scholarly journals Sequential movement pattern-mining (SMP) in field-based team-sport: A framework for quantifying spatiotemporal data and improve training specificity?

2021 ◽  
pp. 1-10
Author(s):  
Ryan White ◽  
Anna Palczewska ◽  
Dan Weaving ◽  
Neil Collins ◽  
Ben Jones
Keyword(s):  
Pattern Mining ◽  
Movement Pattern ◽  
Team Sport ◽  
Spatiotemporal Data ◽  
Sequential Movement ◽  
Training Specificity

scholarly journals Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in Hainan

2018 ◽  
Author(s):  
Xinning Zhu ◽  
Tianyue Sun ◽  
Hao Yuan ◽  
Zheng Hu ◽  
Jiansong Miao
Keyword(s):  
Pattern Mining ◽  
Historical Data ◽  
Movement Pattern ◽  
Phone Call ◽  
Specific Group ◽  
Group Movement ◽  
Trajectory Similarity ◽  
Group Members ◽  
Call Detail Records

Identifying group movement patterns of crowds and understanding group behaviors is valuable for urban planners, especially when the groups are special such as tourist groups. In this paper, we present a framework to discover tourist groups and investigate the tourist behaviors using mobile phone call detail records (CDRs). Unlike GPS data, CDRs are relatively poor in spatial resolution with low sampling rates, which makes it a big challenge to identify group members from thousands of tourists. Moreover, since touristic trips are not on a regular basis, no historical data of the specific group can be used to reduce the uncertainty of trajectories. To address such challenges, we propose a method called group movement pattern mining based on similarity (GMPMS) to discover tourist groups. To avoid large amounts of trajectory similarity measurements, snapshots of the trajectories are firstly generated to extract candidate groups containing co-occurring tourists. Then, considering that different groups may follow the same itineraries, additional traveling behavioral features are defined to identify the group members. Finally, with Hainan province as an example, we provide a number of interesting insights of travel behaviors of group tours as well as individual tours, which will be helpful for tourism planning and management.

scholarly journals Deferred decentralized movement pattern mining for geosensor networks

2011 ◽  
Vol 25 (2) ◽  
pp. 273-292 ◽  
Author(s):  
Patrick Laube ◽  
Matt Duckham ◽  
Marimuthu Palaniswami
Keyword(s):  
Pattern Mining ◽  
Movement Pattern

scholarly journals APDS: A framework for discovering movement pattern from trajectory database

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771988816
Author(s):  
Guan Yuan ◽  
Zhongqiu Wang ◽  
Zhixiao Wang ◽  
Fukai Zhang ◽  
Li Yuan ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Moving Objects ◽  
Pattern Mining ◽  
Pattern Discovery ◽  
Movement Pattern ◽  
Moving Object ◽  
Periodic Pattern ◽  
Trajectory Data ◽  
Discovery System ◽  
Spatiotemporal Data Mining

Currently, the boosting of location acquisition devices makes it possible to track all kinds of moving objects, and collect and store their trajectories in database. Therefore, how to find knowledge from huge amount of trajectory data has become an attractive topic. Movement pattern is an efficient way to understand moving objects’ behavior and analyze their habits. To promote the application of spatiotemporal data mining, a moving object activity pattern discovery system is designed and implemented in this article. First of all, raw trajectory data are preprocessed using methods like data clean, data interpolation, and compression. Second, a simplified density-based trajectory clustering algorithm is implemented to find and group similar movement patterns. Third, in order to discover the trends and periodicity of movement pattern, a trajectory periodic pattern mining algorithm is developed. Finally, comprehensive experiments with different parameters are conducted to validate the pattern discovery system. The experimental results show that the system is robust and efficient to analyze moving object trajectory data and discover useful patterns.

scholarly journals Vessel Crowd Movement Pattern Mining for Maritime Traffic Management

2019 ◽  
Vol 10 (2) ◽  
pp. 105-115
Author(s):  
Rong Wen ◽  
Wenjing Yan
Keyword(s):  
Decision Making ◽  
Traffic Management ◽  
Clustering Algorithm ◽  
Pattern Mining ◽  
Movement Pattern ◽  
Temporal Data Mining ◽  
Trajectory Data ◽  
Maritime Traffic ◽  
Travel Behaviors ◽  
Crowd Movement

Abstract The goal of maritime traffic management is to provide a safe and efficient maritime environment for different type of vessels facilitating port logistics and supply chain business. However, current maritime traffic management mainly relies on the massive individual vessel’s data for decision making. Lack of macro-level understanding of vessel crowd movement around port challenges maritime safety and traffic efficiency. In this paper, we describe a spatio-temporal data mining method to discover crowd movement patterns of vessels from their short-term history data. The method first captures vessels’ crowd movement features by building vessels’ tracklets with their speed and location. A movement vector clustering algorithm is developed to find different travel behaviors for different group of vessels. With nonparametric regression on the classified vessel movement vectors which represent the crowd travel behaviors, an overall vessel movement pattern can then be discovered. In this research, we tested real trajectory data of vessels near Singapore ports. Comparing with the actual massive vessel movement data, we found that this method was able to extract vessels’ crowd movement information. The hotspots on risk area in terms of vessel traffic and speed can be identified. The method can be used to provide decision-making support for maritime traffic management.

