Global EV Data Initiative

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Data Sets ​

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1. Global Public Electric Vehicle Charging Station (EVCS) Data in 2024 ​

1.1 Data Description ​

  • For the US, we collected the location data of 69,677 EVCSs (with a sample rate of around 100%) from the Alternative Fuels Data Center;

  • For China, we collected the location data of 159,199 EVCSs (with a sample rate of 69.15%) from the AMAP, one of the largest online mapping service providers in China.

  • For Europe, we collected the location data of 226,253 EVCSs (with a sample rate of 69.56%) from the European Alternative Fuels Observatory and Open Charge Map.

1.2 Sample Data Download (2024) ​

You could request for the full dataset by sending us (global.ev.map@gmail.com) the Data Request Form.

2. Global Public Electric Vehicle Charging Station (EVCS) Data in 2022 ​

2.1 Data Description ​

  • For the US, we collected the location data of 46,548 EVCSs (with a sample rate of 87.02%) from the Alternative Fuels Data Center.

  • For China, we collected the location data of 73,114 EVCSs (with a sample rate of 65.64%) from the AMAP, one of the largest online mapping service providers in China.

  • For Europe, we collected the location data of 95,133 EVCSs (with a sample rate of 48.78%) from the European Alternative Fuels Observatory.

2.2 Sample Data Download (2022) ​

You could request for the full dataset by sending us (global.ev.map@gmail.com) the Data Request Form.

3. Advanced Air Mobility (AAM) Social Media Text Data (2015 – 2024) ​

3.1 Data Description ​

We collected social media data related to Advanced Air Mobility (AAM) from Twitter/X and Sina Weibo using Python-based web crawlers. The dataset includes posts from January 1, 2015, to December 31, 2024, covering keywords such as advanced air mobility, urban air mobility, eVTOL, drone taxis, urban aerial mobility, and leading AAM companies i.e., Joby Aviation and Guangzhou EHang. After data cleaning, the final dataset contains 220,552 Twitter/X posts and 69,965 Sina Weibo posts. This social media text dataset contains only the cleaned data alongside results from our AAM text mining research, including sentiment and topic analyses. The dataset includes detailed attributes including User ID, User Type, Nation, Year, Sentiment (categorized as Positive, Neutral, and Negative), Topic, and Theme (categorized as Passenger or Freight AAM), allowing social media or other relevant research and analysis. A sample of the dataset is available for download, allowing users to review the data structure and content.

3.2 Sample Data Download ​

To request access to the social media text dataset used and produced in our study, please send the completed Data Request Form to: global.ev.map@gmail.com

4. Advanced Air Mobility (AAM) Bibliometric Text Data (2015 – 2024) ​

4.1 Data Description ​

We collected bibliometric text data from two leading academic databases, including Scopus and Web of Science, covering peer-reviewed journal articles as well as conference proceedings related to AAM. The dataset spans from January 1, 2015, to December 31, 2024, and was compiled using a comprehensive search query including terms such as advanced air mobility, urban air mobility, eVTOL, drone taxi, and related keywords. After removing duplicates, incomplete records, and irrelevant entries, the final dataset consists of 2,548 relevant journal articles. Abstracts were selected for analysis as they effectively summarize the key objectives, methods, and findings of the scholarly works, enabling insights into thematic trends and research priorities in the AAM field. This bibliometric text dataset contains only the cleaned data alongside results from our AAM text mining research, including sentiment and topic analyses. The dataset features detailed attributes such as Keyword, User Type, Nation, Year, Sentiment (categorized as Positive, Neutral, or Negative), Topic, and Theme (classified as Passenger or Freight AAM), facilitating bibliometric and related research analyses. A sample of the dataset is available for download, allowing users to review the data structure and content.

4.2 Sample Data Download ​

To request access to the social media text dataset used and produced in our study, please send the completed Data Request Form to: global.ev.map@gmail.com