54,990 research outputs found
Estimating Attendance From Cellular Network Data
We present a methodology to estimate the number of attendees to events
happening in the city from cellular network data. In this work we used
anonymized Call Detail Records (CDRs) comprising data on where and when users
access the cellular network. Our approach is based on two key ideas: (1) we
identify the network cells associated to the event location. (2) We verify the
attendance of each user, as a measure of whether (s)he generates CDRs during
the event, but not during other times. We evaluate our approach to estimate the
number of attendees to a number of events ranging from football matches in
stadiums to concerts and festivals in open squares. Comparing our results with
the best groundtruth data available, our estimates provide a median error of
less than 15% of the actual number of attendees
Networked Individualism of Urban Residents: Discovering the Communicative Ecology in Inner-City Apartment Buildings
Certain patterns of interaction between people point to networks as an adequate conceptual model to characterise some aspects of social relationships mediated or facilitated by information and communication technology. Wellman proposes a shift from groups to networks and describes the ambivalent nature inherent in an ego-centric yet still well-connected portfolio of sociability with the term ‘networked individualism’. In this paper we use qualitative data from an action research study of social networks of residents in three inner-city apartment buildings in Australia to provide empirical grounding for the theoretical concept of networked individualism. However, this model focuses on network interaction rather than collective interaction. We propose ‘communicative ecology’ as a concept which integrates the three dimensions of "online and offline", "global and local" as well as "collective and networked". We present our research on three layers of interpretation (technical, social and discursive) to deliver a rich description of the communicative ecology we found, that is, the way residents negotiate membership, trust, privacy, reciprocity, permeability and social roles in person-to-person mediated and direct relationships. We find that residents seamlessly traverse between online and offline communication; local communication and interaction maintains a more prominent position than global or geographically dispersed communication; and residents follow a dual approach which allows them to switch between collective and networked interaction depending on purpose and context
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
Dirichlet belief networks for topic structure learning
Recently, considerable research effort has been devoted to developing deep
architectures for topic models to learn topic structures. Although several deep
models have been proposed to learn better topic proportions of documents, how
to leverage the benefits of deep structures for learning word distributions of
topics has not yet been rigorously studied. Here we propose a new multi-layer
generative process on word distributions of topics, where each layer consists
of a set of topics and each topic is drawn from a mixture of the topics of the
layer above. As the topics in all layers can be directly interpreted by words,
the proposed model is able to discover interpretable topic hierarchies. As a
self-contained module, our model can be flexibly adapted to different kinds of
topic models to improve their modelling accuracy and interpretability.
Extensive experiments on text corpora demonstrate the advantages of the
proposed model.Comment: accepted in NIPS 201
Privacy-Friendly Mobility Analytics using Aggregate Location Data
Location data can be extremely useful to study commuting patterns and
disruptions, as well as to predict real-time traffic volumes. At the same time,
however, the fine-grained collection of user locations raises serious privacy
concerns, as this can reveal sensitive information about the users, such as,
life style, political and religious inclinations, or even identities. In this
paper, we study the feasibility of crowd-sourced mobility analytics over
aggregate location information: users periodically report their location, using
a privacy-preserving aggregation protocol, so that the server can only recover
aggregates -- i.e., how many, but not which, users are in a region at a given
time. We experiment with real-world mobility datasets obtained from the
Transport For London authority and the San Francisco Cabs network, and present
a novel methodology based on time series modeling that is geared to forecast
traffic volumes in regions of interest and to detect mobility anomalies in
them. In the presence of anomalies, we also make enhanced traffic volume
predictions by feeding our model with additional information from correlated
regions. Finally, we present and evaluate a mobile app prototype, called
Mobility Data Donors (MDD), in terms of computation, communication, and energy
overhead, demonstrating the real-world deployability of our techniques.Comment: Published at ACM SIGSPATIAL 201
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
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