Do Human Mobility Network Analyses Produced from Different Location-based Data Sources Yield Similar Results across Scales?
arxiv(2022)
摘要
The burgeoning availability of sensing technology and location-based data is
driving the expansion of analysis of human mobility networks in science and
engineering research, as well as in epidemic forecasting and mitigation, urban
planning, traffic engineering, emergency response, and business development.
However, studies employ datasets provided by different location-based data
providers, and the extent to which the human mobility measures and results
obtained from different datasets are comparable is not known. To address this
gap, in this study, we examined three prominent location-based data sources:
Spectus, X-Mode, and Veraset to analyze human mobility networks across
metropolitan areas at different scales: global, sub-structure, and microscopic.
Dissimilar results were obtained from the three datasets, suggesting the
sensitivity of network models and measures to datasets. This finding has
important implications for building generalized theories of human mobility and
urban dynamics based on different datasets. The findings also highlighted the
need for ground-truthed human movement datasets to serve as the benchmark for
testing the representativeness of human mobility datasets. Researchers and
decision-makers across different fields of science and technology should
recognize the sensitivity of human mobility results to dataset choice and
develop procedures for ground-truthing the selected datasets in terms of
representativeness of data points and transferability of results.
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