Seismic vulnerability assessments play a significant role in comprehensive risk mitigation efforts and seismic emergency planning, especially for urban areas with a high population density and a complex construction environment. Traditional approaches such as in situ fieldwork are accurate for conducting seismic vulnerability assessments of buildings; however, they are too much time and cost-consuming, especially in moderate to low seismic hazard regions. To address this issue, an integrated approach for a macroseismic vulnerability assessment composed of data mining methods and GIScience technology was presented and applied to Urumqi, China. First, vulnerability proxies were established via in situ data of buildings in the Tianshan District with an EMS-98 vulnerability classification scheme and two data mining methods, namely, support vector machine and association rule learning methods. Then, vulnerability proxies were applied to the Urumqi database, and the accuracy was validated. Finally, seismic risk maps were constructed through data consisting of direct damage to buildings and human casualties. The results indicated that the two data mining methods could achieve desirable accuracies and stabilities when estimating the seismic vulnerability. The seismic risk of Urumqi was estimated as Slight with a predicted number of 61,380 homeless people for a seismic intensity scenario of VIII.

China is one of the most earthquake-prone countries in the world. The highest-priority mission after an earthquake is to rapidly save lives, and to minimize the loss of life. Rapid judgment of the trapped personnel location is the important basis to identify the emergency supply demands and carry out the search and rescue work after the earthquake. Through analyzing the main influencing factors, we constructed an assessment model of people trapped in collapsed buildings caused by the earthquakes. The accuracy of the estimation results from the model was then tested against the actual investigation data in 2014 Ludian earthquake-hit area. Results showed that, the trapped personnel distribution assessed by this model is generally concordant with that obtained by the actual survey in Ludian earthquake. The grid-based assessment of people trapped in earthquakes can meet the requirements of key search and rescue zone identification and rescue forces allocation in the early stage of earthquake emergency. Although there were some limitations in the study, it offers a simple and rapid approach for assessing the trapped people losses based on basic empirical data. The approach can be further improved to provide more information and suggestions for earthquake emergency search and rescue.

High-resolution, dynamic assessments of the spatiotemporal distributions of populations are critical for urban planning and disaster management. Mobile phone big data have real-time collection, wide coverage, and high resolution advantages and can thus be used to characterize human activities and population distributions at fine spatiotemporal scales. Based on six days of mobile phone user-location signal (MPLS) data, we assessed the dynamic spatiotemporal distribution of the population of Xining City, Qinghai Province, China. The results show that strong temporal regularity exists in the daily activities of local residents. The spatiotemporal distribution of the local population showed a significant downtown-suburban attenuation pattern. Factors such as land use types, holidays, and seasons significantly affect the spatiotemporal patterns of the local population. By combining other spatiotemporal trajectory data, high-resolution and dynamic real-time population distribution evaluations based on mobile phone location signals could be better developed and improved for use in urban management and disaster assessment research.

The increase in the number and severity of seismic disasters has put communities in danger, especially in rapidly developing and densely populated areas. Traditional seismic vulnerability and risk assessment methods, including field investigation, are accurate at the building scale; however, their low-efficiency and high-cost characteristics limit the application of these methods in urban-scale regions with high-speed development and risk exposure. To address this issue, multisource remote sensing interpretation, support vector machine (SVM) and geographic information science (GIScience) technologies are combined to test the performance and efficiency of a urban-scale macroscopic seismic vulnerability and risk assessment method in the Lixia District of Jinan City, Shandong Province, China, which is characterized by rapid development, a variety of building types, and moderate-to-low seismic risk. First, a traditional field survey was conducted in Lixia District, and a building attribute information database was constructed. Second, the vulnerability proxies of building attribute information and building seismic vulnerability were estimated based on the EMS-98 standard and the SVM. Finally, vulnerability proxies established based on the RISK-UE model were applied to the Lixia database, and the vulnerability and risk assessment under different seismic intensities were estimated with the experimental accuracy verified. The results showed that the SVM method can obtain stable and accurate results in urban scale vulnerability assessment. The mean building vulnerability index in Lixia District is 0.43, which indicates that the overall seismic performance is good. Most of the area falls within the seismic intensity range of VII-X degrees and would experience slight to moderate damage. The results of the study contribute to enhancing the precision and efficacy of large-scale seismic risk assessment, and they can be used by relevant departments to create tailored emergency plans and reduce seismic hazard losses. Additionally, these results can aid in achieving the climate action goal (SDG13) of the United Nations Sustainable Development Goals (SDGs).