Description: This dataset contains the final output from the Step 2 analysis. Data Step One of this analysis primarily relies on travel data acquired through Replica.10 This dataset is produced by an activity-based model, calibrated locally with ground truth data from a diverse set of third-party source data such as mobile location data, consumer marketing data, geographic and land use data, credit card transaction data, built environment, and economic activity. Trip data provided by Replica platform includes information such as origin, destination, land use, trip purpose, and socio-demographic of the trip taker. Step Two of this analysis relies on a variety of parcel-level data from member jurisdictions, MWCOG, and DOE charging station data. This data includes existing charging stations11, Equity Emphasis Areas (EEA), Alternative Fuel Corridors (AFCs), transit stations, multifamily housing, EV Charging Justice40 Map, and MWCOG Regional Activity Centers (RACs). Step One Step One relies primarily on travel data at the census block group level. Census block groups are scored based on trip characteristics that end within that group. Trip characteristics considered in Step One include: Trip purpose Trip length Dwelling time (30 to 60 minutes, 60 to 120 minutes, and greater than 120 minutes) Income of trip Trips originating from multifamily housing Trips originating from equity emphasis areas Three different trip characteristic scenarios were completed in Step One, outlined in below. Prioritizing DCFCs with High Utilization: This scenario weights trips taken by people with higher incomes more heavily. Because people with higher incomes are also more likely to be homeowners with access to home charging, this scenario would focus on building out DCFCs to provide opportunities for public charging that would help serve a larger number of vehicles more quickly. Scoring adjustments in Step two provide a check on recommending an overbuilding of DCFCs in wealthier areas that already have ample access to public charging. Prioritizing Level 2 Chargers with Equity Focus: DCFCs require higher upfront costs for equipment, installation, and potential utility upgrades that may be needed to accommodate higher powered charging infrastructure. The cost of the electricity at the point of purchase is also higher, which can cause some service providers to cite economic infeasibility when deciding whether to cite DCFCs in communities with less EVs and lower utilization. Most Level 2 charging infrastructure will not require grid or electrical service upgrades, and the projects will have lower costs across other factors (e.g., equipment costs, electricity pricing for customers). Prioritizing Level 2 charging will mean there are fewer barriers to entry for a jurisdiction or project team looking to build out their charging network in EEAs. Prioritizing DCFCs for Multi-Family Housing: Individuals living in multi-family housing that don’t have a dedicated parking spot or reliable access to at-home charging, opportunity charging with DCFCs and workplace charging are two available options. Multi-family residents are more likely to use DCFC stations. Establishing DCFC charging hubs near higher concentrations of multi-family housing developments could provide an attractive and highly utilized alternative to on-site charging for buildings where it is challenging to install and maintain charging infrastructure. Each CBG is scored based on the percentage of regional trips it receives meeting the criteria. The final Step 1 analysis assigns each CBG in the study area a score of 1 to 6. The higher the CBG score, the more traffic a CBG experiences. For example, if a CBG with a score of 1 has a low number of trips starting or ending there, whereas a CBG with a score of 6 has a very high number of trips starting or ending there. Step TwoOnce the census block groups have been scored, individual parcels within high-scoring census block groups are evaluated based on characteristics that make that parcel more or less desirable for charging infrastructure. Those characteristics, called proximity score modifiers, include a parcel’s distance to existing charging stations, distance to multi-family housing, distance to highway on- and off-ramps, proximity to environmental justice communities, and distance to park-and-ride locations. These proximity score modifiers have been selected for the following reasons: Distance to existing charging stations. Locations that are close to existing public chargers have already begun to be built out and may have less demand. Distance to MFH. Residents of MFH typically lack access to home charging and will rely on public infrastructure to meet charging needs. Distance to highway on-ramp or off-ramp. Sites located near highway ramps are likely to attract EV drivers who are making longer trips, typically needing DCFC. Location in or near an EEA. Ensuring the benefits of EVs are spread equitably in the region is a priority. Providing access to charging infrastructure in or near EEAs can help remove barriers to EV adoption. Distance to park-and-ride locations. The distance from potential sites to the nearest public transportation stop with park-and-ride lots is calculated to determine which sites will be most useful in enabling more sustainable first and last miles of multimodal trips. Charging locations near transit stops could benefit EV ride-sharing companies or commuters that use a combination of personal vehicles and mass transit. Each proximity score modifier can increase or decrease a parcel’s overall score. If a parcel is not located near any proximity score modifier, their final score will not be influenced by these characteristics. Proximity score modifiers and their associated point values are as follows: Within ¼ mile of a Park-and-Ride Location - Parcel score increases by 1 Within ¼ mile of MFH - Parcel score increases by 1 Within ¼ mile of an EEA - Parcel score increases by 1 Within ½ mile of Existing Level 2 Charging Stations - Parcel score decreases by up to 2 points Within ½ mile of Existing DCFC Stations - Parcel score decreases by up to 4 points These factors are assessed with GIS software and compiled to modify the parcel’s charging demand score. A parcel’s final score in Step 2 is determined by the following formula: Parcel Score = [Step 1 Census Block Group Score] + [Proximity Score Modifier Total] Parcels that are better suited for charging will score higher than parcels less suitable for charging. Each high scoring parcel should be further reviewed to determine suitability for public EV charging stations such as parcel size, parking availability, facility access, potential site host partnerships, and electric utility service capacity. Local knowledge is key to understanding results.