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

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.

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

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8020074 ◽

2019 ◽

Vol 8 (2) ◽

pp. 74 ◽

Cited By ~ 6

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.

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On Mining Movement Pattern from Mobile Users

International Journal of Distributed Sensor Networks ◽

10.1080/15501320601069499 ◽

2007 ◽

Vol 3 (1) ◽

pp. 69-86 ◽

Cited By ~ 27

Author(s):

David Taniar ◽

John Goh

Keyword(s):

Pattern Mining ◽

Movement Pattern ◽

Mobile User ◽

Mobile Users ◽

Data Mining Algorithm ◽

Movement Data ◽

Mining Algorithm ◽

The Individual ◽

Base Location

In the era in which activities performed by mobile users are tracked through various sensing mechanisms, the movement data collected through these sensors is submitted into a data mining algorithm in order to determine the movement pattern. The movement pattern refers to the pattern that mobile users generally take to move from one base location to another base location through multiple intermediate locations. This paper provides a proposal and case study on how the movement pattern can be extracted from mobile users through transforming the user movement database to the location movement database and subsequently transferred to an algorithm Apriori-like movement pattern (AMP) and movement tree (M-tree). The result is a list of sequences in which mobile users frequently go through that which satisfies min-support and min-confidence. The result of this movement pattern mining exercise opens up a new future for the prediction of the movement for the individual mobile user.

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Teaching Computer Based Historical Data Analysis

History and Computing ◽

10.3366/hac.1994.6.1.52 ◽

1994 ◽

Vol 6 (1) ◽

pp. 52-58 ◽

Cited By ~ 1

Author(s):

Charles Anderson ◽

Robert J. Morris

Keyword(s):

Data Analysis ◽

Social History ◽

Historical Data ◽

Good Practice ◽

Computer Assisted ◽

Historical Data Analysis ◽

University Of Edinburgh ◽

Computer Based ◽

The University

A case study ofa third year course in the Department of Economic and Social History in the University of Edinburgh isusedto considerandhighlightaspects of good practice in the teaching of computer-assisted historical data analysis.

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A Case Study on the Un-Used Capacity Assessment Using Time Driven Activity Based Costing for Magnetic Components

International Journal of Industrial Management ◽

10.15282/ijim.6.1.2020.5410 ◽

2020 ◽

Vol 6 (1) ◽

pp. 18-39

Author(s):

Areena Zaini ◽

Haryantie Kamil ◽

Mohd Yazid Abu

Keyword(s):

Historical Data ◽

Manufacturing Sector ◽

Type I ◽

Activity Based Costing ◽

Cost Rate ◽

Continuous Innovation ◽

Magnetic Components ◽

Alignment Process ◽

Capacity Cost

The Electrical & Electronic (E&E) company is one of Malaysia’s leading industries that has 24.5% in manufacturing sector production. With a continuous innovation of E&E company, the current costing being used is hardly to access the complete activities with variations required for each workstation to measure the un-used capacity in term of resources and cost. The objective of this work is to develop a new costing structure using time-driven activity-based costing (TDABC) at . This data collection was obtained at E&E company located at Kuantan, Pahang that focusing on magnetic component. The historical data was considered in 2018. TDABC is used to measure the un-used capacity by constructing the time equation and capacity cost rate. This work found three conditions of un-used capacity. Type I is pessimistic situation whereby according to winding toroid core, the un-used capacity of time and cost are -14820 hours and -MYR2.60 respectively. It means the system must sacrifice the time and cost more than actual apportionment. Type II is most likely situation whereby according to assembly process, the un-used capacity of time and cost are 7400 hours and MYR201575.45 respectively. It means the system minimize the time and cost which close to fully utilize from the actual apportionment. Type III is optimistic situation whereby according to alignment process, the un-used capacity of time and cost are 4120 hours and MYR289217.15 respectively. It means the system used small amount of cost and time from the actual apportionment.

