Comparative analysis of statistical sampling methods in evaluating building damages

The present work investigated the Yunnan Ludian earthquake in 2014. Simple random sampling, stratified random sampling and cluster random statistical sampling combined with remote sensing techniques were used to assess building damages. The accuracy and stability of simple random sampling was
found to be the highest among the three sampling methods. The overall accuracy of simple random sampling was up to 98.96%, the estimation accuracy of damage degree for different types of building was found to be above 97%. The costs of investigation were rather high because the sampling points were excessively scattered. We should pay attention to the errors caused by no answer samples due to unreachable roads. The accuracy of stratified random sampling was rather low. A variety of factors could influenced the degree of building damages. A single indicator can not reflect the overall characteristics of the target nor highlight the advantage of stratification. The cluster random sampling was found to be reasonable. The overall accuracy of cluster random
sampling was above 93.31% and the estimation accuracy of damage degree for different types of building was above 85%. More importantly, this method made the investigation much easier and cost-efficient by reducing the number of sampling units. Correlation and regression analysis indicated that the variation coefficient of both simple random sampling and cluster random sampling showed negative correlation with the total number of sampled buildings .According to actual requirements, we can increase the ratio of sampling to decrease variation coefficient and reduce the variation of estimation.