Why Coffee?

For most of us, coffee has become a non-negotiable in our morning routine.

But in a lot of urban planning studies, the number of coffee shops has become indicative of the level of development of a place or a city. It is not really surprising as coffee shops are avenues for business and client meetings and are found in business districts where busy employees need quick access to a coffee fix.

As such, just by knowing the number of coffee shops in a location, may tell us something valuable. But how do we proceed?

How Data Science Can Improve This Process

So now that we know the importance of this data, how then should we collect it?

Some coffee chains have a list of locations that are found on their websites and we can get them manually just by copying the info and geocoding the data ourselves.

But this would result in a tedious process that is prone to error and may take a longer time to clean.

This is where the magic of data science enters. For this tutorial, let’s use Google API to collect the data and Folium to visualize our data in HTML format.

_Credits: We borrowed some codes from __artemrys _and modified it to fir out purpose for the Philippines’ cities and to return more than the count.

#google #philippines #python #geospatial #folium