scholarly journals Exploring Group Movement Pattern through Cellular Data: A Case Study of Tourists in Hainan

2019 ◽  
Vol 8 (2) ◽  
pp. 74 ◽  
Author(s):  
Xinning Zhu ◽  
Tianyue Sun ◽  
Hao Yuan ◽  
Zheng Hu ◽  
Jiansong Miao
Keyword(s):  
Pattern Mining ◽  
Historical Data ◽  
Movement Pattern ◽  
Phone Call ◽  
Specific Group ◽  
Group Movement ◽  
Trajectory Similarity ◽  
Group Members ◽  
Call Detail Records

Identifying group movement patterns of crowds and understanding group behaviors are valuable for urban planners, especially when the groups are special such as tourist groups. In this paper, we present a framework to discover tourist groups and investigate the tourist behaviors using mobile phone call detail records (CDRs). Unlike GPS data, CDRs are relatively poor in spatial resolution with low sampling rates, which makes it a big challenge to identify group members from thousands of tourists. Moreover, since touristic trips are not on a regular basis, no historical data of the specific group can be used to reduce the uncertainty of trajectories. To address such challenges, we propose a method called group movement pattern mining based on similarity (GMPMS) to discover tourist groups. To avoid large amounts of trajectory similarity measurements, snapshots of the trajectories are firstly generated to extract candidate groups containing co-occurring tourists. Then, considering that different groups may follow the same itineraries, additional traveling behavioral features are defined to identify the group members. Finally, with Hainan province as an example, we provide a number of interesting insights of travel behaviors of group tours as well as individual tours, which will be helpful for tourism planning and management.

scholarly journals All in One: Mining Multiple Movement Patterns

2016 ◽  
Vol 15 (05) ◽  
pp. 1115-1156 ◽  
Author(s):  
Nhathai Phan ◽  
Pascal Poncelet ◽  
Maguelonne Teisseire
Keyword(s):  
Moving Objects ◽  
Pattern Mining ◽  
Movement Patterns ◽  
Movement Pattern ◽  
Specific Kind ◽  
Object Movement ◽  
Effectiveness And Efficiency ◽  
Synthetic Datasets ◽  
Mining Algorithms ◽  
Frequent Closed Itemset

Recent improvements in positioning technology have led to a much wider availability of massive moving object data. A crucial task is to find the moving objects that travel together. In common, these object sets are called object movement patterns. Due to the emergence of many different kinds of object movement patterns in recent years, different approaches have been proposed to extract them. However, each approach only focuses on mining a specific kind of patterns. It is costly and time consuming to mine and manage various number of patterns, since we have to execute a large number of different pattern mining algorithms. Moreover, we have to execute these algorithms again whenever new data are added to the existing database. To address these issues, we first redefine movement patterns in the itemset context. Second, we propose a unifying approach, named GeT_Move, which uses a frequent closed itemset-based object movement pattern-mining algorithm to mine and manage different patterns. GeT_Move is developed in two versions which are GeT_Move and Incremental GeT_Move. To optimize the efficiency and to free the parameters setting, we further propose a Parameter Free Incremental GeT_Move algorithm. Comprehensive experiments are performed on real and large synthetic datasets to demonstrate the effectiveness and efficiency of our approaches.

scholarly journals Analysis of Instagram Users’ Movement Pattern by Cluster Analysis and Association Rule Mining

2022 ◽  
pp. 97-109
Author(s):  
Zehui Wang ◽  
Luca Koroll ◽  
Wolfram Höpken ◽  
Matthias Fuchs
Keyword(s):  
Social Media ◽  
Pattern Mining ◽  
Movement Pattern ◽  
Noise Removal ◽  
Rule Mining ◽  
Social Media Data ◽  
Social Media Platforms ◽  
Information And Communication ◽  
Data Source ◽  
Media Data

AbstractUnderstanding the characteristics of tourists’ movements is essential for tourism destination management. With advances in information and communication technology, more and more people are willing to upload photos and videos to various social media platforms while traveling. These openly available media data is gaining increasing attention in the field of movement pattern mining as a new data source. In this study, uploaded images and their geographic information within Lake Constance region, Germany were collected and through clustering analysis, a state-of-the-art k-means with noise removal algorithm was compared with the commonly used DBCSCAN on Instagram dataset. Finally, association rules between popular attractions at region-level and city-level were mined respectively. Results show that social media data like Instagram constitute a valuable input to analyse tourists’ movement patterns as input to decision support and destination management.

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