Spatio-temporal Networks: Modeling, Storing, and Querying Temporally-Detailed Roadmaps - Space-Time Integration in Geography and GIScience - page 74

Chapter 6
Spatio-temporal Networks: Modeling, Storing,
and Querying Temporally-Detailed Roadmaps

Michael R. Evans, KwangSoo Yang, Viswanath Gunturi, Betsy George,
and Shashi Shekhar

6.1 Introduction

Given a spatial network and its variations over time (e.g., time-varying travel
times on road networks) this chapter discusses how to model, query and store
spatio-temporal networks. This problem has application in several domains such
as transportation networks, emergency planning, knowledge discovery from sensor
data, and crime analysis. Adequately representing the temporal nature of spatial
networks would potentially allow us to raise interesting questions (e.g., eco-
routing, non-FIFO behavior) and find efficient solutions. In transportation networks,
travelers are often interested in finding the best time to start so that they spend the
least time on the road. Crime data analysts may be interested in finding temporal
patterns of crimes at certain locations or the routes in the network that show
significantly high crime rates. Modeling the time dependence of sensor network
data would certainly improve the process of discovering patterns such as hot
spots. In these application domains, it is often necessary to develop a model that
captures both the time dependence of the data and the underlying connectivity of
the locations. There are significant challenges in developing a model for spatio-
temporal networks. The model needs to balance storage efficiency and expressive
power and provide adequate support for the algorithms that process the data.

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