Optimal Visualization Method for Analyzing Restaurant Ratings: Heat Map vs Kernel Density
Hello everyone,
I'm currently working on analyzing a dataset provided by a CRE (Commercial Real Estate) client, consisting of approximately 200 data points representing individual restaurants, each graded from A to F (with A being the highest and F the lowest rating). My task is to create a visualization that effectively highlights the distribution of higher-rated establishments across the area.
To tackle this, I initially experimented with a weighted heat map approach. I assigned numerical values to each grade (e.g., A=5, B=4, and so on down to F=1) and utilized this as the 'Weight field' in the heat map symbology. Playing around with the radius and method parameters within the tool gave me some insights, but I'm wondering if there might be a more insightful way to present this data.
In my quest for alternatives, I stumbled upon the Kernel Density tool. Although less familiar with it, I decided to give it a try, using the same attribute as the 'Population' field. However, I'm uncertain if this method is the most suitable for capturing the spatial distribution of higher-rated stores across the landscape.
I would greatly appreciate any suggestions or insights on alternative methods that could provide a clearer understanding of how these higher-rated restaurants are dispersed throughout the area. Your expertise would be invaluable in refining this analysis and delivering a more impactful visualization for our client.
Thank you in advance for your assistance! I am providing screenshots below for a visual.
