Many spatiotemporal events can be viewed as contagions. These events implicitly
propagate across space and time by following cascading patterns, expanding their …

The spatial time series generated by city sensors allow us to observe urban phenomena like
environmental pollution and traffic congestion at an unprecedented scale. However …

Urban data is massive, heterogeneous, and spatio-temporal, posing a substantial challenge
for visualization and analysis. In this paper, we design and implement a novel visual …

We consider the setting of cascades that result from contagion dynamics on large realistic
contact networks. We address the question of whether the structural properties of a (partially) …

Causality is crucial to understanding the mechanisms behind complex systems and making
decisions that lead to intended outcomes. Event sequence data is widely collected from …

Information diffusion analysis is important in social media. In this work, we present a
coherent ego-centric and event-centric model to investigate diffusion patterns and user …

The steady growth of data from social networks has resulted in wide-spread research in a
host of application areas including transportation, health-care, customer-care and many …

Understanding and comparing the evolution of public opinions on a social media event is
important. However, such a task requires summarizing rich semantic information and an in …

As a frontier research topic in the field of scientific visualization, trajectory data visualization
extracts valuable patterns and knowledge from trajectory data for decision support via …

Air pollution has become a serious public health problem for many cities around the world.
To find the causes of air pollution, the propagation processes of air pollutants must be …