From Data to Map: Geospatial Visualization of Istanbul’s Daily Traffic

Abstract. Urban mobility challenges in megacities such as Istanbul require advanced approaches to effectively analyse and interpret traffic flow data. This study focuses on the visualization of spatial attribute data, specifically the number of vehicles recorded by location-based sensors across the city. Rather than relying solely on traditional visualization platforms, the proposed approach emphasizes coding-based solutions to enhance flexibility, reproducibility, and integration with artificial intelligence technologies. To achieve this, multiple visualization tools, including Folium, Mapbox, and Power BI, were applied to the same dataset, enabling a comparative evaluation of their capabilities in representing daily traffic patterns. The results highlight the advantages and limitations of each platform in terms of interactivity, spatial accuracy, and scalability. Beyond technical comparisons, the study demonstrates the potential of spatial data visualization through coding as a bridge between raw sensor data and actionable insights for urban mobility planning. This approach is particularly significant in the era of AI-driven smart city applications, where spatial data can be dynamically integrated into decision-making processes. Ultimately, the study aims to guide researchers and practitioners working with web technologies, geospatial tools, and coding frameworks by providing a methodological reference for processing, mapping, and visualizing spatial traffic data.