wind profile patterns.One of them highly suitable to harvest wind energy at a height of 128 m and 68.97%of the geographical area covered by this pattern already contains wind turbines.This study shows that the proposed approach is capable of efficiently extracting meaningful patterns from complex spatio-temporal datasets.
文摘User-generated social media data tagged with geographic information present messages of dynamic spatiotemporal trajectories. These increasing mobility data provide potential opportunities to enhance the understanding of human mobility behaviors. Several trajectory data mining approaches have been proposed to benefit from these rich datasets, but fail to incorporate aspatial semantics in mining. This study investigates mining frequent moving sequences of geographic entities with transit time from geo-tagged data. Different from previous analysis of geographic feature only trajectories, this work focuses on extracting patterns with rich context semantics. We extend raw geographic trajectories generated from geo-tagged data with rich context semantic annotations, use regions-of-interest as stops to represent interesting places, enrich them with multiple aspatial semantic annotations, and propose a semantic trajectory pattern mining algorithm that returns basic and multidimensional semantic trajectory patterns. Experimental results demonstrate that semantic trajectory patterns from our method present semantically meaningful patterns and display richer semantic knowledge.
基金the receipt of research supported by the TATA Consultancy Service's scholarship
文摘Data mining is a powerful emerging technology that helps to extract hidden information from a huge volume of historical data. This paper is concerned with finding the frequent trajectories of moving objects in spatio-temporal data by a novel method adopting the concepts of clustering and sequential pattern mining. The algorithms used logically split the trajectory span area into clusters and then apply the k-means algorithm over this clusters until the squared error minimizes. The new method applies the threshold to obtain active clusters and arranges them in descending order based on number of trajectories passing through. From these active clusters, inter cluster patterns are found by a sequential pattern mining technique. The process is repeated until all the active clusters are linked. The clusters thus linked in sequence are the frequent trajectories. A set of experiments conducted using real datasets shows that the proposed method is relatively five times better than the existing ones. A comparison is made with the results of other algorithms and their variation is analyzed by statistical methods. Further, tests of significance are conducted with ANOVA to find the efficient threshold value for the optimum plot of frequent trajectories. The results are analyzed and found to be superior than the existing ones. This approach may be of relevance in finding alternate paths in busy networks （ congestion control）, finding the frequent paths of migratory birds, or even to predict the next level of pattern characteristics in case of time series data with minor alterations and finding the frequent path of balls in certain games.
基金This research is sponsored by the National High Technology Research and Development of China （No. 2013AA 12A402）, the National Natural Science Foundation of China （Grant Nos. 40771138, 41101371, and 41301484） and the Zhejiang Province Key Scientific and Technological Project （No. 2013C01124）. Thanks to Dr. Zhongwei Deng for providing taxi trajectory data of Shanghai, China.
文摘With the widespread adoption of location- aware technology, obtaining long-sequence, massive and high-accuracy spatiotemporal trajectory data of individuals has become increasingly popular in various geographic studies. Trajectory data of taxis, one of the most widely used inner-city travel modes, contain rich information about both road network traffic and travel behavior of passengers. Such data can be used to study the microscopic activity patterns of individuals as well as the macro system of urban spatial structures. This paper focuses on trajectories obtained from GPS-enabled taxis and their applications for mining urban commuting patterns. A novel approach is proposed to discover spatiotemporal patterns of household travel from the taxi trajectory dataset with a large number of point locations. The approach involves three critical steps： spatial clustering of taxi origin-destination （OD） based on urban traffic grids to discover potentially meaningful places, identifying thresh- old values from statistics of the OD clusters to extract urban jobs-housing structures, and visualization of analytic results to understand the spatial distribution and temporal trends of the revealed urban structures and implied household commuting behavior. A case study with a taxi trajectory dataset in Shanghai, China is presented to demonstrate and evaluate the proposed method.
基金funded by the National Natural Science Foundation of China(42471329,42101306,42301102)the Natural Science Foundation of Shandong Province(ZR2021MD047)+1 种基金the Scientific Innovation Project for Young Scientists in Shandong Provincial Universities(2022KJ224)the Gansu Youth Science and Technology Fund Program(24JRRA100).
文摘The ecological environment of the Yellow River Basin has become more fragile under the combined action of natural and manmade activities.However,the change mechanisms of ecological vulnerability in different sub-regions and periods vary,and the reasons for this variability are yet to be explained.Thus,in this study,we proposed a new remote sensing ecological vulnerability index by considering moisture,heat,greenness,dryness,land degradation,and social economy indicators and then analyzed and disclosed the spatial and temporal change patterns of ecological vulnerability of the Yellow River Basin,China from 2000 to 2022 and its driving mechanisms.The results showed that the newly proposed remote sensing ecological vulnerability index had a high accuracy,at 86.36%,which indicated a higher applicability in the Yellow River Basin.From 2000 to 2022,the average remote sensing ecological vulnerability index of the Yellow River Basin was 1.03,denoting moderate vulnerability level.The intensive vulnerability area was the most widely distributed,which was mostly located in the northern part of Shaanxi Province and the eastern part of Shanxi Province.From 2000 to 2022,the ecological vulnerability in the Yellow showed an overall stable trend,while that of the central and eastern regions showed an obvious trend of improvement.The gravity center of ecological vulnerability migrated southwest,indicating that the aggravation of ecological vulnerability in the southwestern regions was more severe than in the northeastern regions of the basin.The dominant single factor of changes in ecological vulnerability shifted from normalized difference vegetation index(NDVI)to temperature from 2000 to 2022,and the interaction factors shifted from temperature∩NDVI to temperature∩precipitation,which indicated that the global climate change exerted a more significant impact on regional ecosystems.The above results could provide decision support for the ecological protection and restoration of the Yellow River Basin.
