After an earthquake, the important task of emergency rescue work is to minimize casualties, but due to the suddenness of earthquake disasters, it is difficult to obtain enough disaster information immediately, especially personnel distribution and movement information. The traditional methods of obtaining disaster data are through reports from the disaster area or field investigations by the emergency rescue team; this work lags, and its efficiency is low. This paper analyzes the feasibility of using mobile phone location signal data in earthquake emergency rescue work in several respects, such as quantity, location, change rate, and epicentral distance. The results show that mobile phone location signal data can quickly obtain the situation of personnel distribution and quantity after an earthquake, and we find the change rate, distance, etc., can determine the approximate range of the earthquake impact field. Through the data distribution in different time periods, the movement of personnel after the earthquake can be obtained. Based on several situations, we can determine the basic situation of the disaster-stricken areas in times after the earthquake, especially the personnel relevant to the situation, and these data can provide a scientific basis for emergency rescue decision making.

This article focuses on the rapid assessment of possible numbers of casualties after an earthquake. We use data on earthquake casualties to study and verify the relationship between casualties and building types, building anti-lethal levels, seismic intensity and other influencing factors. A rapid assessment method for casualties based on buildings’ anti-lethal level is proposed. The method first uses the anti-lethal level and proportions of various types of buildings to obtain the overall anti-lethal level of the study area. Second, according to historical casualty data, the casualty rate of each intensity in the study area is obtained. On this basis, a rapid assessment matrix of casualties is constructed based on the overall anti-lethal level of the study area and the casualty rate of each intensity. Finally, we make a rapid assessment of casualties based on a matrix model. Through the verification of 7 selected historical earthquakes, we find that the data on casualties obtained based on this method are close to the actual number of casualties. The resulting casualty parameters are described against the overall casualty data from several historical earthquakes in China, and the error rates of earthquakes in Lancang-Gengma, Yushu, Lushan Min county-Zhang county and Changning are 4.1%, 25.27%, 12.75%, 2% and 7.69%, respectively. The error is relatively small, within 50%, and a more accurate evaluation result is obtained. Our findings illustrate the effectiveness and practicability of a rapid assessment method for casualties based on anti-lethal levels.

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.