Social media activity has become an important component of daily life for many people. Messages from Twitter (US) and Weibo (China) have shown their potential as important data sources for detecting and analyzing infectious diseases. Such emerging and dynamic new data sources allow us to predict how infectious diseases develop and evolve both spatially and temporally. We report the dynamics of dengue fever in China using messages from Weibo. We first extract and construct a list of keywords related to dengue fever in order to analyze how frequently these words appear in Weibo messages based on the Latent Dirichlet Allocation (LDA). Spatial analysis is then applied to detect how dengue fever cases cluster spatially and spread over time.

In this paper, we examine the influence of environmental regulation on sustainable economic growth from both theoretical and empirical perspectives. Our research is twofold. First, we apply a modified NEG (New Economic Geography) model to analyze how environmental regulation influences firms’ location choices and cities’ sustainable economic growth. Second, we test a spatial econometric model employing panel data of the three largest urban agglomerations in China from 2003 to 2013 to study the relationship between environmental regulation and sustainable economic growth as well as the spillover channels of economic activities. The results reveal a remarkable negative effect of environmental regulation on economic growth. In addition, we find no sufficient evidence to prove the existence of long-term effects of environmental regulation on economic growth in the three urban agglomerations. Furthermore, using different weight matrices to illustrate the different economic networks of the urban agglomeration, we validate the difference in spillover mechanisms across these three urban agglomerations. Specifically, the disparity in environmental regulation acts as a spillover channel for the Yangtze River Delta and the Pearl River Delta, while it is not significant for Jing-Jin-Ji.

Social networking has become a crucial factor affecting regional economic activities. Employing the panel data of the Yangtze River Delta and the Pearl River Delta in China, we examine the influence of environmental regulation on industrial structure and the role that social networks play in the spillover effect. Using the social media data from the Weibo API and the geo-information of enterprises, we construct the Weibo network and the enterprise network, then we analyze the network structures by employing a social network analysis method. The empirical results find the evidence of the spillover effects of environmental regulation through the above two networks by using network linkages as weight matrices in spatial econometric regressions.

Spatial club convergence is a group of regions which are adjacent to each other in space and have similar initial conditions and structural features while converging towards the same steady state in the economic development. Based on the Solow model, this paper builds a theoretical model to prove the mechanism of spatial club convergence. The spatial club convergence process is explored in inland China, using the case of the Zhongyuan urban agglomeration during the years of 1993–2009. This region has been experiencing a dramatic economic development and serves as an ideal test bed of the theory of spatial club convergence. The results show that in the two periods of 1993–1999, and 1993–2009, there was spatial club convergence in the 56 regions of Zhongyuan urban agglomeration of China. The respective convergence rates were 2.0% and 1.0%. Hence, both theoretical deduction and empirical studies verify the hypothesis of spatial club convergence.

A major obstacle to sustainable land management in ecologically sensitive areas is our limited understanding of the complex nonlinear mechanisms and threshold effects that dictate trade-offs between land use functions (LUFs). This study takes the Funiu Mountain area, a typical ecologically sensitive area in China, as a case study. At the township scale, the spatiotemporal patterns of LUFs from 2000 to 2020 were assessed based on the “production-living-ecological” function framework. The root mean square error (RMSE) model was introduced to quantify inter-functional trade-off intensity. Furthermore, the optimal parameters geographical detector (OPGD) and generalized additive model (GAM) were innovatively coupled to systematically analyze their driving mechanisms and nonlinear threshold effects. The results indicate that: (1) LUFs show clear functional complementarity and spatial game characteristics. The production function (PF) exhibits a heterogeneous pattern of “locally high, overall low”; the living function (LF) shows a local central agglomeration feature; and the ecological function (EF) displays a continuous gradient distribution of “high in the northwest, low in the southeast”. (2) The trade-off intensities between PF&EF and LF&EF are relatively strong, with high-value areas mainly distributed in the high-altitude central regions; while the trade-off intensity of PF&LF is weaker, with high-value areas mostly appearing in the central urban areas of each county. (3) The spatial heterogeneity of the trade-off relationship of LUFs is a comprehensive manifestation of the combined effects of the natural environment, socio-economic factors, and landscape patterns. The driving mechanisms of trade-off intensity among different functions show significant heterogeneity. (4) Key driving factors have significant nonlinear threshold effects. POP shows a complex dynamic regulatory effect with multiple thresholds and strong nonlinearity, while SLOPE, PET, and NDVI continuously play a fundamental constraining role in the trade-offs related to ecological functions. The key thresholds identified in this study can provide a direct scientific basis for regional differentiated territorial space governance.