In dense datasets, important patterns often remain hidden due to overlapping points.
Traditional scatter plots struggle when thousands of data points compete for space, masking trends and creating visual noise.
Hexbin charts address this challenge by grouping overlapping points into hexagonal bins, making concentration and distribution easier to see.
Use Case: Mapping Ecommerce Order Volume by Location
This example compares a scatter plot with a hexbin chart to illustrate ecommerce order volume across New York City neighborhoods.
Key observations:
• Urban centers show concentrated activity and high order flow
• Peripheral zones reflect sparse demand patterns
• The hexbin view highlights patterns that support spatial decisions such as delivery planning, store placement, and hotspot identification
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