## 3 Results  
The section presents the results of the previous methodologies to analyze data derived from citizen interaction with the coordinate-based problem mapping platform of the Municipality of Patras. Within the realm of urban innovation, our digital twin application, powered by Flask, not only presents a dynamic snapshot of the city's current state but also delves into the future through predictive modeling. At the heart of this endeavor is a web of artificial intelligence (AI) techniques, predominantly machine learning, employing the randomforest classifier as a vehicle for unraveling the complexities embedded in our urban data. Trained with accuracy, this machine learning model stands as an analytical technique, adept at deciphering intricate relationships among diverse features within the city's landscape. What sets the digital twin apart is its ability to project these analyses into the future, providing a futuristic gaze into the urban landscape--a gaze extending six months ahead. These services enrich the predictive capability of the city stakeholders and position them within a long-term time frame, serving as a roadmap for future urban insights. The synergy between the Flask application and the machine learning model offers a new possibility to the leadership of the city of Patras, which before this project did not exist. In addition to visualizing the current state of the city, this application now extends its capabilities to display predictions regarding the likelihood of each reported issue occurring in specific neighborhoods. This forward-looking feature enhances the utility of the digital twin, transforming it into a valuable tool for not just understanding the present but also anticipating and preparing for the challenges that lie ahead. As the research progresses in the following paragraphs of the application presentation, it becomes clear that the digital twin, in addition to being a dynamic observer of the urban core, is also a proactive assistant in the resilience of the city. The finalization of the application unfolded through a gradual and evolutionary process, a narrative worth sharing as it underscores the evident requirement for a holistic digital twin of the city's physical layer. Preceding the development of the updated application version, incremental efforts were undertaken to address the needs stemming from the reports. These requirements center on gaining complete control over historical data and enhancing predictive capabilities for requests and areas within the city. To provide readers with a quick insight into the spatial distribution of citizen requests related to city issues based on specific coordinates, Figure 10 delineates the city's dimensions on the map, while Figure 11 elucidates the distribution of issues. It is observed that a multitude of issues have arisen from these reports, spanning a wide range of city areas where authorities are called upon to promptly manage, prioritize, and intervene to resolve the problems. For the swift resolution of these issues, a key element is the clustering of the reported issues, as illustrated by the clusters in Figure 12. The user is informed about the number of problems per area through the respective clusters. Additionally, the interactive map is enhanced by the development of a pop-up window at the final points, displaying information about the issue, the area, and the reporting date. Simultaneously, the map's functionalities are enriched through the implementation of a filter linked to the frequency of citizen-reported issues and the utilization of color-coded clusters for a more straightforward identification of each particular issue within an area (Figure 13). Users receive ongoing updates on the number of problems per region prior to the full expansion of the respective cluster. Figure 11: The spread of the reported issues in the city in the last 5 years. Figure 12: Issue clustering and information pop-up tag.