# A Web-Based Application for Smart City Data Analysis and Visualization
## 5 Results
### Evaluation  
The evaluation of the system is twofold: one aspect focuses on the accuracy of the imputation process, while the other addresses performance in terms of execution time and memory usage. To assess the accuracy of the imputation methods, we employed two widely used error metrics: root mean squared error (RMSE) and mean absolute error (MAE) per sensor data. \[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{t=1}^{N}(Y_{t}-\hat{Y}_{t})^{2}}\]  
\[\text{MAE}=\frac{1}{N}\sum_{t=1}^{N}|Y_{t}-\hat{Y}_{t}|\]  
where \(N\) represents the sample size, \(Y_{i}\) is the original data value, and \(\hat{Y}_{i}\) is the corresponding imputed value. Low RMSE and MAE values suggest high fidelity between imputed and original observations. Table 2 summarizes the per-sensor error metrics for NO\({}_{2}\), O\({}_{3}\), and PM\({}_{2.5}\). The results show consistent imputation performance across sensor locations, with RMSE values ranging from approximately 2.30 to 8.05, depending on the pollutant and sensor variability. PM\({}_{2.5}\) exhibited the lowest average error, whereas NO\({}_{2}\) showed higher error variance across sites, likely due to greater fluctuation and sensor-specific noise. Although the overall performance of the imputation model is satisfactory, some error values, particularly in NO\({}_{2}\) estimates, are relatively higher. This can be attributed to several factors. First, the imputation process was applied directly to the continuous data without categorizing values into ranges, viz., low, medium, and high pollution levels, which may have limited the model's ability to adapt to different data regimes. Second, no outlier removal was performed before imputation, which could have introduced noise, especially in sensors exposed to irregular patterns or anomalies. Additionally, pollutants such as NO\({}_{2}\) often exhibit high temporal variability and sensitivity to localized conditions (e.g., traffic bursts, wind), making accurate estimation more difficult. Sensor-level noise, hardware inconsistencies, and unmeasured external influences, such as weather and topography, may also contribute to the observed discrepancies. Despite these challenges, the method demonstrated consistent accuracy across most sensors and pollutants, particularly for PM\({}_{2.5}\), validating its robustness in real-world environmental data scenarios. Figure 12: Daily number of vehicles vs. average speed. This plot displays the daily vehicle count (blue bars, left axis) in conjunction with the average vehicle speed (orange line, right axis). Dashed horizontal lines indicate median values. Although vehicle volume remains relatively stable throughout the period, fluctuations in speed are observed, particularly during peak periods of congestion or reduced flow. The visualization aids in understanding traffic dynamics High vehicle counts correspond to lower average speeds, indicating potential congestion or the effects of traffic regulation. To evaluate system responsiveness and resource usage, we measured the execution time and memory consumption of key endpoints under typical usage. As shown in Table 3, the response times range from 0.01 s (e.g., static metadata) to 8.67 s (e.g., high-resolution audio frequency data). The corresponding memory usage ranged from as low as 2.4 MB to over 400 MB, depending on data complexity and processing requirements. These results indicate that caching and data downsampling contribute significantly to maintaining low latency and acceptable memory footprint across diverse data sources. Moreover, while no formal usability testing was performed, internal evaluations confirmed consistent functionality and responsiveness across various devices, reflecting the system's design focus on accessibility and multi-stakeholder usability. Figure 14: Histogram showing the distribution of electric vehicle charging session durations. The data indicate that most sessions are relatively short, with a high frequency of charges under 200 min.