Global and local land-cover mapping products provide important data on land surface. However, the accuracy of land-cover products is the key issue for their further scientific application. There has been neglect of the relationship between inclusion probability and spatial heterogeneity in traditional spatially balanced sampling. The aim of this paper was to propose an improved spatially balanced sampling method using landscape pattern-based inclusion probability. Compared with other global land-cover datasets, Globeland30 has the advantages of high resolution and high classification accuracy. A two-stage stratified spatially balanced sampling scheme was designed and applied to the regional validation of GlobeLand30 in China. In this paper, the whole area was divided into three parts: the Tibetan Plateau region, the Northwest China region, and the East China region. The results show that 7242 sample points were selected, and the overall accuracy of GlobeLand30-2010 in China was found to be 80.46%, which is close to the third-party assessment accuracy of GlobeLand30. This method improves the representativeness of samples, reduces the classification error of remote sensing, and provides better guidance for biodiversity and sustainable development of environment.

The new type of multi-temporal global land use data with multiple classes is able to provide information on both the different land covers and their temporal changes; furthermore, it is able to contribute to many applications, such as those involving global climate and Earth ecosystem analyses. However, the current accuracy assessment methods have two limitations regarding multi-temporal land cover data that have multiple classes. First, multi-temporal land cover uses data from multiple phases, which is time-consuming and inefficient if evaluated one by one. Secondly, the conversion between different land cover classes increases the complexity of the sample stratification, and the assessments with different types of land cover suffer from inefficient sample stratification. In this paper, we propose a spatiotemporal stratified sampling method for stratifying the multi-temporal GlobeLand30 products for China. The changed and unchanged types of each class of data in the three periods are used to obtain a reasonable stratification. Then, the strata labels are simplified by using binary coding, i.e., a 1 or 0 representing a specified class or a nonspecified class, to improve the efficiency of the stratification. Additionally, the stratified sample size is determined by the combination of proportional allocation and empirical evaluation. The experimental results show that spatiotemporal stratified sampling is beneficial for increasing the sample size of the “change” strata for multi-temporal data and can evaluate not only the accuracy and area of the data in a single data but also the accuracy and area of the data in a multi-period change type and an unchanged type. This work also provides a good reference for the assessment of multi-temporal data with multiple classes.

The innovative development of logistics has become a powerful starting point and strong support for the construction of smart cities. An accurate evaluation of logistics service quality can promote intelligent transformation, and upgrading logistics enterprises can improve the urban public service infrastructure. In this study, we propose a logistics service quality evaluation model based on a combination of sentiment analysis technology and a traditional index evaluation system. With the help of sentiment analysis technology, the model focuses on extracting the sentiment characteristics of logistics service quality from user reviews and analyzing user attitudes from different aspects. We designed a new logistics service quality evaluation index system by improving the SERVQUAL model. The system uses sentiment analysis technology to explore evaluation content through feature extraction and builds relations between the evaluation content and indices. Additionally, we use sentiment orientation analysis with different indices to comprehensively evaluate service quality. The experimental analysis shows that the proposed model and algorithm have high accuracy.

The novel coronavirus (COVID-19) epidemic broke out in Wuhan at the end of 2019 and spread around the whole of China in 2020. In order to reduce the spread of COVID-19, transportation and industrial activities in different regions were limited to varying degrees. This study uses bivariate concentration polar plots, integrated with k-means clustering and temporal variation analyses for PM_2.5 time series data, to understand the PM_2.5 source characteristics in Shanghai during the COVID-19 pandemic in the winter of 2020. Our findings show that 34.33% of the PM_2.5 particles arise from external sources while 65.67% are from local sources. The results of source apportionment combined with land use, wind speed, and direction data are further used to locate the most likely directions of different source categories and geographic origins of PM_2.5. During the lockdown period in 2020, traffic and industrial activity were still primary local sources of PM_2.5 emissions in Shanghai. The growth of motor vehicle ownership, limited public transport, and a large volume of freight transport in Shanghai result in a higher level of PM_2.5 concentrations on weekends than in midweeks. On the other hand, the regional-scale transport of air pollutants from the Yangtze River Delta, the Central Plains, the inland area of northern China, and coastal cities in the north and south of Shanghai aggravates PM_2.5 pollution in Shanghai under unfavorable meteorological conditions. The methods and results presented here lay a basis for further study on the complicated effects of meteorological and anthropogenic factors on PM_2.5 pollution and on the development of detailed and urgent strategies for the improvement of air quality.