Hyperlocal environmental data with a mobile platform in urban environments
Name
s41597-023-02425-3.pdf
Size
1.78 MB
Format
Adobe PDF
Checksum (MD5)
ea2e7ad2844cc3d4773771c93d8258e5
Author(s)
Wang, An
Mora, Simone
Machida, Yuki
deSouza, Priyanka
Paul, Sanjana
Oyinlola, Oluwatobi
Duarte, Fábio
Ratti, Carlo
Date Issued
August 5, 2023
Publisher
Springer Science and Business Media LLC
Citation
Wang, A., Mora, S., Machida, Y. et al. Hyperlocal environmental data with a mobile platform in urban environments. Sci Data 10, 524 (2023).
Version
Final published version
Abstract
Environmental data with a high spatio-temporal resolution is vital in informing actions toward tackling urban sustainability challenges. Yet, access to hyperlocal environmental data sources is limited due to the lack of monitoring infrastructure, consistent data quality, and data availability to the public. This paper reports environmental data (PM, NO2, temperature, and relative humidity) collected from 2020 to 2022 and calibrated in four deployments in three global cities. Each data collection campaign targeted a specific urban environmental problem related to air quality, such as tree diversity, community exposure disparities, and excess fossil fuel usage. Firstly, we introduce the mobile platform design and its deployment in Boston (US), NYC (US), and Beirut (Lebanon). Secondly, we present the data cleaning and validation process, for the air quality data. Lastly, we explain the data format and how hyperlocal environmental datasets can be used standalone and with other data to assist evidence-based decision-making. Our mobile environmental sensing datasets include cities of varying scales, aiming to address data scarcity in developing regions and support evidence-based environmental policymaking.
Subjects
Library and Information Sciences
Statistics, Probability and Uncertainty
Computer Science Applications
Education
Information Systems
Statistics and Probability
MIT Department
Senseable City Laboratory
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/s41597-023-02425-3
