# A Web-Based Application for Smart City Data Analysis and Visualization
## 2 Related Work
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Sensor Type** & **Data Output** & **Primary Applications** \\ \hline Air Quality Sensors & \begin{tabular}{l} NO2, O3, CO, PM2,5, PM10 \\ concentrations (\(\mathrm{\SIUnitSymbolMicro g/m^{3}}\)) \\ \end{tabular} & \begin{tabular}{l} Pollution monitoring, emissions \\ tracking, health risk alerts, \\ environmental policy [5] \\ \end{tabular} \\ \hline Traffic and Mobility Sensors & \begin{tabular}{l} Vehicle counts, speed, occupancy, \\ pedestrian flow \\ \end{tabular} & \begin{tabular}{l} Traffic optimization, congestion \\ management, safety enforcement, \\ flow analysis [6] \\ \end{tabular} \\ \hline Noise Sensors & \begin{tabular}{l} Sound level (dB), frequency \\ spectrum \\ \end{tabular} & \begin{tabular}{l} Noise mapping \\ compliance, quality-of-life \\ assessment, urban planning [7] \\ \end{tabular} \\ \hline Parking and EV Sensors & \begin{tabular}{l} Availability status, occupancy, \\ energy transferred (kWh) \\ \end{tabular} & \begin{tabular}{l} Optimizing parking space \\ utilization and monitoring EV \\ charging infrastructure [8] \\ \end{tabular} \\ \hline Weather Sensors & \begin{tabular}{l} Temperature, humidity, wind, \\ rainfall, UV index \\ \end{tabular} & \begin{tabular}{l} Microclimate modeling, disaster \\ preparedness, energy forecasting, \\ agriculture support [9] \\ \end{tabular} \\ \hline Structural and Utility Sensors & \
\begin{tabular}{l} Vibration, tilt, pressure, \\ current/voltage, flow rates \\ \end{tabular} &
\begin{tabular}{l} Infrastructure health monitoring, \\ predictive maintenance, utility \\ management [11] \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Common sensor types, data outputs, and applications in smart cities. Regarding missing data, their imputation is a long-standing challenge. Broadly, in the context of data analysis, two main strategies try to address missing data. The first is to ignore or remove the missing values. The second is to apply imputation techniques in order to estimate them [53,54]. In [32], the authors deleted all missing entries, arguing that the dataset was sufficiently large and that imputation would incur unnecessary computational cost. Conversely, other studies have embraced imputation strategies [55], while others adopted more advanced machine learning techniques [56]. Beyond analytical accuracy, it is clear that missing or imputed values when represented visually can significantly affect the user's perception, interpretability, and the quality of decision-making. As presented in [57,58] the visual treatment of missing data plays a decisive role in the exploration process. Therefore, imputation quality directly affects both the statistical validity of the data and the interpretability of its visual representation, making it an essential component of any platform that supports interactive, sensor-based analytics. In alignment with these developments, our platform adopts a web-based visualization-centered approach that transforms heterogeneous sensor data streams into interactive, interpretable visual outputs designed to support both expert analysis and public engagement.