Based on Logistic Regression, the model accounts for 93% of the variance (Nagelkerke R Square) in the dependent variables included in the equation. The results from a series of simulated experiments show that social influence, political influence, sympathy, net risk, and grievance were statistically significant factors that contribute to the probability of students engaging in a protest. In this study, an agent-based model that integrates grievance as relative deprivation, perceived risk, and various network effects was used to simulate student protest. Student protest actions have often resulted in damage to property and cancelation of academic programs. The purpose of the paper is to design and implement an agent-based model of student protests to predict the emergent of protest actions. Thirdly, according to the driving force of the factors and the interaction between them, suggestions are put forward based on the development stage and key demands for city and provincial governments to make policies for the development of national new districts, to support the establishment of scientific competition and cooperation between new towns and mother cities or regions, and to build a long-term collaborative development mechanism. Secondly, the pairwise interaction between different factors exhibits two-factor enhancement, and the population shows a nonlinear increase in the driving force of investment, openness, and innovation on a provincial scale. The strength and dimension of the driving factors are characterized by prominent heterogeneity R&D personnel, export and import trade are the key factors to expand the increment, optimize the inventory, and improve the quality the overall development driving forces are in the order of innovation > opening > industry > investment > population. The study shows that, firstly, there are five types of driving factors, that is, all-round driving factors, scale-increasing factors, expansion and quality-improving factors, expertise driving factors, and non-driving factors. Based on the geodetector method, this paper reveals the key factors driving the development of national new districts by mother cities and hinterland provinces and their interaction effects, which provides a basis for municipal and provincial governments to accurately formulate policies to promote the development of new towns by classification. They are mutually reinforcing with their mother cities and hinterland provinces. National new districts constitute a new regional space for China to implement the national strategy and promote the transformation of urban development mode. Īs an important carrier of expanded urban spatial growth, new towns have been a “policy tool” for spatial production in the new era and have received long-term and constant attention from circles such as geography, planning, and economics. Expand the economy (GDP) and foreign trade, increase government investment (fiscal expenditure) and give priority to promoting industrial development Optimize real estate development and investment management and control policies, and further innovate technology investment and talent aggregation policies. According to the force size of the driving factors and their interaction effects, as well as different development stages of the life cycle of the national new districts and their key demands, this paper puts forward the suggestions that should be properly considered when the hinterland provinces and cities of the national new districts and even the central government make policies (see Table 5), so as to better play the role of government or politics in the sustainable development of the new town. As there is a certain deviation in the understanding of the concept of "new town" in different countries and even between governments in a country at different levels besides, the planning and construction of new towns is a long-term and complex comprehensive development project, the central, provincial, and city governments of the countries must sort out and accurately position new towns, and design targeted policies according to the areas where they play an effective role to give hierarchical and classified guidance.
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