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Accuracy assessment of global historical cropland datasets based on regional reconstructed historical data—A case study in Northeast China

Science China Earth Sciences ◽

10.1007/s11430-010-4053-5 ◽

2010 ◽

Vol 53 (11) ◽

pp. 1689-1699 ◽

Cited By ~ 30

Author(s):

BeiBei Li ◽

XiuQi Fang ◽

Yu Ye ◽

XueZhen Zhang

Keyword(s):

Northeast China ◽

Historical Data ◽

Accuracy Assessment

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Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases

Electronics ◽

10.3390/electronics10121478 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1478

Author(s):

Penugonda Ravikumar ◽

Palla Likhitha ◽

Bathala Venus Vikranth Raj ◽

Rage Uday Kiran ◽

Yutaka Watanobe ◽

...

Keyword(s):

Real World ◽

Pattern Mining ◽

Transportation Network ◽

Frequent Pattern Mining ◽

Temporal Databases ◽

Frequent Pattern ◽

Frequent Patterns ◽

Temporal Database ◽

First Case

Discovering periodic-frequent patterns in temporal databases is a challenging problem of great importance in many real-world applications. Though several algorithms were described in the literature to tackle the problem of periodic-frequent pattern mining, most of these algorithms use the traditional horizontal (or row) database layout, that is, either they need to scan the database several times or do not allow asynchronous computation of periodic-frequent patterns. As a result, this kind of database layout makes the algorithms for discovering periodic-frequent patterns both time and memory inefficient. One cannot ignore the importance of mining the data stored in a vertical (or columnar) database layout. It is because real-world big data is widely stored in columnar database layout. With this motivation, this paper proposes an efficient algorithm, Periodic Frequent-Equivalence CLass Transformation (PF-ECLAT), to find periodic-frequent patterns in a columnar temporal database. Experimental results on sparse and dense real-world and synthetic databases demonstrate that PF-ECLAT is memory and runtime efficient and highly scalable. Finally, we demonstrate the usefulness of PF-ECLAT with two case studies. In the first case study, we have employed our algorithm to identify the geographical areas in which people were periodically exposed to harmful levels of air pollution in Japan. In the second case study, we have utilized our algorithm to discover the set of road segments in which congestion was regularly observed in a transportation network.

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Determining hydraulic parameters of a karst aquifer using unique historical data from large-scale dewatering by deep level mining – a case study from South Africa

Water SA ◽

10.4314/wsa.v40i3.20 ◽

2014 ◽

Vol 40 (3) ◽

pp. 555

Author(s):

A Schrader ◽

E Erasmus ◽

F Winde

Keyword(s):

South Africa ◽

Large Scale ◽

Historical Data ◽

Deep Level ◽

Karst Aquifer ◽

Hydraulic Parameters

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Research on Ship Classification Based on Trajectory Features

Journal of Navigation ◽

10.1017/s0373463317000546 ◽

2017 ◽

Vol 71 (1) ◽

pp. 100-116 ◽

Cited By ~ 14

Author(s):

Kai Sheng ◽

Zhong Liu ◽

Dechao Zhou ◽

Ailin He ◽

Chengxu Feng

Keyword(s):

Pattern Mining ◽

Extraction Methods ◽

Behaviour Pattern ◽

Automatic Identification ◽

Identification System ◽

Trajectory Data ◽

Trajectory Features ◽

Ship Classification ◽

Historical Trajectory

It is important for maritime authorities to effectively classify and identify unknown types of ships in historical trajectory data. This paper uses a logistic regression model to construct a ship classifier by utilising the features extracted from ship trajectories. First of all, three basic movement patterns are proposed according to ship sailing characteristics, with related sub-trajectory partitioning algorithms. Subsequently, three categories of trajectory features with their extraction methods are presented. Finally, a case study on building a model for classifying fishing boats and cargo ships based on real Automatic Identification System (AIS) data is given. Experimental results indicate that the proposed classification method can meet the needs of recognising uncertain types of targets in historical trajectory data, laying a foundation for further research on camouflaged ship identification, behaviour pattern mining, outlier behaviour detection and other applications.

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