# A Web-Based Application for Smart City Data Analysis and Visualization
## 2 Related Work  
Initially, smart cities were defined through telecommunications and information technologies [17]. However, most recently, newer interconnected devices and their extensive applications in urban environments have shaped the current digital city form [18]. Such modules collect different types of data (see Table 1), each associated with specific outputs [19] and urban application areas, reflecting the multifunctional part these technologies play in modern civil hubs [20]. These include air quality [5], traffic and mobility [6], noise [7], parking and EV infrastructure [8], as well as environmental conditions [9]. Overall, they encompass a diverse array of connected devices [21] from environmental sensors [22] to surveillance nodes [23] that provide insights into urban dynamics [24] and collectively establish a comprehensive data foundation supporting real-time decision-making [25], cross-domain analytics [26], and responsive urban management [27]. Preliminary research addressed the technical and architectural aspects of digital-enabled urban systems, focusing on the challenges of context-aware data acquisition [28], sensor interoperability [29], and distributed computing [30]. Newer studies have shifted toward developing advanced data analytics and visualization platforms, particularly emphasizing improving accessibility and usability for both expert and non-expert users [31]. These platforms commonly integrate sensor outputs into interactive dashboards [32], enabling real-time monitoring [33], visual analysis [34], and triggering automated responses through smart city control systems [10]. Such responses are supported by the evolution of internet-of-things infrastructures [35] and improved by machine learning and cross-domain integration frameworks [27]. Visualization is not merely a presentation layer but a functional core of these platforms. Web-based interfaces and built-in visualization tools [36], i.e., charts, heatmaps, and spatial layers are increasingly adopted to translate raw, multidimensional sensor streams into real-time visuals with a responsive interaction point [37]. This approach is particularly relevant in citizen-oriented strategies that promote transparency and community participation [38]. Since most computations occur on the client side, users can zoom in, hover over data points, and apply filters in real time without increasing backend processing demands [39]. Technologies like Plotly (v 1.58.5) and Leaflet (v 1.7.1) enable client-side rendering of rich, interactive visual content, reducing server load while improving responsiveness and user engagement [40]. In contrast to desktop-based software, web applications eliminate installation barriers [41], ensure cross-platform compatibility [42], and facilitate seamless updates via asynchronous data loading (AJAX) [43]. These features enable scalable, device-agnostic analytics [44] while accommodating frequent data updates without requiring a reload of the control panel [45]. These developments highlight a broader transition toward modular, open-source platforms that support data exploration [46], cross-domain integration [47], and reproducible analysis [48]. Consequently, modern urban environments increasingly rely on the seamless integration of digital technologies, such as artificial intelligence (AI) [49], and cloud computing [50], to enhance service delivery [50], streamline infrastructure [51], and support decision-making with robust data insights [52].