25,112 research outputs found
Analysing Human Mobility Patterns of Hiking Activities through Complex Network Theory
The exploitation of high volume of geolocalized data from social sport
tracking applications of outdoor activities can be useful for natural resource
planning and to understand the human mobility patterns during leisure
activities. This geolocalized data represents the selection of hike activities
according to subjective and objective factors such as personal goals, personal
abilities, trail conditions or weather conditions. In our approach, human
mobility patterns are analysed from trajectories which are generated by hikers.
We propose the generation of the trail network identifying special points in
the overlap of trajectories. Trail crossings and trailheads define our network
and shape topological features. We analyse the trail network of Balearic
Islands, as a case of study, using complex weighted network theory. The
analysis is divided into the four seasons of the year to observe the impact of
weather conditions on the network topology. The number of visited places does
not decrease despite the large difference in the number of samples of the two
seasons with larger and lower activity. It is in summer season where it is
produced the most significant variation in the frequency and localization of
activities from inland regions to coastal areas. Finally, we compare our model
with other related studies where the network possesses a different purpose. One
finding of our approach is the detection of regions with relevant importance
where landscape interventions can be applied in function of the communities.Comment: 20 pages, 9 figures, accepte
Inferring Unusual Crowd Events From Mobile Phone Call Detail Records
The pervasiveness and availability of mobile phone data offer the opportunity
of discovering usable knowledge about crowd behaviors in urban environments.
Cities can leverage such knowledge in order to provide better services (e.g.,
public transport planning, optimized resource allocation) and safer cities.
Call Detail Record (CDR) data represents a practical data source to detect and
monitor unusual events considering the high level of mobile phone penetration,
compared with GPS equipped and open devices. In this paper, we provide a
methodology that is able to detect unusual events from CDR data that typically
has low accuracy in terms of space and time resolution. Moreover, we introduce
a concept of unusual event that involves a large amount of people who expose an
unusual mobility behavior. Our careful consideration of the issues that come
from coarse-grained CDR data ultimately leads to a completely general framework
that can detect unusual crowd events from CDR data effectively and efficiently.
Through extensive experiments on real-world CDR data for a large city in
Africa, we demonstrate that our method can detect unusual events with 16%
higher recall and over 10 times higher precision, compared to state-of-the-art
methods. We implement a visual analytics prototype system to help end users
analyze detected unusual crowd events to best suit different application
scenarios. To the best of our knowledge, this is the first work on the
detection of unusual events from CDR data with considerations of its temporal
and spatial sparseness and distinction between user unusual activities and
daily routines.Comment: 18 pages, 6 figure
Moving Object Trajectories Meta-Model And Spatio-Temporal Queries
In this paper, a general moving object trajectories framework is put forward
to allow independent applications processing trajectories data benefit from a
high level of interoperability, information sharing as well as an efficient
answer for a wide range of complex trajectory queries. Our proposed meta-model
is based on ontology and event approach, incorporates existing presentations of
trajectory and integrates new patterns like space-time path to describe
activities in geographical space-time. We introduce recursive Region of
Interest concepts and deal mobile objects trajectories with diverse
spatio-temporal sampling protocols and different sensors available that
traditional data model alone are incapable for this purpose.Comment: International Journal of Database Management Systems (IJDMS) Vol.4,
No.2, April 201
Stigmergy-based modeling to discover urban activity patterns from positioning data
Positioning data offer a remarkable source of information to analyze crowds
urban dynamics. However, discovering urban activity patterns from the emergent
behavior of crowds involves complex system modeling. An alternative approach is
to adopt computational techniques belonging to the emergent paradigm, which
enables self-organization of data and allows adaptive analysis. Specifically,
our approach is based on stigmergy. By using stigmergy each sample position is
associated with a digital pheromone deposit, which progressively evaporates and
aggregates with other deposits according to their spatiotemporal proximity.
Based on this principle, we exploit positioning data to identify high density
areas (hotspots) and characterize their activity over time. This
characterization allows the comparison of dynamics occurring in different days,
providing a similarity measure exploitable by clustering techniques. Thus, we
cluster days according to their activity behavior, discovering unexpected urban
activity patterns. As a case study, we analyze taxi traces in New York City
during 2015
A qualitative approach to the identification, visualisation and interpretation of repetitive motion patterns in groups of moving point objects
Discovering repetitive patterns is important in a wide range of research areas, such as bioinformatics and human movement analysis. This study puts forward a new methodology to identify, visualise and interpret repetitive motion patterns in groups of Moving Point Objects (MPOs). The methodology consists of three steps. First, motion patterns are qualitatively described using the Qualitative Trajectory Calculus (QTC). Second, a similarity analysis is conducted to compare motion patterns and identify repetitive patterns. Third, repetitive motion patterns are represented and interpreted in a continuous triangular model. As an illustration of the usefulness of combining these hitherto separated methods, a specific movement case is examined: Samba dance, a rhythmical dance will? many repetitive movements. The results show that the presented methodology is able to successfully identify, visualize and interpret the contained repetitive motions
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