Typically, when we want to visualize a variable across a territory using choropleth maps, we use administrative geometries that are commonly known. For instance, if we wanted to see unemployment…

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This article provides a step-by-step explanation of how to create hexagonal maps in Python using the H3 and Plotly libraries. It discusses the advantages of using hexagons for data visualization across a territory and uses a dataset of hotels in Barcelona to demonstrate the process. The article covers data cleaning, hexagon grid generation, hotel assignment to hexagons, data grouping, and the final visualization using Plotly.

Constructing Hexagon Maps with H3 and Plotly: A Comprehensive Tutorial

Analysis Data: Barcelona City Hotel Dataset

Assignment of Each Hotel to Its Respective Hexagon

Data Grouping Based on the Variables to Be Visualized

Data Visualization: Cartographic Representation of Hotels in Barcelona Using Hexagons