The EV Hub is Atlas’s approach to policy analysis. We aggregate publicly available and manually-assembled datasets from across the internet to equip users with the information needed to evaluate the EV market in the United States. Our platform gives you the ability to quickly gain insights into the effectiveness of public policies and activities, which will save you time and give you decision-relevant data.

Atlas has worked state and federal agencies, the non-profit community, and private companies to create this valuable relational database to better understand the rapidly evolving EV market.

Have an idea? Let us know if you have any suggestions on how we can make this a more valuable platform for the EV community.

Read on to learn more about how we put together the EV Hub and where we get all of this data!

What’s a Relational Database?

A relational database links different data tables by using unique “keys” incorporated into the tables. These keys allow users to compare similar information in multiple tables.

For example, a three-column table could describe a state’s annual median household income by county. In this example, the columns are year, county, and median household income, and a row would exist for each of New York’s counties for every year that data is available. Using a relational data model, this table could be compared to another table that tracked the number of Clean Passes issued by county on a monthly basis. [The Clean Pass Program is administered by the New York State Department of Transportation and was designed “to expand the use of energy-efficient vehicles, providing Long Island commuters with incentives to save money, gas and time, while reducing stress during the daily commute on the LIE.” See here. ] To connect these tables, two additional tables must be created: one table defines all the dates in the median household and EV deployment tables and the other defines all the counties in New York. See the figure below for an illustration of these connections:

In this example, dates are used as the connection for the time series data and the counties are used to connect the geolocation data. The figure below illustrates the power of this concept using Microsoft Excel. Using relational databases allows for data of different types to be compared quickly and easily. In addition, because each data type is stored in its own Excel table, data maintenance is easier.

Datasets in the EV Hub

Launch the dashboard by clicking the image above. 

Launch the dashboard by clicking the image above.

Atlas has collected datasets from publicly available sources, such as the U.S. Department of Energy, U.S. Census Bureau, and New York Department of Motor Vehicles. Below is a description of the fields in a dataset summary table, Table 1.

  • Title: the title of the dataset.
  • Data Source: the name of the organization that provided the data.
  • Description: a brief description of the dataset.
  • Category:
    • Vehicles: vehicle sales, registrations, and characteristics.
    • Geography: description of a geography (state, utility, county, city, or ZIP code).
    • Infrastructure: fueling stations and roadways.
    • Energy: gasoline and electricity prices.
    • Public Policy: policies and programs from the Alternative Fuel Data Center along with manually assembled supplements.
    • Web Resources: Research reports, websites, case studies, and other web resources.
    • Demographics: Various data fields from the American Community Survey 5-Year Data (2009-2015). Data is at the ZIP Code Tabulation Area level.
    • Mass Transit: Manually assembled data on electric bus deployment, including state and federal grants.
    • Environment: Emission sources.
  • Data Source Type:
    • Web API: data was retrieved programmatically through an application programmable interface (API).
    • Web Excel or Other: data was retrieved directly from the web through a file format understood by Power BI (Excel, CSV, JSON, etc.).
    • Manual: data was manually assembled from various sources. These data are typically retrieved from a private web address only accessible to Atlas.
  • Geography:
    • National: data only valid at a national level.
    • State: data only valid for an entire U.S. state.
    • Utility: data valid for an electric utility territory as defined by the U.S. Energy Information Administration.
    • County: data valid for a U.S. county.
    • City: data valid for a U.S. city.
    • ZIP Code: data valid for a U.S. ZIP code.
    • N/A
  • Geographic Scope: comma-separated list of valid locations for the data. Some datasets can have multiple geographic scopes.
    • Nations
    • U.S. States
    • Electric Utilities as defined U.S. Energy Information Administration
    • U.S. Counties (FIPS code)
    • Cities defined by (City, State)
    • ZIP Codes
    • N/A
  • Time Scope
    • Year: Data is available annually.
    • Month: Data is available monthly.
    • Day: Data is available for a specific day or daily.
    • N/A
  • Web Reference: the primary web address where the data can be accessed. For manually assembled data, this is the primary source of reference.