Database Reference
In-Depth Information
Chapter 12
Spatiotemporal Pattern Mining: Algorithms
and Applications
Zhenhui Li
Abstract
With the fast development of positioning technology, spatiotemporal data
has become widely available nowadays. Mining patterns from spatiotemporal data
has many important applications in human mobility understanding, smart transporta-
tion, urban planning and ecological studies. In this chapter, we provide an overview
of spatiotemporal data mining methods. We classify the patterns into three categories:
(1) individual periodic pattern; (2) pairwise movement pattern and (3) aggregative
patterns over multiple trajectories. This chapter states the challenges of pattern dis-
covery, reviews the state-of-the-art methods and also discusses the limitations of
existing methods.
Keywords
Spatiotemporal data
·
Trajectory
·
Moving object
·
Data mining
1
Introduction
With the rapid development of positioning technologies, sensor networks, and on-
line social media, spatiotemporal data is now widely collected from smartphones
carried by people, sensor tags attached to animals, GPS tracking systems on cars and
airplanes, RFID tags on merchandise, and location-based services offered by social
media. While such tracking systems act as real-time monitoring platforms, analyzing
spatiotemporal data generated from these systems frames many research problems
and high-impact applications:
Understanding animal movement is important to addressing environmental chal-
lenges such as climate and land use change, bio-diversity loss, invasive species,
and infectious diseases.
Traffic patterns help people with fastest path finding based on dynamic traffic
information; automatic and early identification of traffic incidents; and safety
alerts when dangerous driving behaviors are recognized.
