Characterizing visitor engagement behavior at large-scale events: Activity sequence clustering and ranking using GPS tracking data

Abstract Background New wildlife telemetry and tracking technologies have become available in the last decade, leading to a large increase in the volume and resolution of animal tracking data. These technical developments have been accompanied by various statistical tools aimed at analysing the data obtained by these methods. Methods We used simulated habitat and tracking data to compare some of the different statistical methods frequently used to infer local resource selection and large-scale attraction/avoidance from tracking data. Notably, we compared spatial logistic regression models (SLRMs), spatio-temporal point process models (ST-PPMs), step selection models (SSMs), and integrated step selection models (iSSMs) and their interplay with habitat and animal movement properties in terms of statistical hypothesis testing. Results We demonstrated that only iSSMs and ST-PPMs showed nominal type I error rates in all studied cases, whereas SSMs may slightly and SLRMs may frequently and strongly exceed these levels. iSSMs appeared to have on average a more robust and higher statistical power than ST-PPMs. Conclusions Based on our results, we recommend the use of iSSMs to infer habitat selection or large-scale attraction/avoidance from animal tracking data. Further advantages over other approaches include short computation times, predictive capacity, and the possibility of deriving mechanistic movement models.

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An analysis on older driver's driving behavior by GPS tracking data: Road selection, left/right turn, and driving speed

Journal of Traffic and Transportation Engineering (English Edition) ◽

10.1016/j.jtte.2017.05.013 ◽

2018 ◽

Vol 5 (1) ◽

pp. 56-65 ◽

Cited By ~ 3

Author(s):

Yanning Zhao ◽

Toshiyuki Yamamoto ◽

Takayuki Morikawa

Keyword(s):

Driving Behavior ◽

Gps Tracking ◽

Tracking Data ◽

Driving Speed

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A Pilot Study on Home Range and Habitat Use of Chinese Goral (Naemorhedus Griseus): Exploring GPS Tracking Data in Cliff Landscape by Three Estimation Methods

10.21203/rs.3.rs-597882/v1 ◽

2021 ◽

Author(s):

yang teng ◽

Shupei TANG ◽

lai heda meng ◽

Liji Wu ◽

Zhiqing HAN ◽

...

Keyword(s):

Home Range ◽

Habitat Use ◽

Estimation Methods ◽

Conservation Strategies ◽

Gps Tracking ◽

Tracking Data ◽

Size Estimation ◽

Range Estimation ◽

Lower Altitude ◽

Effective Conservation

Abstract Home range size estimation is a crucial basis for developing effective conservation strategies and provides important insights into animal behavior and ecology. This study aimed at analyzing the home range variations, the influence of altitude in habitat selection, and comparing three methods in home range estimation of Chinese gorals (Naemorhedus griseus) living at a cliff landscape. The results indicated that there were significant differences between the annual home range sizes of individual animals but there was no difference in their seasonal home range sizes based on GPS tracking data of five female Chinese gorals from February 2015 to September 2018. The monthly home ranges decreased dramatically in May, June and July due to birth-giving. Notable seasonal variations were found in the micro-habitats of the Chinese gorals, as reflected by the altitude they inhabit, with higher altitude habitats used in spring and lower altitude habitats used in winter. Additionally, the altitude of monthly habitats was lowest in January, which may indicate an adaptation to low air temperature. We also found differences between estimation methods, namely minimum convex polygon (MCP), kernel density estimation (KDE) and α-local convex hull (α-LoCoH), with seasonal home range sizes derived from α-LoCoH being substantially smaller than those derived from MCP and KDE. In conclusion, our findings filled the gaps in home range study for this endangered species and contributed to effective conservation strategies. Considerations shall have to be given to the variations in home range estimation caused by different methods when dealing with rugged habitats, so as to make sure that any interpretation concerning the habitat use of the targeted species made on basis of such results would be meaningful and valid.

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Comparative analysis of methods for inferring successful foraging areas from Argos and GPS tracking data

Marine Ecology Progress Series ◽

10.3354/meps09618 ◽

2012 ◽

Vol 452 ◽

pp. 253-267 ◽

Cited By ~ 38

Author(s):

AC Dragon ◽

A Bar-Hen ◽

P Monestiez ◽

C Guinet

Keyword(s):

Comparative Analysis ◽

Gps Tracking ◽

Tracking Data

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A Pilot Study on Home Range and Habitat Use of Chinese Goral (Naemorhedus Griseus): Exploring GPS Tracking Data in Cliff Landscape by Three Estimation Methods

10.21203/rs.3.rs-597882/v2 ◽

2021 ◽

Author(s):

Yang Teng ◽

Shupei TANG ◽

Dalai Menghe ◽

Liji Wu ◽

Zhiqing HAN ◽

...

