Fast Food Restaurants Across America
Code Repository
Explore the code on the GitHub Code Repository.
Table of Contents
- Introduction
- About Dataset
- Fast Food Restaurant Map
- Effectiveness of the graph
- Requirements
- Features
- Configuration
- Authors
Introduction:
Delving into the geographical landscape of fast-food establishments in the United States, this article utilizes Folium, a dynamic mapping library, to visually unravel their distribution. Through an interactive graph, we navigate the spatial nuances, providing an insightful exploration of these culinary landscapes nationwide.
About Dataset:
This is a list of over 10,000 fast food restaurants provided by Datafiniti's Business Database. The dataset includes the restaurant's address, city, latitude and longitude coordinates, name, and more. The dataset is taken from : Kaggle - Fast Food Restaurants Dataset
Fast Food Restaurant Map
Effectiveness of the graph
- Geographic Visualization: The map provides a visual representation of the locations of food chain restaurants on a geographical map. This makes it easy to see where these restaurants are concentrated and their distribution across a specific area.
- Marker Clustering: By using marker clustering, you can avoid clutter on the map when there are many data points (restaurant locations). This feature groups nearby markers into clusters, which can help you quickly identify areas with a high density of food chain restaurants.
- Tooltip Information: You've added tooltips to the markers, which display the name of each restaurant when you hover over a marker. This allows you to access additional information about each restaurant without cluttering the map with labels, making it easy to identify individual restaurants.
- Custom Icons: You've used custom icons for the markers, which can visually distinguish food chain restaurants from other map markers, making it clear which locations are of interest.
- Zoom and Pan: The map allows users to zoom in and pan around to explore specific areas of interest in more detail, which can be useful for analyzing the density and distribution of restaurants at different levels of granularity.
Requirements
Make sure you have the following Python libraries installed:
pip install pandas folium Features
- Geospatial data extraction using OSMnx.
- Visualization with Folium.
- Marker clustering for fast food restaurants.
- Latitude and longitude information popup.
Configuration
Modify the code as needed for your specific use case. You can customize the map style, marker icons, and other parameters according to your preferences.
