Identifying building function type (BFT) is vital for many studies and applications, such as urban planning, disaster risk assessment and management, and traffic control. Traditional remote sensing methods are commonly used for land use/cover classification, but they have some limitations in BFT identification. Considering that the dynamic variations of social sensing mobile signaling (MS) data at diurnal and daily scales are directly related to BFT, in this paper, we propose a method to infer BFT using MS data obtained from mobile devices. First, based on the different patterns of population dynamics within different building types, we propose a BFT classification scheme with five categories: residential (R), working (W), entertainment (E), visiting (V), and hospital (H). Then, a random forest (RF) classification model is constructed based on two days (one workday and one weekend) of MS data with a temporal resolution of one hour to identify the BFT. According to the cross-validation method, the overall classification accuracy is 84.89%, and the Kappa coefficient is 0.78. Applying the MS data-constructed RF model to the central areas of Beijing Dongcheng and Xicheng Districts, the overall detection rate is 97.35%. In addition, to verify the feasibility of the MS data, the Sentinel-2 (S2) remote sensing data are used for comparison, with a classification accuracy of 73.33%. The better performance of the MS method shows its excellent potential for BFT identification, as the spatial and temporal population dynamics reviewed based on MS data are more correlated with BFT than geometric or spectral features in remote sensing images. This is an innovative attempt to identify BFT with MS data, and such a method compensates for the scarcity of BFT studies driven by population dynamics. Overall, in this study, we show the feasibility of using time series MS data to identify BFT and we provide a new path for building function mapping at large scales.

After an earthquake, rapidly assessing the affected areas, particularly the meizoseismal area, is crucial to emergency rescue work. Currently, many methods exist for delimiting the range of the area affected by an earthquake. The main method for estimating this area is to use an empirical model of the seismic intensity attenuation relationship. In contrast, this article uses the change rate (CR) of the phones’ location data in an area after the earthquake to infer the extent of the meizoseismal area. This study selects phone location data from eight earthquakes for experimentation and analysis. The results show a correlation between the phone location data and the actual distribution of the meizoseismal area based on intensity maps that were issued by the China Earthquake Administration. According to the results, the extent of the meizoseismal area can be estimated by the magnitude, the focal depth, the CR, the time of the earthquake, and the distance to the epicenter. The magnitude, CR, and focal depth are dominant and are positively correlated with the intensity. Based on the aforementioned parameters, a calculation model for delimiting the extent of the meizoseismal area based on phone location data can be constructed. The accuracy of the correlation coefficient of fitting result is 0.7826, and the $R2$ value is 0.6125. The results demonstrate that phone location data can be used to quickly delimit the meizoseismal area and can provide a basis for earthquake emergency rescue work.

Earthquake emergency preparedness plays a vital role in strengthening disaster prevention and mitigation capacity, as well as societal resilience. This study focuses on the Xiong’an New Area, a rapidly developing national demonstration zone in China. An evaluation index system for earthquake emergency preparedness was established, and the entropy weight method was applied to objectively determine indicator weights. By integrating field questionnaire data with statistical analysis, preparedness was quantitatively assessed across three administrative levels: township, county, and city. The results reveal pronounced heterogeneity in earthquake emergency preparedness capacity, with township-level performance varying considerably, county-level performance being relatively higher yet still requiring improvement, and the New Area demonstrating strong overall capacity, particularly in emergency organization and coordination mechanisms. However, limited risk awareness and insufficient self-protection capability among grassroots residents remain key constraints on overall preparedness. This research enhances the understanding of earthquake preparedness and provides valuable insights for strengthening disaster prevention, emergency management, and public safety governance in rapidly urbanizing regions.