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Massive Trajectory Data Based on Patterns of Life

Published: 22 December 2023 Publication History

Abstract

Individual human location trajectory and check-in data have been the driving force for human mobility research in recent years. However, existing human mobility datasets are very limited in size and representativeness. For example, one of the largest and most commonly used datasets of individual human location trajectories, GeoLife, captures fewer than two hundred individuals. To help fill this gap, this Data and Resources paper leverages an existing data generator based on fine-grained simulation of individual human patterns of life to produce large-scale trajectory, check-in, and social network data. In this simulation, individual human agents commute between their home and work locations, visit restaurants to eat, and visit recreational sites to meet friends. We provide large datasets of months of simulated trajectories for two example regions in the United States: San Francisco and New Orleans. In addition to making the datasets available, we also provide instructions on how the simulation can be used to re-generate data, thus allowing researchers to generate the data locally without downloading prohibitively large files.

References

[1]
N. Armenatzoglou, R. Ahuja, and D. Papadias. Geo-social ranking: functions and query processing. VLDB Journal, 24(6):783--799, 2015.
[2]
N. Armenatzoglou, S. Papadopoulos, and D. Papadias. A general framework for geo-social query processing. Proc. of the VLDB Endowment, 6(10):913--924, 2013.
[3]
P. Biltgen, T. Bacastow, T. Kaye, and J. Young. Activity-based intelligence: Understanding patterns-of-life. USGIFs State & Future of GEOINT Report, 2017.
[4]
C. Guo, B. Yang, O. Andersen, C. S. Jensen, and K. Torp. Ecomark 2.0: empowering eco-routing with vehicular environmental models and actual vehicle fuel consumption data. GeoInformatica, 19:567--599, 2015.
[5]
M. Haklay and P. Weber. Openstreetmap: User-generated street maps. IEEE Pervasive computing, 7(4):12--18, 2008.
[6]
B. Hong, B. J. Bonczak, A. Gupta, and C. E. Kontokosta. Measuring inequality in community resilience to natural disasters using large-scale mobility data. Nature communications, 12(1):1870, 2021.
[7]
J. S. Kim, H. Jin, H. Kavak, O. C. Rouly, A. Crooks, D. Pfoser, C. Wenk, and A. Züfle. Location-based social network data generation based on patterns of life. In MDM, pages 158--167, 2020.
[8]
J. J. Levandoski, M. Sarwat, A. Eldawy, and M. F. Mokbel. Lars: A location-aware recommender system. In ICDE, pages 450--461. IEEE, 2012.
[9]
M. Li, R. Westerholt, H. Fan, and A. Zipf. Assessing spatiotemporal predictability of lbsn: a case study of three foursquare datasets. GeoInformatica, pages 1--21, 2016.
[10]
X. Liu, Y. Liu, K. Aberer, and C. Miao. Personalized point-of-interest recommendation by mining users' preference transition. In CIKM, pages 733--738. ACM, 2013.
[11]
Y. Liu, T.-A. N. Pham, G. Cong, and Q. Yuan. An experimental evaluation of point-of-interest recommendation in location-based social networks. Proc. VLDB Endowment, 10(10):1010--1021, 2017.
[12]
A. H. Maslow. A theory of human motivation. Psychological review, 50(4):370, 1943.
[13]
M. F. Mokbel, L. Xiong, and D. Zeinalipour-Yazti. Introduction to the special issue on contact tracing. ACM Transactions on Spatial Algorithms and Systems (TSAS), 8(2):1--2, 2022.
[14]
OpenStreetMap contributors. Overpass turbo. https://overpass-turbo.eu/, 2023.
[15]
M. A. Saleem, X. Xie, and T. B. Pedersen. Scalable processing of location-based social networking queries. In MDM, volume 1, pages 132--141. IEEE, 2016.
[16]
D. Wang, Y. Fu, P. Wang, B. Huang, and C.-T. Lu. Reimagining city configuration: Automated urban planning via adversarial learning. In ACM SIGSPATIAL, pages 497--506, 2020.
[17]
H. Yuan and G. Li. A survey of traffic prediction: from spatio-temporal data to intelligent transportation. Data Science and Engineering, 6:63--85, 2021.
[18]
J. Yuan, Y. Zheng, C. Zhang, W. Xie, X. Xie, G. Sun, and Y. Huang. T-drive: driving directions based on taxi trajectories. In ACM SIGSPATIAL, pages 99--108. ACM, 2010.
[19]
Y. Zheng, X. Xie, W.-Y. Ma, et al. Geolife: A collaborative social networking service among user, location and trajectory. IEEE Data Eng. Bull., 33(2):32--39, 2010.
[20]
A. Züfle, C. Wenk, D. Pfoser, A. Crooks, J.-S. Kim, H. Kavak, U. Manzoor, and H. Jin. Urban life: a model of people and places. Computational and Mathematical Organization Theory, 29(1):20--51, 2023.

Cited By

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  • (2024)Encoding Agent Trajectories as Representations with Sequence TransformersProceedings of the 7th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery10.1145/3687123.3698284(38-49)Online publication date: 29-Oct-2024
  • (2024)Data and Resources for Combining Point of Interest Semantics, Locations, and Road NetworksProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691300(705-708)Online publication date: 29-Oct-2024
  • (2024)Trajectory Anomaly Detection with Language ModelsProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691257(208-219)Online publication date: 29-Oct-2024
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cover image ACM Conferences
SIGSPATIAL '23: Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems
November 2023
686 pages
ISBN:9798400701689
DOI:10.1145/3589132
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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Published: 22 December 2023

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Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

View all
  • (2024)Encoding Agent Trajectories as Representations with Sequence TransformersProceedings of the 7th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery10.1145/3687123.3698284(38-49)Online publication date: 29-Oct-2024
  • (2024)Data and Resources for Combining Point of Interest Semantics, Locations, and Road NetworksProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691300(705-708)Online publication date: 29-Oct-2024
  • (2024)Trajectory Anomaly Detection with Language ModelsProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691257(208-219)Online publication date: 29-Oct-2024
  • (2024)In Silico Human Mobility Data Science: Leveraging Massive Simulated Mobility Data (Vision Paper)ACM Transactions on Spatial Algorithms and Systems10.1145/367255710:2(1-27)Online publication date: 3-Jul-2024
  • (2024)Source Localization for Cross Network Information DiffusionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671624(5419-5429)Online publication date: 25-Aug-2024
  • (2024)HumoNet: A Framework for Realistic Modeling and Simulation of Human Mobility Network2024 25th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM61037.2024.00042(185-194)Online publication date: 24-Jun-2024

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