## 2 Materials and Methods  
Smart cities represent a paradigm shift in sustainable urban development, tackling the intricacies of dynamic urban environments. At the core of our methodology lies a fusion of advanced data analytics, predictive modeling, and digital twin techniques. Predictive analytics stands as the linchpin, empowering cities to proactively plan for evolving challenges. Simultaneously, digital twin methodologies provide a virtual mirror of the urban landscape, enabling real-time monitoring, simulation, and analysis. Our research emphasizes the criticality of real-time monitoring, simulation, and analysis for supporting test scenarios, revealing bottlenecks, and optimizing smart city efficiency. This work uses a dataset of 144 text files that include 93,053 citizen reports retrieved through the API from the Sense City platform, a service launched by the Municipality of Patras, available to the public since 2018. This platform enables citizens to report various issues based on geographic graphical coordinates, offering a direct channel for community feedback on issues such as infrastructure, services, and various aspects of urban life. Structured in JSON format, each report includes key details like a unique report identifier (_id), a bug identifier (bug_id), the current status of the reported issue (status), geographic coordinates (loc), the reported issue's type (issue), a bilingual description in Greek and English (value_desc), and the timestamp of the report submission (reported). These details provide a comprehensive understanding of reported incidents, encompassing their nature, location, and reporting status. Figure 1 shows a sample of a single report. The report consists of:_id: \"6540 dcf9d18942 dac7c2b2e2\" (the unique identifier for the report), bug_id: 146753 (the bug identifier associated with the report), status: \"CONTROL\" (the current status of the reported issue, indicating that it has been confirmed), loc: (\"type\": \"Point\", \"coordinates\": [21.7475708, 38.2666247]) (the location information for the reported issue, specifying that it is a point on the Earth's surface with latitude 38.2666247 and longitude 21.7475708), issue: \"road-constructor\" (the type or category of the reported issue, indicating a problem related to road construction), value_desc: \"KxrtAmJn\" (this is encoded using Unicode escape sequences specifically in Greek. When decoded, it represents the text \"KxrtAmJn\" (which translates to \"Occupation of the Sidewalk\" in English. This is a common method of representing non-ASCII characters in a Unicode string using escape sequences. ), reported: \"2023-10-31T10:54:52.113Z\" (the date and time when the issue was reported, which is 31 October 2023 at 10:54:52 UTC). This report provides details about a confirmed issue related to road construction. The reported problem is the occupation of the sidewalk, and the report includes the location coordinates, status, and a timestamp of when it was reported. The bug_id and _id serve as unique identifiers for tracking and referencing the report. Geographically, the reports span various locations within Patras, reflecting the broad coverage of citizen concerns across the city. The spatial distribution offers insights into localized challenges and aids in understanding the dynamics of urban issues. The 'issue' field, categorizing reported problems, showcases the diversity of concerns raised by citizens, ranging from road construction to public space-related matters. In terms of anonymity, the provided JSON seems to be structured in a way that focuses on the details of the reported issue and its location rather than revealing personal information about the citizen making the report. More specifically, the \"bug_id\" and \"_id\" fields serve as unique identifiers for the reported issue. These identifiers are important for tracking and managing reports, and they don't reveal personal information about the individual making the report.