# A Web-Based Application for Smart City Data Analysis and Visualization
## 1 Introduction  
Urbanization is rapidly reshaping the global demographic landscape, with 58% of the world's population living in metropolitan areas; this number is projected to increase to 70% by 2050 [1]. As this transition has given rise to environmental pressures, demand for efficient mobility solutions, and a growing need to improve citizens' quality of life and health, the adoption of digital technologies in upcoming smart cities is accelerated [2]. At present, many modern civil hubs gather real-time data from various urban domains through interconnected intelligent sensors [3], aiming to achieve environmental monitoring and traffic control of public infrastructure [4]. These sensors predominantly measure airquality [5], traffic volume [6], sound pollution [7], parking occupancy [8], and electric vehicle (EV) charging behavior [9], facilitating the development of data analytics pipelines that support informed decision-making [10]. These data streams hold immense potential for enhancing operational efficiency and urban responsiveness [11]. Therefore, complexity increases with the volume of data, while various challenges arise, such as missing data [12], calibration inconsistencies [13], and privacy concerns [14], hindering accessibility and meaningful interpretation [15]. As a result, practical representation tools are introduced, which serve as a key enabler for transforming complex, raw data into actionable insights that are accessible and noteworthy to a wide range of stakeholders [16].  
Towards this goal, this article presents a web-based data analysis and visualization approach tailored for smart city sensor networks. Central to the proposed application is a reliable imputation process that addresses the frequent occurrence of missing values in urban sensing data, ensuring data integrity for downstream analysis. The system combines interactive charts, heatmaps, and sensor overlays, permitting users to detect temporal trends, spatial anomalies, and cross-variable correlations. Beyond its technical contributions, our strategy embraces ethical urban sensing principles and incorporates privacy-aware design decisions. Additionally, it addresses sustainability considerations through its modular and efficient architecture. Thus, this work outlines the method's design, implementation, and potential use cases, offering a helpful tool reflecting on the broader implications of data-centric smart city infrastructure. The main contributions are as follows:  
* A web-based application for thoughtful city data analysis and interactive visualization, combining a Python Flask backend and a JavaScript frontend via Plotly (v 1.58.5) and Leaflet (v 1.7.1).
* The integration of hybrid imputation methods, i.e., SARIMAX and \(k\)-NN, and random forest regression to ensure temporal consistency and cross-variable accuracy.
* An interactive map-based visualization component that supports spatial-temporal analysis through statistical aggregation, integrating privacy-aware design aligned with ethical data practices.
* Open-source ([[https://github.com/PanagiotisKara/Web-Based-Data-Analysis-and-Visualization](https://github.com/PanagiotisKara/Web-Based-Data-Analysis-and-Visualization)]([https://github.com/PanagiotisKara/Web-Based-Data-Analysis-and-Visualization](https://github.com/PanagiotisKara/Web-Based-Data-Analysis-and-Visualization)), accessed on 20 April 2025) and flexible code to facilitate use by future researchers.  
The rest of this article is organized as follows. The subsequent section presents relevant smart city infrastructures and interactive data visualization approaches, while Section 3 introduces the architecture of the proposed strategy. Section 4 provides the experimental protocol, including a description of the dataset and its backend and frontend implementations. Our results are presented in Section 5. Finally, Section 6 concludes this work.