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.

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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.

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Earthquakes happen suddenly and are immensely destructive. They not only destroy entire societal production and infrastructure systems but also seriously interfere with daily life and reduce opportunities to earn income in earthquake-affected areas. In this paper, using the Ning'er Ms 6.4 earthquake in 2007 as an example, we analyzed the livelihood vulnerability of rural households in Ning'er County, Yunnan, based on data from questionnaires and on-site interviews. The results showed that on the whole, local rural household livelihoods are relatively vulnerable in the earthquake-affected area of Ning'er. The main reason for the high level of vulnerability of rural households is the lack of single or multiple incomes. Due to the shortage of household income, the capacity of rural households to manage the aftermath of an earthquake is low. Improving the income allocation and transformation level and expanding methods of earning income is an effective way for rural households to decrease livelihood vulnerability in earthquake-prone areas. Some suggestions are given for local rural households to enhance their livelihood income levels in the event of earthquakes.