StoreView

A to-do

Inspiration

After reading the challenge for GrowthFactor, we thought this project was interesting and allowed us to further our knowledge with graphing, something we recently learned.

What it does

Our python script runs in terminal, it takes in an address, and if within a 200m radius, the dataset possesses coordinates close to it, then it will process how many impressions in that area exist. The impressions are determined through multiple factors, amongst them being visibility (determined through historical weather conditions), pedestrian traffic, vehicle traffic, storefront obstructions, and the dataset. It will calculate the impression score based on these factors.

How we built it

We used python and split the different factors into different scripts, and we used OSM and Folium to visualize the mappings of the storefront and visibility spots.

Challenges we ran into

A few merging issues, but the main issue was figuring out what formula to ultimately use to determine the impression score. A few worries we had were what would be the most representative, what sort of units, and how the different population densities would impact the overall impression score. We also wanted the scores to not be relative to the location, so we would expect urban areas to have much higher scores than rural areas simply because of population density and traffic received.

Accomplishments that we're proud of

We learned how to use OSM and Folium to visualize the line segments and pinpoint the address on a map.

What's next for StoreView

Since this submission track just asks for a python script, our next step would be to implement it into a webapp that allows users to input their addresses and we generate an impression score for them based on the factors we took into consideration.