Spatiotemporal clustering of vehicle emissions, which reveals the evolution pattern of air
pollution from road traffic, is a challenging representation learning task due to the lack of …

In spatiotemporal data commonly encountered in geographical systems, biomedical signals,
and the like, each datum is composed of features comprising a spatial component and a …

With rapid industrial development, air pollution problems, especially in urban and
metropolitan centers, have become a serious societal problem and require our immediate …

Urban hotspots are regions with intensive passenger flow, sound infrastructure, and thriving
business during a certain period of time, which mirror the travel behavior of residents. Taxi …

The ability to predict human mobility, ie, transitions between a user's significant locations
(the home, workplace, etc.) can be helpful in a wide range of applications, including targeted …

Although considerable success has been achieved in urban air quality prediction (AQP) with
machine learning techniques, accurate and long-term prediction is still challenging. One of …

Predicting future frames using historical spatiotemporal data sequences is challenging and
critical, and it is receiving a lot of attention these days from academic and industrial scholars …

Clustering allows considering groups of similar data elements at a higher level of
abstraction. This facilitates the extraction of patterns and useful information from large …

The Getis-Ord Gi* statistic clustering technique was used to create a hot spot exposure map
using 14 potentially toxic elements (PTEs) found in urban dust samples in a semiarid city in …

Abstract Origin-Destination (OD) flow, as an abstract representation of the object's
movement or interaction, has been used to reveal the movement patterns of human activities …