# A Web-Based Application for Smart City Data Analysis and Visualization
## 5 Results
### Interactive Visualization Features  
Our platform features several advanced interactive elements that support flexible data analysis and personalized user experiences. These elements are designed to help users draw insights by allowing them to toggle specific data series within charts to isolate the variables or sensor stations they are most interested in (see Figures 6 and 7). This functionality reduces visual clutter and simplifies comparing selected metrics (see Figure 8). Moreover, this kind of user-driven filtering improves visual clarity and interpretability. Similarly, several dashboards allow users to reorganize, resize, or minimize data panels based on their preferences. This personalization enables more efficient workflows by allowing users to prioritize content that aligns with their interests, such as temporal trends, spatial distributions, or statistical summaries. In Figure 9, time-series plots from various sensor locations visualize pollutant levels over time, with embedded median lines offering a clear baseline for detecting deviations. A correlation heatmap is also provided to reveal interrelationships among pollutants and sensors (see Figure 10). These visuals demonstrate how the interface supports interactive exploration of complex environmental data in an accessible and comparative format.  
Figure 6: Bar plot illustrating average pollutant levels (NO\({}_{2}\), O\({}_{3}\), PM\({}_{2.5}\)) captured across various sensor locations. Error bars denote standard deviation, reflecting measurement variability. Comparative assessment supports spatial analysis of air quality across the urban network.  
Figure 7: Comparative distribution of air quality pollutants (O\({}_{3}\), PM\({}_{2.5}\), and NO\({}_{2}\)) between sensor datasets. Boxplots compare the value distributions of three major air pollutants—ozone (O\({}_{3}\)), particulate matter (PM\({}_{2.5}\)), and nitrogen dioxide (NO\({}_{2}\))—across two datasets: one collected from the primary urban sensor network (blue) and the other from comparative reference stations (orange). The O\({}_{3}\) measurements show a wider range and higher upper whisker in the primary dataset, indicating sporadically elevated ozone concentrations. PM\({}_{2.5}\) distributions are relatively similar in median values, but the comparative dataset exhibits greater variability and more outliers. For NO\({}_{2}\), the reference data show a significantly higher median and broader distribution, suggesting denser traffic exposure or local emission sources.  
Figure 8: Daily distribution of vehicle types. This horizontal stacked bar chart illustrates the composition of different vehicle categories recorded on selected days. Each bar represents the total number of vehicles detected per day, categorized into four types: cars (blue), trucks or buses (orange), bicycles or motorbikes (green), and vans or SUVs (purple). The figure highlights the predominance of standard cars in daily traffic volumes, while also capturing the recurring contribution of heavy vehicles, two-wheelers, and light commercial vehicles. This breakdown facilitates analysis of modal share trends, enabling transportation planners to assess usage diversity and evaluate infrastructure demand for each vehicle class.  
Figure 9: Time-series visualizations from three air quality sensors (e.g., G107, G125, G131) used in the Basel Smart Road project. Each chart displays pollutant levels (e.g., NO\({}_{2}\), O\({}_{3}\), PM\({}_{2.5}\)). The X-axis represents timestamps, and the Y-axis shows \(\mathrm{\SIUnitSymbolMicro m^{3}}\) concentrations. Median reference lines highlight central tendencies, helping to identify trends, spikes, and deviations across different monitoring locations.