Do Human Mobility Network Analyses Produced from Different Location-based Data Sources Yield Similar Results across Scales?
Chia-Wei Hsu, Chenyue Liu, Kiet Minh Nguyen, Yu-Heng Chien, Ali Mostafavi
TL;DR
This study addresses whether mobility network analyses derived from different location-based data sources yield similar results across scales. It constructs daily census-tract level mobility networks from Spectus, X-Mode, and Veraset for 11 US counties in February 2020 and compares them at macroscopic, substructure, and microscopic levels using DTW, motif analysis, and cosine similarity. The results show that similarity across datasets is partial and highly scale-dependent; Spectus and X-Mode align for some global measures, Spectus and Veraset align for certain micro metrics, while X-Mode often diverges in motif analyses, underscoring dataset-specific biases. The study highlights the need for ground-truth movement datasets and multi-dataset benchmarking to enable generalizable theories of human mobility and informed decision-making in urban planning and epidemic forecasting.
Abstract
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.