## 3 Multi-Source Data in the Construction of Smart City  
### Data Sources  
Smart city construction involves a variety of multi-source data, mainly including sensor data, social media data, citizen feedback data, and GIS data [9]. These data provide rich information resources for real estate management and urban optimization. The comparison of these four types of data is shown in Table 1.  
Sensor data comes from various sensors, such as meteorology, traffic flow, and environmental monitoring. Sensors are strategically deployed across the city to monitor real-time information on the urban environment, traffic, and population distribution. These sensors collect data that is then transmitted to a centralized data-processing center or cloud infrastructure. At this hub, the data undergoes aggregation, analysis, and interpretation \begin{table}
\begin{tabular}{c c c c c} \hline
**Data Types** & **Data Source** & **Features** & **Application** & **Challenge** \\ \hline \multirow{4}{*}{Sensor data} & Sensors for weather, traffic flow, environmental monitoring, etc. & Real-time, dynamic, and accurate & Real estate evaluation and urban planning & Large amount of data, complex processing, privacy protection \\ \hline \multirow{4}{*}{Social media data} & Data from social platforms such as Weibo, WeChat, and TikTok & Real-time, interactive, and diverse & Dynamic demand analysis, traffic management, public services & Authenticity is difficult to guarantee, privacy issues \\ \hline \multirow{4}{*}{Citizen feedback data} & Public service platform, government microblog, mayor’s mailbox, etc. & Authenticity, timeliness, and diversity & Government decision-making, measurement effectiveness evaluation, public services & Information overload, difficulty in distinguishing true from false, and strong subjectivity \\ \hline \multirow{4}{*}{GIS Data} & Surveying and mapping data, remote sensing images, and intelligent sensing equipment & Rich spatial and attribute data & Urban planning, real estate management & Data quality, updating, and sharing issues \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of sensor data, social media data, citizen feedback data, and GIS data. using advanced algorithms to extract meaningful insights. This centralized approach ensures efficient data management and allows for the integration of data from various sources, enabling comprehensive urban monitoring and decision-making. The processed information is subsequently used to inform urban planning, optimize resource allocation, and enhance the overall efficiency of city operations. Sensor data provides a scientific basis for evaluating real estate value, optimizing resource allocation, and improving urban management. These data can also realize real-time monitoring and early warning. For example, environmental monitoring sensors can detect air quality, water quality, etc., and issue timely warnings when abnormalities are found [10]. However, sensor data faces problems such as large data volume and obvious spatiotemporal characteristics, which require efficient processing and privacy protection measures [11]. Social media data includes user-generated content, social relationships, and geographic location information and is sourced from various social platforms [12]. These data reflect the dynamic needs of residents and can assist in decision-making in urban planning, traffic management, and public services. Social media data have challenges, such as difficulty in ensuring quality and authenticity, as well as privacy protection. Therefore, strict screening and verification are required during use, and technical means such as desensitization and encryption are used to protect privacy. Citizen feedback data are collected through channels such as urban public service platforms, government microblogs, mayor mailboxes, and online questionnaires, reflecting residents' needs and satisfaction with urban services. These data are authentic, reliable, and timely, covering a wide range of fields.