Data
Electric_Vehicles

Electric_Vehicles

active ARFF Publicly available Visibility: public Uploaded 05-05-2024 by Iwo Godzwon
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Description: The Electric_Vehicle_Population_Data.csv dataset provides a comprehensive overview of the electric vehicle (EV) population within a specific region, highlighting key information about the vehicles, their owners, and associated geographical details. It encompasses a variety of attributes including Vehicle Identification Number (VIN), county and city of the owner, state, postal code, model year, make and model of the vehicle, type of electric vehicle (battery electric vehicle (BEV) or plug-in hybrid electric vehicle (PHEV)), eligibility for clean alternative fuel vehicle (CAFV) programs, electric range, base Manufacturer Suggested Retail Price (MSRP), legislative district of the vehicle location, Department of Licensing (DOL) vehicle ID, precise vehicle location coordinates, the electric utility provider, and the 2020 census tract. This dataset serves as a vital resource for understanding the distribution, diversity, and adoption trends of electric vehicles in specified areas, aiding in infrastructural planning, environmental research, and policy making aimed at promoting the use of clean alternative fuel vehicles. Attribute Description: - VIN: Unique vehicle identification number. - County/City/State/Postal Code: Geographical information of the vehicle owner. - Model Year: Year the vehicle was manufactured. - Make/Model: The brand and model of the vehicle. - Electric Vehicle Type: Specifies if the vehicle is a BEV or PHEV. - CAFV Eligibility: Indicates if the vehicle is eligible for clean alternative fuel vehicle programs. - Electric Range: Maximum distance the vehicle can travel on electric power alone. - Base MSRP: Manufacturer's suggested retail price excluding extras. - Legislative District: Legislative district where the vehicle is registered. - DOL Vehicle ID: Unique ID assigned by the Department of Licensing. - Vehicle Location: Coordinates of the vehicle's registered location. - Electric Utility: The electric utility provider for the vehicle location. - 2020 Census Tract: The census tract where the vehicle is registered. Use Case: This dataset can be particularly useful for governmental and non-governmental organizations focusing on environmental policy, urban planning, and sustainable transportation. Analysts can use this data to measure the adoption rates of electric vehicles, assess the effectiveness of CAFV incentives, and understand the geographical distribution of EVs. Urban planners can also leverage this information to optimize the placement of charging stations and to forecast the needs for electrical grid upgrades in areas with high EV concentration. Additionally, it provides a foundation for academic research into factors influencing EV adoption and the environmental impact of transitioning to electric vehicles.

17 features

VIN (1-10)nominal11060 unique values
0 missing
Countynominal193 unique values
3 missing
Citynominal726 unique values
3 missing
Statenominal44 unique values
0 missing
Postal Codenominal871 unique values
3 missing
Model Yearnominal22 unique values
0 missing
Makenominal40 unique values
0 missing
Modelnominal143 unique values
0 missing
Electric Vehicle Typenominal2 unique values
0 missing
Clean Alternative Fuel Vehicle (CAFV) Eligibilitystring3 unique values
0 missing
Electric Rangenumeric103 unique values
0 missing
Base MSRPnumeric31 unique values
0 missing
Legislative Districtnumeric49 unique values
398 missing
DOL Vehicle IDstring181458 unique values
0 missing
Vehicle Locationnominal870 unique values
8 missing
Electric Utilitystring76 unique values
3 missing
2020 Census Tractnumeric2124 unique values
3 missing

19 properties

181458
Number of instances (rows) of the dataset.
17
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
421
Number of missing values in the dataset.
403
Number of instances with at least one value missing.
4
Number of numeric attributes.
10
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
23.53
Percentage of numeric attributes.
58.82
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
1
Number of binary attributes.
5.88
Percentage of binary attributes.
0.22
Percentage of instances having missing values.
Average class difference between consecutive instances.
0.01
Percentage of missing values.

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