Grocial Distancing allows store managers to simulate the infectivity of the coronavirus in their stores and to design new store layouts with high capacity and low infectivity.
Inspiration: A lot of small retailers are going bankrupt because they were not prepared for the long-term shutdown. However, if more grocery stores are allowed to reopen, they might cause another coronavirus outbreak. Therefore, we wanted to create a tool that will help stores plan reopening while providing shoppers with a safe shopping environment as a part of getting back to their normal lives.
What it does: Our application evaluates the infectivity of COVID-19 in a store based on its layout and other high-impact parameters. Store managers can simulate and test different layouts of their stores to maximize capacity and minimize infectivity. The app also provides shoppers with information such as infectivity and the safe capacity limit so that they can make informed decisions about their shopping behaviors.
How we built it: The Grocial Distancing demo was built using standard web technologies, including HTML, CSS, and JavaScript. No third-party software libraries were used.
Challenges we ran into: One challenge was deciding which features would be available to the customer and store owner user bases. Initially, we planned that users would only be able to see layouts made by managers; these interactions would be overseen by an account system. However, for simplicity, all features were ultimately given to all users, including full editor and simulator capabilities. Another challenge found during design was selecting a pathfinding algorithm for implementation. We performed some research on pathfinding methods, but ultimately, we chose a light-weight approach with smoothed A*. Although this algorithm served well in the current implementation, we may need to reconsider in the future to add deeper features like customer collisions and hard wall collisions.
Accomplishments that we're proud of: We successfully implemented pathfinding and goal searching in our canvas layout. We also provided a relative metric of the infectivity of various layouts given the map and customer parameters. Finally, we implemented a intuitive model for COVID spread by measuring customer distances and increasing infectivity with less distance. From our product planning, we believe that these features should help store owners and users gain insight about shopping in the wake of COVID.
What we learned: Academic models for the spread of COVID-19 are fairly simple, while simulation outcomes return intuitive results. Even an uncomplicated model can identify potential patterns and issues.
What's next for Grocial Distancing: A key point of future development will be increasing the realism of the simulation. For example, the infection algorithm could change based on research of COVID spread. Further, with more development time, customer movement could be altered to detect collisions and interact more realistically with goals and other customers. Lastly, we also hope to coordinate Grocial Distancing with other utilities, such as Google services and data from app users. These developments could also see the return of the account system.
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