Rapid urbanization has brought many problems, including housing shortages, traffic congestion, air pollution, and lack of public space. To solve these problems, the United Nations proposed “The 2030 Agenda for Sustainable Development”, which contains 17 Sustainable Development Goals covering three dimensions: economy, society, and environment. Among them, Sustainable Development Goal 11 (SDG11), “Make cities and human settlements inclusive, safe, resilient and sustainable”, can be measured at the city level. So far SDG11 still lacks three-quarters of the data required to accurately assess progress towards the goal. In this paper, we localized the indicators of SDG11 and collected Earth observation data, statistical data, and monitoring data at the city and county levels to build a better urban sustainable development assessment framework. Overall, we found that Haikou and Sanya were close to achieving sustainable development goals, while other cities were still some distance away. In Hainan Province, there was a spatial distribution pattern of high development levels in the north and south, but low levels in the middle and west. Through the Moran’s I Index of Hainan Province, we found that the sustainable development of Hainan Province did not yet form part of integrated development planning. The sustainable development assessment framework and localization methods proposed in this paper at the city and county levels provide references for the sustainable development of Hainan. At the same time, it also provides a reference for the evaluation of county-level sustainable development goals in cities in China and even the world.

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Human activities are usually collective, so clustering has become an important feature of human behavior. This paper studied the evolution of the community in the process of public opinion propagation so as to put forward a public opinion evolution model for the network community number. This study proposed the community number evolution model of public opinion based on stochastic competitive learning, and the proposed model consists of an increase in the number of communities and a decrease in the number of communities. The highlight of this model is that on the one hand, it realizes the research on the evolution of public opinions on the dynamic network; on the other hand, unlike other public opinion evolution models, this model pays attention to the community number increase and decrease rules in the evolution of public opinions. Then, as an extension of the community number evolution model of public opinion, the community number prediction model had been proposed. Based on Twitter data from the 2017 London Bridge attack, the proposed models were validated by experiments. In the verification section of this paper, two methods had been introduced as a comparison. The experimental results show that the community number evolution model of public opinion is correct.

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