Exploring Economic Sectoral Dynamics Through High-resolution Mobility Data

• URL: http://arxiv.org/abs/2506.13985v1
• Date: Mon, 16 Jun 2025 20:38:24 GMT
• Title: Exploring Economic Sectoral Dynamics Through High-resolution Mobility Data
• Authors: Timothy F Leslie, Hossein Amiri, Andreas Züfle,
• Abstract summary: We present a comprehensive dataset capturing patterns of human mobility across the United States from January 2019 to January 2023.<n>The dataset reports visits, travel, and time spent at public locations organized by economic sector for approximately 12 million Points of Interest (POIs)<n>By disaggregating patterns across different types of businesses, it provides valuable insights for researchers in economics, urban studies, and public health.
• Score: 0.22120851074630168
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a comprehensive dataset capturing patterns of human mobility across the United States from January 2019 to January 2023, based on anonymized mobile device data. Aggregated weekly, the dataset reports visits, travel distances, and time spent at public locations organized by economic sector for approximately 12 million Points of Interest (POIs). This resource enables the study of how mobility and economic activity changed over time, particularly during major events such as the COVID-19 pandemic. By disaggregating patterns across different types of businesses, it provides valuable insights for researchers in economics, urban studies, and public health. To protect privacy, all data have been aggregated and anonymized. This dataset offers an opportunity to explore the dynamics of human behavior across sectors over an extended time period, supporting studies of mobility, resilience, and recovery.

Related papers

• The NetMob25 Dataset: A High-resolution Multi-layered View of Individual Mobility in Greater Paris Region [64.30214722988666]
  This paper describes the survey design, collection protocol, processing methodology, and characteristics of the released dataset.<n>The dataset includes three components: (i) an Individuals database describing demographic, socioeconomic, and household characteristics; (ii) a Trips database with over 80,000 annotated displacements including timestamps, transport modes, and trip purposes; and (iii) a Raw GPS Traces database comprising about 500 million high-frequency points.
  arXiv  Detail & Related papers  (2025-06-06T09:22:21Z)
• Investigating Vulnerabilities of GPS Trip Data to Trajectory-User Linking Attacks [49.1574468325115]
  We propose a novel attack to reconstruct user identifiers in GPS trip datasets consisting of single trips.<n>We show that the risk of re-identification is significant even when personal identifiers have been removed.<n>Further investigations indicate that users who frequently visit locations that are only visited by a small number of others tend to be more vulnerable to re-identification.
  arXiv  Detail & Related papers  (2025-02-12T08:54:49Z)
• Generative Models for Synthetic Urban Mobility Data: A Systematic Literature Review [44.99833362998488]
  This systematic review provides a structured comparative overview of the current state of this heterogeneous, active field of research. A special focus is put on the applicability of the reviewed models in practice.
  arXiv  Detail & Related papers  (2024-07-12T11:54:29Z)
• Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis [11.90100976089832]
  We develop a novel generative deep learning approach for human mobility modeling and synthesis. It incorporates both activity patterns and location trajectories using open-source data. The model can be fine-tuned with local data, allowing it to adapt to accurately represent mobility patterns across diverse regions.
  arXiv  Detail & Related papers  (2024-05-24T02:04:10Z)
• Ethical and Privacy Considerations with Location Based Data Research [1.9388567720411736]
  We review a vast corpus of scientific work on human mobility and how ethics and privacy were considered. We demonstrate that these ever growing collections, while enabling new and insightful studies, have not all consistently followed a pre-defined set of guidelines regarding acceptable practices in data governance.
  arXiv  Detail & Related papers  (2024-02-11T14:50:32Z)
• Dataset Bias in Human Activity Recognition [57.91018542715725]
  This contribution statistically curates the training data to assess to what degree the physical characteristics of humans influence HAR performance. We evaluate the performance of a state-of-the-art convolutional neural network on two HAR datasets that vary in the sensors, activities, and recording for time-series HAR.
  arXiv  Detail & Related papers  (2023-01-19T12:33:50Z)
• On Inferring User Socioeconomic Status with Mobility Records [61.0966646857356]
  We propose a socioeconomic-aware deep model called DeepSEI. The DeepSEI model incorporates two networks called deep network and recurrent network. We conduct extensive experiments on real mobility records data, POI data and house prices data.
  arXiv  Detail & Related papers  (2022-11-15T15:07:45Z)
• A deep learning framework to generate realistic population and mobility data [5.180648702293017]
  Census and Household Travel Survey datasets are regularly collected from households and individuals. These datasets often represent a limited sample of the population due to privacy concerns or are given aggregated. We propose a framework to generate a synthetic population that includes both socioeconomic features (e.g., age, sex, industry) and trip chains (i.e., activity locations)
  arXiv  Detail & Related papers  (2022-11-14T14:05:09Z)
• Tracking the State and Behavior of People in Response to COVID-1 19 Through the Fusion of Multiple Longitudinal Data Streams [2.477349483168562]
  We describe a rich panel dataset of active and passive data from U.S. residents collected between August 2020 and July 2021. Such a dataset allows important research questions to be answered; for example, to determine the factors underlying the heterogeneous behavioral responses to COVID-19 restrictions imposed by local governments.
  arXiv  Detail & Related papers  (2022-09-23T18:49:23Z)
• Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy [61.60099467888073]
  We show how linking administrative data can enable auditing mobility data for bias. We show that older and non-white voters are less likely to be captured by mobility data. We show that allocating public health resources based on such mobility data could disproportionately harm high-risk elderly and minority groups.
  arXiv  Detail & Related papers  (2020-11-14T02:04:14Z)
• Urban Sensing based on Mobile Phone Data: Approaches, Applications and Challenges [67.71975391801257]
  Much concern in mobile data analysis is related to human beings and their behaviours. This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
  arXiv  Detail & Related papers  (2020-08-29T15:14:03Z)
• Country-wide mobility changes observed using mobile phone data during COVID-19 pandemic [5.402663611963239]
  In March 2020, the Austrian government introduced a widespread lock-down in response to the COVID-19 pandemic. Here we assess the effect of the lock-down quantitatively for all regions in Austria using near-real-time anonymized mobile phone data.
  arXiv  Detail & Related papers  (2020-08-23T16:00:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.