# A Web-Based Application for Smart City Data Analysis and Visualization
## 5 Results
### Discussion  
As innovative city systems increasingly rely on sensor networks and data-driven applications, it is vital to acknowledge the limitations and responsibilities of this technological evolution. While the presented application demonstrates the analytical and practical value of visualizing urban sensor data, several underlying challenges warrant further attention, including data quality, and ethical safeguards, as well as sustainable implementation. One of the most pressing concerns is data inconsistency. Real-world sensor deployments are subject to calibration errors, environmental interference, and device degradation over time. In our datasets, discrepancies have been documented, e.g., speed sensors are found to overestimate vehicle velocities by an average of 11.8 km/h due to poor calibration [68]. Similar discrepancies are observed in counting sensors, which recorded approximately 15% fewer vehicles than validated traffic counting stations, often misclassifying passenger vehicles as vans. These issues highlight well-documented challenges in real-world sensor deployments, including device drift, environmental interference, and variation between low-cost and reference-grade instruments. Such inconsistencies underscore the importance of regular calibration, cross-validation with certified instruments, and transparent reporting of sensor limitations in smart city infrastructures. Likewise, uneven sensor distribution may cause specific neighborhoods or conditions to be overrepresented in policy decisions, while others remain under-monitored. Moreover, data gaps due to power interruptions or connectivity issues require imputation, which can never fully substitute for original observations. Under environmental conditions, such as humidity or temperature fluctuations, sensor accuracy can be further distorted. Lastly, ethical considerations are equally vital [70]. While the data used in this system are primarily aggregated and \begin{table}
\begin{tabular}{l c c} \hline
**Sensor-Pollutant** & **MAE** & **RMSE** \\ \hline A2 Hard NO\({}_{2}\) & 5.374 & 8.049 \\ Feld NO\({}_{2}\) & 4.794 & 6.746 \\ G125 O\({}_{3}\) & 4.922 & 6.083 \\ G107 O\({}_{3}\) & 4.699 & 5.865 \\ StJohanns O\({}_{3}\) & 4.515 & 5.707 \\ A2 Hard O\({}_{3}\) & 4.301 & 5.653 \\ G131 O\({}_{3}\) & 4.534 & 5.598 \\ Feld O\({}_{3}\) & 3.807 & 4.945 \\ StJohanns NO\({}_{2}\) & 3.403 & 4.832 \\ G131 NO\({}_{2}\) & 3.178 & 4.689 \\ G125 NO\({}_{2}\) & 2.652 & 3.926 \\ G107 NO\({}_{2}\) & 2.508 & 3.783 \\ G131 PM\({}_{2.5}\) & 1.827 & 2.895 \\ G125 PM\({}_{2.5}\) & 1.781 & 2.580 \\ Feld PM\({}_{2.5}\) & 1.757 & 2.428 \\ G107 PM\({}_{2.5}\) & 1.729 & 2.372 \\ A2 Hard PM\({}_{2.5}\) & 1.704 & 2.327 \\ StJohanns PM\({}_{2.5}\) & 1.667 & 2.302 \\ \hline \end{tabular}
\end{table} 
Table 2: Imputation accuracy per sensor and pollutant (air quality). \begin{table}
\begin{tabular}{l c c c} \hline
**Endpoint** & **Execution Time (s)** & **Memory Start (MB)** & **Memory Increase (MB)** \\ \hline get\_spso\_data & 0.01 & 165.12 & – \\ get\_echarging\_data & 0.13 & 183.73 & 62.21 \\ get\_corr\_matrix & 0.46 & 165.10 & 84.09 \\ get\_road\_data & 0.66 & 165.04 & 135.11 \\ get\_vehicle\_data & 2.69 & 165.11 & 419.95 \\ get\_soundHz\_data & 8.67 & 180.53 & 419.28 \\ get\_parking\_data & 0.51 & 388.28 & 2.43 \\ \hline \end{tabular}
\end{table} 
Table 3: Performance evaluation of selected API endpoints. anonymized, the increasing deployment of high-resolution sensors, such as surveillance cameras or license plate readers, raises significant concerns around privacy and potential misuse [71]. In this article, privacy was a central design priority, as the parking monitoring system relied on a deliberately low-resolution camera incapable of capturing personally identifiable features, i.e., faces or license plates. This aligns with the European Data Protection Board guidance [72], which emphasizes data minimization and purpose limitation in the context of urban surveillance.