Keyword(s):

Home Range ◽

Habitat Use ◽

Estimation Methods ◽

Conservation Strategies ◽

Gps Tracking ◽

Tracking Data ◽

Size Estimation ◽

Range Estimation ◽

Lower Altitude ◽

Effective Conservation

Abstract Home range size estimation is a crucial basis for developing effective conservation strategies and provides important insights into animal behavior and ecology. This study aimed at analyzing the home range variations, the influence of altitude in habitat selection, and comparing three methods in home range estimation of Chinese gorals (Naemorhedus griseus) living at a cliff landscape. The results indicated that there were significant differences between the annual home range sizes of individual animals but there was no difference in their seasonal home range sizes based on GPS tracking data of five female Chinese gorals from February 2015 to September 2018. The monthly home ranges decreased dramatically in May, June and July due to birth-giving. Notable seasonal variations were found in the micro-habitats of the Chinese gorals, as reflected by the altitude they inhabit, with higher altitude habitats used in spring and lower altitude habitats used in winter. Additionally, the altitude of monthly habitats was lowest in January, which may indicate an adaptation to low air temperature. We also found differences between estimation methods, namely minimum convex polygon (MCP), kernel density estimation (KDE) and α-local convex hull (α-LoCoH), with seasonal home range sizes derived from α-LoCoH being substantially smaller than those derived from MCP and KDE. In conclusion, our findings filled the gaps in home range study for this endangered species and contributed to effective conservation strategies. Considerations shall have to be given to the variations in home range estimation caused by different methods when dealing with rugged habitats, so as to make sure that any interpretation concerning the habitat use of the targeted species made on basis of such results would be meaningful and valid.

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EdClust: A heuristic sequence clustering method with higher sensitivity

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720021500360 ◽

2021 ◽

Author(s):

Ming Cao ◽

Qinke Peng ◽

Ze-Gang Wei ◽

Fei Liu ◽

Yi-Fan Hou

Keyword(s):

Large Scale ◽

Sequence Data ◽

Clustering Algorithms ◽

Clustering Methods ◽

Sequencing Data ◽

Clustering Method ◽

Cluster Number ◽

Sequence Clustering ◽

Downstream Analysis ◽

Heuristic Clustering

The development of high-throughput technologies has produced increasing amounts of sequence data and an increasing need for efficient clustering algorithms that can process massive volumes of sequencing data for downstream analysis. Heuristic clustering methods are widely applied for sequence clustering because of their low computational complexity. Although numerous heuristic clustering methods have been developed, they suffer from two limitations: overestimation of inferred clusters and low clustering sensitivity. To address these issues, we present a new sequence clustering method (edClust) based on Edlib, a C/C[Formula: see text] library for fast, exact semi-global sequence alignment to group similar sequences. The new method edClust was tested on three large-scale sequence databases, and we compared edClust to several classic heuristic clustering methods, such as UCLUST, CD-HIT, and VSEARCH. Evaluations based on the metrics of cluster number and seed sensitivity (SS) demonstrate that edClust can produce fewer clusters than other methods and that its SS is higher than that of other methods. The source codes of edClust are available from https://github.com/zhang134/EdClust.git under the GNU GPL license.

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Psychiatric epidemiology: Looking to the future

Practical Psychiatric Epidemiology ◽

10.1093/med/9780198735564.003.0025 ◽

2020 ◽

pp. 425-432

Author(s):

Jayati Das-Munshi ◽

Tamsin Ford ◽

Matthew Hotopf ◽

Martin Prince ◽

Robert Stewart

Keyword(s):

Science Communication ◽

Large Scale ◽

New Technologies ◽

Open Science ◽

Psychiatric Epidemiology ◽

Biological Data ◽

Gps Tracking ◽

The Future ◽

The Impact

In this final chapter to the second edition of Practical Psychiatric Epidemiology, developments in psychiatric epidemiology since the first edition are summarized and the editors offer a view on where the future may lie. The themes summarized in this chapter include those related to large-scale datasets or ‘big data’, new technologies and science communication (including data generated through GPS tracking systems and the impact of social media), expanding biological data and biobanks, as well as the impact of globalization, migration, and culture on understanding psychiatric epidemiological principles. The last part of this chapter raises the important issue of open science initiatives. The chapter concludes with a brief discussion on the constancy and ongoing evolution of psychiatric epidemiology.

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