Constructing Hexagon Maps with H3 and Plotly: A Comprehensive Tutorial

Constructing Hexagon Maps with H3 and Plotly: A Comprehensive Tutorial AI News, AI, AI tools, Amanda Iglesias Moreno, Innovation, itinai.com, LLM, t.me/itinai, Towards Data Science - Medium 🌐 Unlocking the Potential of Hexagon Maps for Data Analysis 📊 Visualizing data across a territory can be challenging due to irregular administrative boundaries and varying sizes. Hexagon maps offer a practical solution by providing balanced geometry for better regional comparisons and improved territorial coverage. In this article, we will guide you step-by-step on how to create hexagonal maps using Python libraries H3 and Plotly. 📍 Analysis Data: Barcelona City Hotel Dataset 🏨 We will be using a dataset from the open data portal of Barcelona, which contains information on hotels in the city. By visualizing the number of hotels on the hexagonal map, we can effectively analyze their distribution. 📚 Data Reading and Cleaning 🧹 To prepare the dataset for visualization, we will clean it by selecting relevant columns such as hotel name and geographical location. This step ensures that the dataset is ready for analysis. ⚙️ Hexagon Grid Generation Using H3 ⚒️ The H3 library developed by Uber enables us to generate hexagons of different sizes and resolutions. By adjusting the size and number of concentric rings of hexagons, we can cover the desired area. 🔢 Assignment of Each Hotel to Its Respective Hexagon 🏢 After creating the hexagonal grid, we need to assign each hotel to the hexagon it belongs to. This information will be stored in a new column called Hexagon_ID, allowing us to associate hotels with their respective locations. 🔀 Data Grouping Based on the Variables to Be Visualized 📊 Next, we group the data based on the hexagon ID and calculate the variables we want to visualize. For example, we can display the number of hotels in each hexagon. Additionally, we can implement a hover-over feature to view the names of hotels in each hexagon. 🎨 Data Visualization: Cartographic Representation of Hotels in Barcelona Using Hexagons 🗺️ Using Plotly, we create a choropleth map that visualizes the distribution of hotels in Barcelona. Each hexagon is colored based on the number of hotels it contains. The interactive map allows us to zoom in and explore different areas of the city. 📝 Summary 📌 Hexagon maps provide a practical alternative to traditional administrative boundaries for visualizing data across a territory. By leveraging regular geometric shapes, hexagon maps offer better regional comparisons and minimize visual bias. In this article, we have explained how to create hexagonal maps using Python libraries H3 and Plotly, using a dataset of hotels in Barcelona as an example. 🚀 Discover the Power of AI for Your Business 🤖 AI can revolutionize your company and give you a competitive edge. Here are some practical steps to get started: 1️⃣ Identify Automation Opportunities: Find key customer interaction points that can benefit from AI. 2️⃣ Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes. 3️⃣ Select an AI Solution: Choose tools that align with your needs and provide customization. 4️⃣ Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously. For AI KPI management advice, connect with us at hello@itinai.com. Stay tuned on our Telegram channel or follow us on Twitter @itinaicom for continuous insights into leveraging AI. 🔎 Spotlight on a Practical AI Solution: AI Sales Bot 🤝 Consider using the AI Sales Bot from itinai.com/aisalesbot to automate customer engagement and manage interactions across all customer journey stages. Discover how AI can redefine your sales processes and customer engagement. Explore solutions at itinai.com. 🔗 List of Useful Links 🔗 AI Lab in Telegram @aiscrumbot – free consultation Constructing Hexagon Maps with H3 and Plotly: A Comprehensive Tutorial Towards Data Science – Medium Twitter – @itinaicom