Mining Continuous Activity Patterns from Animal Trajectory Data

2014 ◽  
pp. 239-252 ◽  
Author(s):  
Yuwei Wang ◽  
Ze Luo ◽  
Baoping Yan ◽  
John Takekawa ◽  
Diann Prosser ◽  
...  
Keyword(s):  
Activity Patterns ◽  
Trajectory Data ◽  
Continuous Activity

scholarly journals SPATIO-TEMPORAL PATTERN MINING ON TRAJECTORY DATA USING ARM

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 395-399 ◽  
Author(s):  
S. Khoshahval ◽  
M. Farnaghi ◽  
M. Taleai
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Mining ◽  
Activity Patterns ◽  
Handheld Devices ◽  
Trajectory Data ◽  
Hidden Information ◽  
Rule Mining ◽  
User Activity ◽  
Gps Trajectories

Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user’s visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users’ behaviour in a system and can be utilized in various location-based applications.

scholarly journals LABELING RESIDENTIAL COMMUNITY CHARACTERISTICS FROM COLLECTIVE ACTIVITY PATTERNS USING TAXI TRIP DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1481-1486 ◽  
Author(s):  
Y. Zhou ◽  
Z. Fang
Keyword(s):  
Land Use ◽  
Clustering Algorithm ◽  
Activity Patterns ◽  
Socioeconomic Background ◽  
Trajectory Data ◽  
Residential Community ◽  
Activity Intensity ◽  
Urban City ◽  
Residential Communities ◽  
Collective Activity

There existing a significant social and spatial differentiation in the residential communities in urban city. People live in different places have different socioeconomic background, resulting in various geographically activity patterns. This paper aims to label the characteristics of residential communities in a city using collective activity patterns derived from taxi trip data. Specifically, we first present a method to allocate the O/D (Origin/Destination) points of taxi trips to the land use parcels where the activities taken place in. Then several indices are employed to describe the collective activity patterns, including both activity intensity, travel distance, travel time, and activity space of residents by taking account of the geographical distribution of all O/Ds of the taxi trip related to that residential community. Followed by that, an agglomerative hierarchical clustering algorithm is introduced to cluster the residential communities with similar activity patterns. In the case study of Wuhan, the residential communities are clearly divided into eight clusters, which could be labelled as ordinary communities, privileged communities, old isolated communities, suburban communities, and so on. In this paper, we provide a new perspective to label the land use under same type from people’s mobility patterns with the support of big trajectory data.

scholarly journals Activity of eastern chipmunks (Tamias striatus) during the summer and fall

2016 ◽  
Vol 94 (10) ◽  
pp. 685-695 ◽  
Author(s):  
S.E. LaZerte ◽  
D.L. Kramer
Keyword(s):  
Small Mammals ◽  
Daily Activity ◽  
Activity Patterns ◽  
Tamias Striatus ◽  
Continuous Activity ◽  
Food Storing ◽  
Radio Transmitters ◽  
Novel Method ◽  
Eastern Chipmunks ◽  
Daily Activity Patterns

Measuring activity of small mammals in the field is challenging because they are often out of view. We used a novel method, based on temperatures of collar radio transmitters, to quantify the proportion of time eastern chipmunks (Tamias striatus (L., 1758)) spent active, curled up resting, and torpid during the summer and fall of 2 years in southern Quebec. Time active over the 24 h day was lower in a nonmast (8%) than a mast (26%) year. In the mast year, activity varied strongly from a low of 7% during the summer lull to a high of 35% in the fall. Chipmunks that exploited a feeder had higher activity (33%) than chipmunks that did not (19%). Activity was higher during the day, but some activity occurred at night. Daily activity patterns varied strongly among seasonal periods. There was no evidence of torpor during the summer lull. Torpor started much earlier in the nonmast than in the mast year and occurred more at night than during the day. Overall, our study suggests that activity in this food-storing hibernator is positively influenced by food availability and indicates that thermosensitive radiotelemetry is a promising method for recording continuous activity.

Predicting spatial activity patterns in a neural map from a probabilistic atlas of 3-D reconstructed neurons

1995 ◽  
Vol 53 ◽  
pp. 812-813
Author(s):  
G. Jacobs ◽  
F. Theunissen
Keyword(s):  
Activity Patterns ◽  
Three Dimensional ◽  
Sensory System ◽  
Neural System ◽  
Probabilistic Atlas ◽  
Uniform Sample ◽  
Dimensional Reconstruction ◽  
The Time Domain ◽  
And Function ◽  
Visualization Techniques

In order to understand how the algorithms underlying neural computation are implemented within any neural system, it is necessary to understand details of the anatomy, physiology and global organization of the neurons from which the system is constructed. Information is represented in neural systems by patterns of activity that vary in both their spatial extent and in the time domain. One of the great challenges to microscopists is to devise methods for imaging these patterns of activity and to correlate them with the underlying neuroanatomy and physiology. We have addressed this problem by using a combination of three dimensional reconstruction techniques, quantitative analysis and computer visualization techniques to build a probabilistic atlas of a neural map in an insect sensory system. The principal goal of this study was to derive a quantitative representation of the map, based on a uniform sample of afferents that was of sufficient size to allow statistically meaningful analyses of the relationships between structure and function.

Inducing Unconscious Stress

2020 ◽  
Vol 34 (3) ◽  
pp. 192-201
Author(s):  
Melanie M. van der Ploeg ◽  
Jos F. Brosschot ◽  
Markus Quirin ◽  
Richard D. Lane ◽  
Bart Verkuil
Keyword(s):  
Repeated Measures ◽  
Activity Patterns ◽  
Physiological Changes ◽  
Implicit Measures ◽  
Prime Word ◽  
Student Sample ◽  
Priming Task ◽  
Subject Design ◽  
The Relationship ◽  
Stress And Health

Abstract. Stress-related stimuli may be presented outside of awareness and may ultimately influence health by causing repetitive increases in physiological parameters, such as blood pressure (BP). In this study, we aimed to corroborate previous studies that demonstrated BP effects of subliminally presented stress-related stimuli. This would add evidence to the hypothesis that unconscious manifestations of stress can affect somatic health. Additionally, we suggest that these findings may be extended by measuring affective changes relating to these physiological changes, using measures for self-reported and implicit positive and negative affectivity. Using a repeated measures between-subject design, we presented either the prime word “angry” ( n = 26) or “relax” ( n = 28) subliminally (17 ms) for 100 trials to a student sample and measured systolic and diastolic BP, heart rate (HR), and affect. The “angry” prime, compared to the “relax” prime, did not affect any of the outcome variables. During the priming task, a higher level of implicit negative affect (INA) was associated with a lower systolic BP and diastolic BP. No association was found with HR. Self-reported affect and implicit positive affect were not related to the cardiovascular (CV) activity. In sum, anger and relax primes elicited similar CV activity patterns, but implicit measures of affect may provide a new method to examine the relationship between (unconscious) stress and health.

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