美国COVID-19疫情期间多尺度动态人口流动数据集

数据集获取

git clone https://github.com/GeoDS/COVID19USFlows.git

curl -L -o repo.zip https://github.com/GeoDS/COVID19USFlows/archive/refs/heads/master.zip
unzip repo.zip

源站 README 摘录（使用方式）

<! PROJECT LOGO >
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<h2 align=“center”>Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the COVID-19 Epidemic</h2>
GeoDS Lab, Department of Geography, University of Wisconsin-Madison.
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<! TABLE OF CONTENTS >

Table of Contents

• Citation
• About the Project
• Data Processing and Data Descriptor
• Dataset Structure
• How to Download Data?
• Field Descriptions
• License
• Contact
• Acknowledgements

<! Citation >

Citation

If you use this dataset in your research or applications, please cite this source:

Kang, Y., Gao, S., Liang, Y. Li, M., Rao, J. and Kruse, J. Multiscale dynamic human mobility flow dataset in the U.S. during the COVID-19 epidemic. Scientific Data 7, 390 (2020). https://www.nature.com/articles/s41597-020-00734-5

@article{kang2020multiscale,
  title     = {Multiscale Dynamic Human Mobility Flow Dataset in the U.S. during the COVID-19 Epidemic},
  author    = {Kang, Yuhao and Gao, Song and Liang, Yunlei and Li, Mingxiao and Kruse, Jake},
  journal   = {Scientific Data},
  volumn    = {7},
  issue     = {390},
  pages     = {113},
  year = {2020}
}

<! ABOUT THE PROJECT >

About The Project

Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for monitoring and measuring the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the pandemic. In this data descriptor, we introduce an up-to-date multiscale dynamic human mobility flow dataset across the United States, with data starting from January 1st, 2019. By analyzing millions of anonymous mobile phone users’ visit trajectories to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scale

数据加载示例（表格/文本类）

import pandas as pd, glob, os

files = (glob.glob(os.path.join(path, "**", "*.csv"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tsv"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.xlsx"), recursive=True))
print("数据文件:", files)
df = pd.read_csv(files[0])
print(df.shape); print(df.columns.tolist()); print(df.head(3))

完整仓库：github.com/GeoDS/COVID19USFlows