238 research outputs found
A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage
A key aspect of a sustainable urban transportation system is the
effectiveness of transportation policies. To be effective, a policy has to
consider a broad range of elements, such as pollution emission, traffic flow,
and human mobility. Due to the complexity and variability of these elements in
the urban area, to produce effective policies remains a very challenging task.
With the introduction of the smart city paradigm, a widely available amount of
data can be generated in the urban spaces. Such data can be a fundamental
source of knowledge to improve policies because they can reflect the
sustainability issues underlying the city. In this context, we propose an
approach to exploit urban positioning data based on stigmergy, a bio-inspired
mechanism providing scalar and temporal aggregation of samples. By employing
stigmergy, samples in proximity with each other are aggregated into a
functional structure called trail. The trail summarizes relevant dynamics in
data and allows matching them, providing a measure of their similarity.
Moreover, this mechanism can be specialized to unfold specific dynamics.
Specifically, we identify high-density urban areas (i.e hotspots), analyze
their activity over time, and unfold anomalies. Moreover, by matching activity
patterns, a continuous measure of the dissimilarity with respect to the typical
activity pattern is provided. This measure can be used by policy makers to
evaluate the effect of policies and change them dynamically. As a case study,
we analyze taxi trip data gathered in Manhattan from 2013 to 2015.Comment: Preprin
Visual analytics of location-based social networks for decision support
Recent advances in technology have enabled people to add location information to social networks called Location-Based Social Networks (LBSNs) where people share their communication and whereabouts not only in their daily lives, but also during abnormal situations, such as crisis events. However, since the volume of the data exceeds the boundaries of human analytical capabilities, it is almost impossible to perform a straightforward qualitative analysis of the data. The emerging field of visual analytics has been introduced to tackle such challenges by integrating the approaches from statistical data analysis and human computer interaction into highly interactive visual environments. Based on the idea of visual analytics, this research contributes the techniques of knowledge discovery in social media data for providing comprehensive situational awareness. We extract valuable hidden information from the huge volume of unstructured social media data and model the extracted information for visualizing meaningful information along with user-centered interactive interfaces. We develop visual analytics techniques and systems for spatial decision support through coupling modeling of spatiotemporal social media data, with scalable and interactive visual environments. These systems allow analysts to detect and examine abnormal events within social media data by integrating automated analytical techniques and visual methods. We provide comprehensive analysis of public behavior response in disaster events through exploring and examining the spatial and temporal distribution of LBSNs. We also propose a trajectory-based visual analytics of LBSNs for anomalous human movement analysis during crises by incorporating a novel classification technique. Finally, we introduce a visual analytics approach for forecasting the overall flow of human crowds
Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information
Predicting when and where events will occur in cities, like taxi pick-ups,
crimes, and vehicle collisions, is a challenging and important problem with
many applications in fields such as urban planning, transportation optimization
and location-based marketing. Though many point processes have been proposed to
model events in a continuous spatio-temporal space, none of them allow for the
consideration of the rich contextual factors that affect event occurrence, such
as weather, social activities, geographical characteristics, and traffic. In
this paper, we propose \textsf{DMPP} (Deep Mixture Point Processes), a point
process model for predicting spatio-temporal events with the use of rich
contextual information; a key advance is its incorporation of the heterogeneous
and high-dimensional context available in image and text data. Specifically, we
design the intensity of our point process model as a mixture of kernels, where
the mixture weights are modeled by a deep neural network. This formulation
allows us to automatically learn the complex nonlinear effects of the
contextual factors on event occurrence. At the same time, this formulation
makes analytical integration over the intensity, which is required for point
process estimation, tractable. We use real-world data sets from different
domains to demonstrate that DMPP has better predictive performance than
existing methods.Comment: KDD 1
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