Inspiration
Our team wanted to understand how CCAs can encourage EV adoption among low income consumers. A 2017 UC Davis study suggested that price discrimination and market access were not the most likely impediments to adoption amongst low-income and minority groups. Our thesis is that low income consumers are more likely to live in multi-family housing without garages and lack convenient access to charging. We believe charging access is an important factor in the decision to purchase an EV.
What it does
MaatMobility helps CCAs and utilities quickly identify geographic locations where charging access is limited. We did this by developing a ranking methodology to quickly identify low-income demographic areas where charging infrastructure investment should be prioritized or incentivized.
How we built it
Our team needed to calculate a score to measure if an address is “ underserved” from an EV charging perspective. We used latitude/longitude of available public charging stations from OpenChargeMap and simulated average household income, using a Pareto distribution and poverty line data in San Mateo zipcodes from census.data.gov
Step 1: Calculate EV Charger Distance from a specified location: The map was divided into “bins” – each bin is 0.01 degree longitude and latitude (~1.1km x1.1 km2) Using OpenChargeMap data, determine how many EV chargers are present in each bin
Step 2: Simulate median HHI for each bin with a Pareto distribution. In a realistic setting, a CCA/utility user would use household income data at the customer level
Step 3: Calculate Charger Needs Score: Distance/Income, scaled to be a number between 0 - 1
Step 4: Create a visual heat map to indicate which locations in San Mateo should be prioritized for charging infrastructure investment.
Challenges we ran into
Initially, we planned to use the Google Maps API, which costs money. Other APIs we wanted to use also required developer registration and approval, which we didn’t have time for. In addition, as hackers, we didn’t have access to HHI data at the individual address level so we had to approximate it by determining the median income from census data, and then using a pareto distribution to simulate income. Ideally, a CCA or utility will already have this data as part of the customer PII.
Accomplishments that we're proud of
We were able to build a solution to address an important barrier to EV charger access, while at the same time making the best of the limited data available to us. We believe that EV charger access is an important consideration that utilities and CCAs need to take into account when planning EV charger investments.
What we learned
We learned how to appropriately scope a problem and use limited data to design a solution. We also learned how to build a static demo to show our solution within a 24hr period.
What's next for Maat Mobility
Learn from the feedback and keep building!
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