Progress in computational movement analysis – towards movement data science

References

• Buchin, M., Kilgus, B., and Kölzsch, A., 2019. Group diagrams for representing trajectories. International Journal of Geographical Information Science, 2395–2400. doi:10.1080/13658816.2019.1684498
   Web of Science ®Google Scholar
• Demšar, U., et al., 2015. Analysis and visualisation of movement: an interdisciplinary review. Movement Ecology, 3 (1), 5. doi:10.1186/s40462-015-0032-y
   PubMed Web of Science ®Google Scholar
• Dodge, S., 2019. A data science framework for movement. Geographical Analysis, 1–21. https://doi.org/10.1111/gean.12212
   Web of Science ®Google Scholar
• Dodge, S., et al., 2016. Analysis of movement data. International Journal of Geographical Information Science, 30 (5), 825–834. doi:10.1080/13658816.2015.1132424
   Web of Science ®Google Scholar
• Fillekes, M.P., et al., 2019. Towards a comprehensive set of GPS-based indicators reflecting the multidimensional nature of daily mobility for applications in health and aging research. International Journal of Health Geographics, 18, 17. doi:10.1186/s12942-019-0181-0
   PubMed Web of Science ®Google Scholar
• Gao, S., et al., 2020. Mapping county-level mobility pattern changes in the United States in response to COVID-19. SIGSPATIAL Special, 12 (1), 16–26. doi:10.1145/3404111.3404115
   Google Scholar
• Graser, A., Widhalm, P., and Dragaschnig, M., 2020. The M3 massive movement model: a distributed incrementally updatable solution for big movement data exploration. International Journal of Geographical Information Science. doi:10.1080/13658816.2020.1776293
   PubMed Web of Science ®Google Scholar
• Huang, H., Cheng, Y., and Weibel, R., 2019. Transport mode detection based on mobile phone network data: A systematic review. Transportation Research Part C: Emerging Technologies, 101, 297–312. doi:10.1016/j.trc.2019.02.008
   Web of Science ®Google Scholar
• Kitchin, R., 2020. Using digital technologies to tackle the spread of the coronavirus: panacea or folly? The Programmable City Working Paper, 44 (April), 1–24.
   Google Scholar
• Kraemer, M.U.G., et al., 2020. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science, 4218 (February 2019), eabb4218.
   Google Scholar
• Laube, P., 2014. Computational movement analysis. 1st. Springer International Publishing.
   Google Scholar
• Li, M., et al., 2019. Dynamic estimation of individual exposure levels to air pollution using trajectories reconstructed from mobile phone data. International Journal of Environmental Research and Public Health, 16 (22), 4522. doi:10.3390/ijerph16224522
   Web of Science ®Google Scholar
• Li, W., et al., 2020. Understanding intra-urban human mobility through an exploratory spatiotemporal analysis of bike-sharing trajectories. International Journal of Geographical Information Science, 1–24. doi:10.1080/13658816.2020.1712401
   Web of Science ®Google Scholar
• Long, J.A., et al., 2018. Moving ahead with computational movement analysis. International Journal of Geographical Information Science, 32 (7), 1275–1281. doi:10.1080/13658816.2018.1442974
   Web of Science ®Google Scholar
• Ma, D., et al., 2020. Exploring the heterogeneity of human urban movements using geo-tagged tweets. International Journal of Geographical Information Science, 1–22. doi:10.1080/13658816.2020.1718153
   Web of Science ®Google Scholar
• McKenzie, G., Keßler, C., and Andris, C., 2019. Geospatial privacy and security. Journal of Spatial Information Science, 19 (19), 53–55.
   Google Scholar
• Miller, H.J., et al., 2019. Towards an integrated science of movement: converging research on animal movement ecology and human mobility science science. International Journal of Geographical Information Science, 33 (5), 855–876. doi:10.1080/13658816.2018.1564317
   PubMed Web of Science ®Google Scholar
• Naghizade, E., Chan, J., and Tomko, M., 2020. From small sets of GPS trajectories to detailed movement profiles: quantifying personalized trip-dependent movement diversity. International Journal of Geographical Information Science, 1–26. doi:10.1080/13658816.2020.1730849
   Web of Science ®Google Scholar
• Oliver, N., et al., 2020. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. Science Advances, 0764, eabc0764. doi:10.1126/sciadv.abc0764
   Google Scholar
• Qiang, Y. and Xu, J., 2019. Evaluating accessibility resilience of road network in natural hazard using crowdsourced traffic data. International Journal of Geographical Information Science, 1–17. doi:10.1080/13658816.2019.1694681
   Google Scholar
• Scherrer, L., et al., 2018. Travelers or locals? Identifying meaningful sub-populations from human movement data in the absence of ground truth. EPJ Data Science, 7, 1. doi:10.1140/epjds/s13688-018-0147-7
   Web of Science ®Google Scholar
• Wang, Q. and Taylor, J.E., 2016. Patterns and limitations of urban human mobility resilience under the influence of multiple types of natural disaster. PloS One, 11 (1), 1–14.
   Web of Science ®Google Scholar
• Xin, Y. and MacEachren, A.M., 2020. Characterizing traveling fans: a workflow for event-oriented travel pattern analysis using Twitter data. International Journal of Geographical Information Science, 1–20. doi:10.1080/13658816.2020.1770259
   Web of Science ®Google Scholar
• Yang, C., et al., 2020. Big Spatiotemporal Data Analytics: a research and innovation frontier. International Journal of Geographical Information Science, 34 (6), 1075–1088. doi:10.1080/13658816.2019.1698743
   Web of Science ®Google Scholar