Geography Reference
In-Depth Information
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Where are the spatio-temporal hotspots of human activities?
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Do human/vehicle movement flows exhibit different spatio-temporal
patterns in contrast to overall trips?
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How do these patterns relate to the population distribution and urban
land-use structure?
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How to conduct the spatio-temporal autocorrelation analysis?
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What is the impact of spatio-temporal granularity and uncertainty?
To cope with these research questions and problems, spatio-temporal data
mining techniques and workflows need to be studied. However, mining big
geo-data and discovering knowledge of spatial-temporal relations, patterns and
trends about the real world process are not trivial. Issues on spatio-temporal
data organization, computation and integration with visual representation and
human cognition still challenge researchers to make great efforts with
interdisciplinary knowledge. To this end, this paper aims at proposing a spatio-
temporal data processing and analytical framework which can be applied not
only in exploring dynamic mobility and intra-urban flow patterns, but also in
other human and social science research from the emerging big geospatial
datasets and computer techniques.
This paper is structured as follows: In Section 2, we will briefly discuss
some related work and propose a spatio-temporal analytical framework which
includes spatio-temporal visualization (STV), space-time kernel density
estimation (STKDE), and spatio-temporal autocorrelation-analysis (STAA) for
exploring human mobility and urban dynamic patterns. The methodology,
technical implementation of these analytics will be presented in detail. Then
we apply the framework to analyze amounts of geo-referenced mobile phone
call records in a city to reveal the spatio-temporal patterns hidden in such big
geo-data and further, to understand the complex urban dynamics.
The data processing, experiments, main findings and discussions are
presented in Section 3 and 4. We conclude the paper with summarization and
directions for further research in Section 5.
2.
S
PATIO
-T
EMPORAL
A
NALYTICAL
F
RAMEWORK
Modelling human mobility patterns and understanding dynamic urban
structures based on a large amount of GPS sensors, mobile devices, persons,
vehicles, and street networks have become a hot topic in many fields such as
urban planning, transportation, GIScience and computer science (Jiang and
