Grocery Mapper

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  Upload the provided store information as an excel spreadsheet, select the food item you are looking for, and select your location.

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  The nearest best store is plotted based on the relative prices and distances of the grocery stores based on the users input.

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  Also provides the pricing information of other goods in the best store found.

Inspiration

Grocery prices have been on the rise recently, causing many families across Canada to struggle saving their money for food expenses. This app informs the consumer on where to get their groceries for the best prices, at the nearest store with those best prices. Better informing consumers on their options when buying groceries can both allow the struggling consumer to spend less on groceries, and hopefully push large grocers to amend their prices as a more informed consumer base would have a more elastic demand to raises in food prices.

What it does

The user inputs a food item they are looking for, and Grocery Finder finds the best store to find that food item using pricing and distance from that store as ranking metrics. Grocery Mapper also outputs some analytics relating to the costs of other items in the best store found if the shopper wants to shop for other items as well.

How we built it

We used MecSimCalc and python libraries such as pandas and matplotlib to implement the data extraction and display for Grocery Mapper. We accessed OpenStreetMap data using Overpass API to generate the map information.

Challenges we ran into

Learning how MecSimCalc worked caused some trouble initially with some input types interacting with the code well. However, reviewing their documentation helped a lot in debugging some of these issues and we found better ways to take inputs using MecSimCalc.

Accomplishments that we're proud of

We build a novel project using a app-builder we haven't used before. Creating an app using only python was a fun learning experience as we could focus on the functionality of our app without worrying about the convolutions of using other languages for UI and other app development features. We haven't extracted excel data before so it was an interesting experience to learn how pandas worked in extracting data from spreadsheets.

What we learned

We learned how to use python libraries such as pandas to extract spreadsheet data. We also learned how to create an app using MecSimCalc and implement their mapping feature for more functionality to our app..

What's next for Grocery Mapper

We would like to implement a web-scraping functionality to Grocery Mapper to better collect data on store prices.

Built With

• excel
• mecsimcalc
• overpass-openstreetmap
• python