Direct immune-SCIR public-opinion propagation model based on real-time online users

Current public-opinion propagation research usually focused on closed network topologies without considering the fluctuation of the number of network users or the impact of social factors on propagation. Thus, it remains difficult to accurately describe the public-opinion propagation rules of social networks. In order to study the rules of public opinion spread on dynamic social networks, by analyzing the activity of social-network users and the regulatory role of relevant departments in the spread of public opinion, concepts of additional user and offline rates are introduced, and the direct immune-susceptible, contacted, infected, and refractory (DI-SCIR) public-opinion propagation model based on real-time online users is established. The interventional force of relevant departments, credibility of real information, and time of intervention are considered, and a public-opinion propagation control strategy based on direct immunity is proposed. The equilibrium point and the basic reproduction number of the model are theoretically analyzed to obtain boundary conditions for public-opinion propagation. Simulation results show that the new model can accurately reflect the propagation rules of public opinion. When the basic reproduction number is less than 1, public opinion will eventually disappear in the network. Social factors can significantly influence the time and scope of public opinion spread on social networks. By controlling social factors, relevant departments can analyze the rules of public opinion spread on social networks to suppress the propagate of negative public opinion and provide a powerful tool to ensure security and stability of society.

Spatial Spillover and the Factors Influencing Public Service Supply in Sichuan Province, China

This article examines the spatial characteristics of public service supply and the factors influencing such supply in cities of Sichuan Province, China using spatial-autocorrelation and spatial econometric models with statistical data in 2012. The results demonstrate that expenditures on different types of public services present different spatial autocorrelation patterns. Although the spatial differences in basic public service expenditures are relatively small, a clear fan-shaped spillover to the east can be seen in Chengdu City. Chengdu also shows high clustering of advanced public service expenditures, being a typical core-periphery pattern. Post-earthquake reconstruction expenditures are clustered in the "5.12 Wenchuan earthquake" region and spill over toward cities to the east. The efficiency of public services in the mountainous areas in western Sichuan is low and exhibits a pattern of low-low spatial autocorrelation. The efficiency of public service supply is affected by economic, social, political and geographical factors. Based on the results of this analysis, we recommend a supply strategy that incorporates different types of public services and a specialized public service supply strategy for mountainous areas. Overall public service efficiency should be enhanced by focusing on narrowing the gap in farmers' income among regions and accelerating urbanization. Decision-makers should consider moresupportive policies with regard to providing basic public services in mountainous areas to ensure an equalized supply of basic public services. To enhance the efficiency of advanced public service supply, additional growth pole should be encouraged and incentivized; however, investments are required to drive the development of the peripheral regions through regional economic integration. Both software and hardware types of infrastructure are required to supply services efficiently during post-disaster reconstruction.

Uncertainty Reduction Through Data Management in the Development, Validation, Calibration, and Operation of a Hurricane Vulnerability Model

Catastrophe models estimate risk at the intersection of hazard,exposure,and vulnerability.Each of these areas requires diverse sources of data,which are very often incomplete,inconsistent,or missing altogether.The poor quality of the data is a source of epistemic uncertainty,which affects the vulnerability models as well as the output of the catastrophe models.This article identifies the different sources of epistemic uncertainty in the data,and elaborates on strategies to reduce this uncertainty,in particular through identification,augmentation,and integration of the different types of data.The challenges are illustrated through the Florida Public Hurricane Loss Model(FPHLM),which estimates insured losses on residential buildings caused by hurricane events in Florida.To define the input exposure,and for model development,calibration,and validation purposes,the FPHLM teams accessed three main sources of data:county tax appraiser databases,National Flood Insurance Protection(NFIP)portfolios,and wind insurance portfolios.The data from these different sources were reformatted and processed,and the insurance databases were separately cross-referenced at the county level with tax appraiser databases.The FPHLM hazard teams assigned estimates of natural hazard intensity measure to each insurance claim.These efforts produced an integrated and more complete set of building descriptors for each policy in the NFIP and wind portfolios.The article describes the impact of these uncertainty reductions on the development and validation of the vulnerability models,and suggests avenues for data improvement.Lessons learned should be of interest to professionals involved in disaster risk assessment and management.