Introduction - Space-Time Integration in Geography and GIScience - page 3

1.1 Synopsis of Chapters Led by U.S.-Based Authors

The eight chapters by U.S.-based researchers focus on various topics on space-
time integration in GIScience research. They address issues such as the processing
and analysis of spatiotemporal big data, mining spatiotemporal data for knowledge
discovery, and modeling dynamic exposure to air pollution.
The opening chapter by Goodchild and Gong examines the distinctive and
evolving tradition of science and geographic research in the U.S. and China. The
chapter discusses issues ranging from scientific methods to data collection and
sharing to the cultural and political contexts of the U.S. and China. It suggests that
space-time research in U.S. and Chinese science will continue to converge in the
future.
The chapter by Kwan explores three notions in geographic research that will
benefit greatly through the lenses of time and human mobility: racial segregation,
environmental exposure, and accessibility. It argues for the need to expand our
analytical focus from static residential spaces to other relevant places and times
in people's everyday lives. It seeks to conceptualize and understand people's
spatiotemporal experiences based on their everyday mobility, since these complex
experiences cannot be fully understood by just looking at where people live.
Evans et al. discuss spatiotemporal networks as time-aggregated graphs useful
for reasoning, analysis and algorithm design in various social applications. The
chapter presents a spatiotemporal network model called time-aggregated graph that
represents network properties as a time series. The model reduces the temporal
replication needed in other models while retaining spatial network information.
Griffith and Chun implement an extension of the eigenvector spatial filtering
principle to eigenvector space-time filtering to analyze the space-time structure
underlying the Census mail response rates (MRRs) in the U.S. for three decennial
censuses. The results provide helpful insights into various census data collection
issues that are useful for future census data collection.
Kwan, Liu, and Vogliano present a method for improving air pollution exposure
assessment through integrating the spatiotemporal dynamics of pollution con-
centrations and detailed space-time activity patterns of individuals. The chapter
differentiates two sources of exposure to air pollution: (a) static exposure evaluated
at an individual's residential location; and (b) dynamic exposure assessed based
on the individual's locations at different times of the day. The method was imple-
mented to examine individual exposure to particulate matter PM 10 in Columbus,
Ohio (U.S.).
Song and Miller focus on the internal structure of the space-time prism. The
chapter discusses and illustrates two properties of prism interior: the probability to
visit each location within the prism interior and the velocity profile associated with
each location. Two examples are provided to demonstrate the benefits of applying
these two properties: a modified utility-based accessibility benefit measure and a
new measure for the potential environmental costs of accessibility.