With the proliferation of social media, information generated and disseminated from these outlets has become an important part of our everyday lives. For example, this type of information has great potential for effectively distributing political messages, hazard alerts, or messages of other social functions. In this work, we report a case study of the 2012 Beijing Rainstorm to investigate how emergency information was timely distributed using social media during emergency events. We present a classification and location model for social media text streams during emergency events. This model classifies social media text streams based on their topical contents. Integrated with a trend analysis, we show how Sina-Weibo fluctuated during emergency events. Using a spatial statistical analysis method, we found that the distribution patterns of Sina-Weibo were related to the emergency events but varied among different topics. This study helps us to better understand emergency events so that decision-makers can act on emergencies in a timely manner. In addition, this paper presents the tools, methods, and models developed in this study that can be used to work with text streams from social media in the context of disaster management and urban sustainability.

The neural network-based hyperspectral images (HSI) classification model has a deep structure, which leads to the increase of training parameters, long training time, and excessive computational cost. The deepened network models are likely to cause the problem of gradient disappearance, which limits further improvement for its classification accuracy. To this end, a residual unit with fewer training parameters were constructed by combining the residual connection with the depth-wise separable convolution. With the increased depth of the network, the number of output channels of each residual unit increases linearly with a small amplitude. The deepened network can continuously extract the spectral and spatial features while building a cone network structure by stacking the residual units. At the end of executing the model, a 1 × 1 convolution layer combined with a global average pooling layer can be used to replace the traditional fully connected layer to complete the classification with reduced parameters needed in the network. Experiments were conducted on three benchmark HSI datasets: Indian Pines, Pavia University, and Kennedy Space Center. The overall classification accuracy was 98.85%, 99.58%, and 99.96% respectively. Compared with other classification methods, the proposed network model guarantees a higher classification accuracy while spending less time on training and testing sample sites.

Fine particulate matter smaller than 2.5 μm (PM_2.5) in size can significantly affect human health, atmospheric visibility, climate, and ecosystems. PM_2.5 has become the major air pollutant in most cities of China. However, influencing factors and their interactive effects on PM_2.5 concentrations remain unclear. This study used a geographic detector method to quantify the effects of anthropogenic precursors (AP) and meteorological factors on PM_2.5 concentrations in cities of China. Results showed that impacts of meteorological conditions and AP on PM_2.5 have significant spatio-temporal disparities. Temperature was the main influencing factor throughout the whole year, which can explain 27% of PM_2.5 concentrations. Precipitation and temperature were primary impacting factors in southern and northern China, respectively, at the annual time scale. In winter, AP had stronger impacts on PM_2.5 in northern China than in other seasons. Ammonia had stronger impacts on PM_2.5 than other anthropogenic precursors in winter. The interaction between all factors enhanced the formation of PM_2.5 concentrations. The interaction between ammonia and temperature had strongest impacts at the national scale, explaining 46% (q = 0.46) of PM_2.5 concentrations. The findings comprehensively elucidated the relative importance of driving factors in PM_2.5 formation, which can provide basic foundations for understanding the meteorological and anthropogenic influences on the concentration patterns of PM_2.5.

Transmission of dengue virus is a complex process with interactions between virus, mosquitoes and humans, influenced by multiple factors simultaneously. Studies have examined the impact of climate or socio-ecological factors on dengue, or only analyzed the individual effects of each single factor on dengue transmission. However, little research has addressed the interactive effects by multiple factors on dengue incidence. This study uses the geographical detector method to investigate the interactive effect of climate and socio-ecological factors on dengue incidence from two perspectives: over a long-time series and during outbreak periods; and surmised on the possibility of dengue outbreaks in the future. Results suggest that the temperature plays a dominant role in the long-time series of dengue transmission, while socio-ecological factors have great explanatory power for dengue outbreaks. The interactive effect of any two factors is greater than the impact of single factor on dengue transmission, and the interactions of pairs of climate and socio-ecological factors have more significant impact on dengue. Increasing temperature and surge in travel could cause dengue outbreaks in the future. Based on these results, three recommendations are offered regarding the prevention of dengue outbreaks: mitigating the urban heat island effect, adjusting the time and frequency of vector control intervention, and providing targeted health education to travelers at the border points. This study hopes to provide meaningful clues and a scientific basis for policymakers regarding effective interventions against dengue transmission, even during outbreaks.

Regional economic development is inherently uneven as determined by the local conditions and available resources. Specialized villages (SVs) in China played a very important role in the development and economic transformation in rural areas. By integrating regional spatial structure theory, multilevel network theory, and spatial interface theory, this article examines the spatial and temporal evolution of SVs in Henan Province, China. Results from the analyses show that the development of SVs over time progressed in four stages, each corresponding to important adjustments in national agricultural policy. SVs were distributed unevenly in space and the distribution seemed to be scale-dependent. At a macrolevel, SVs displayed a dispersed pattern over a large area. SVs showed localized clusters at a microlevel, however, also exhibiting a core–periphery structure. Rural economic development in China showed that SVs formed a multilevel network hierarchy. We also observed that SVs were often in transitional areas including urban–rural and plain–mountain interfaces and administrative marginal zones. Finally, spatiotemporal clusters of SVs helped to identify the locations and time periods when SVs grew significantly for analysis of impacts by national policies on rural development.