Geography Reference
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large-sample activity-travel data, often collected by government agencies, have
become more available and significantly increased the data capacity of space-time
behavior research in China. For instance, scholars have used floating-car data, smart-
card data, and mobile phone data in travel-behavior research, which contributed to
urban research and planning practices (Li et al.
2011
; Long et al.
2012
; Liu et al.
2012
).
3.2.3
Progress in Analytic Techniques
Another area of major development has occurred in the analytical techniques
adopted in Chinese space-time behavior research. In the 1990s, empirical studies
in China often adopted statistical tools to describe space-time activity patterns on
an aggregate level (e.g., Chai
1996
;Chaietal.
2002
), while Western geographers
had begun to adopt advanced statistical models and explore the geovisualization
of space-time activity patterns and accessibility measurements based on space-
time prisms. These advanced methods have been adopted in more recent empirical
literatures in China. For instance, structural equation models (SEM), nested logit
models, and other statistical tools have been used to understand how urban built
environments and institutional contexts shape individual space-time behavior (e.g.,
Zhang and Li
2006
on shopping location choices; Cao and Chai
2007
on gender
differences in time use; Zhu and Wang
2008
on consumer travel behavior; Wang and
Chai
2009
on commuting behavior; Chai et al.
2010b
on the spatial and temporal
decision making of travel behaviors; Zhao and Chai
2010
;MaandChai
2011
on trip
chaining behaviors).
Despite slower progress until early 2000s, the disaggregate-level analysis of
space-time paths has achieved major methodological breakthroughs owing to the
greater number of scholarly interactions occurring between Chinese and West-
ern geographers. Early analysis of space-time paths analysis often used a two-
dimensional diagram, with the horizontal axis indicating distance from home.
However, the rapid development of GIS techniques and high-resolution space-time
data has enabled Chinese geographers to conduct 3D GIS-based space-time path
analyses instead.
For instance, a space-time GIS toolkit named Activity Pattern Analyst (APA) has
been developed to geovisualize space-time paths, thereby allowing the exploration
of space-time activity patterns in a 3D environment (Chen et al.
2011
,seeFig.
3.2
).
The APA toolkit has six basic functions for space-time pattern exploration, including
space-time path generation, query, and segmentation, as well as activity query,
activity density, and space-time path based clustering analysis. In addition, other
computer-aided data mining methods were introduced to explore the complexity
of space-time activity patterns. For instance, Li et al. (
2009
) have adopted the
sequence alignment tool to analyze the sequencing structure of spatial and temporal
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