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Drought, characterized by frequent occurrences, an extended duration, and a wide range of destruction, has become one of the natural disasters posing a significant threat to both socioeconomic progress and agricultural livelihoods. Large-scale geographical environments often exhibit obvious spatial heterogeneity, leading to significant spatial differences in drought's development and outcomes. However, traditional drought monitoring models have not taken into account the impact of regional spatial heterogeneity on drought, resulting in evaluation results that do not match the actual situation. In response to the above-mentioned issues, this study proposes the establishment of ecological–geographic zoning to adapt to the spatially stratified heterogeneous characteristics of large-scale drought monitoring. First, based on the principles of ecological and geographical zoning, an appropriate index system was selected to carry out ecological and geographical zoning for Yunnan Province. Second, based on the zoning results and using data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) and the Tropical Rainfall Measuring Mission (TRMM) 3B43, the vegetation condition index (VCI), the temperature condition index (TCI), the precipitation condition index (TRCI), and three topographic factors including the digital elevation model (DEM), slope (SLOPE), and aspect (ASPECT) were selected as model parameters. Multiple linear regression models were then used to establish integrated drought monitoring frameworks at different eco–geographical zoning scales. Finally, the standardized precipitation evapotranspiration index (SPEI) was used to evaluate the monitoring effects of the model, and the spatiotemporal variation patterns and characteristics of winter and spring droughts in Yunnan Province from 2008–2019 were further analyzed. The results show that (1) compared to the traditional non-zonal models, the drought monitoring model constructed based on ecological–geographic zoning has a higher correlation and greater accuracy with the SPEI and (2) Yunnan Province experiences periodic and seasonal drought patterns, with spring being the peak period of drought occurrence and moderate drought and light drought being the main types of drought in Yunnan Province. Therefore, we believe that ecological–geographic zoning can better adapt to geographical spatial heterogeneity characteristics, and the zonal drought monitoring model constructed can more effectively identify the actual occurrence of drought in large regions. This research finding can provide reference for the formulation of drought response policies in large-scale regions. [ABSTRACT FROM AUTHOR]

Urbanization has created a significant gap between the Three Northeast Provinces (TNP) and the Yangtze River Delta (YRD) since China’s reform and opening-up. In this study, we explain this problem by employing an urban expansion index, spatial hierarchical clustering, the Zipf-PLE model, fractal theory, and time-series analysis. Statistical data on the built-up area, population, and gross domestic product were downloaded from the National Bureau of Statistics of China. The results revealed the following. (1) During the stage of the planned economy, as a result of the significant government investment in development, the urban built-up areas in the TNP and the YRD reached 1867 and 757 km2, respectively, in 1984. (2) During the stage of the socialist market economy, the urban built-up area of the YRD experienced rapid growth over the 30 years from 1984 to 2014 with a rate of increase of 668.8%, which was higher than that of the TNP (139.9%). In 2014, the urban built-up areas of the TNP and the YRD were 4479 and 5820 km2, respectively. (3) Locational advantages had significant effects on urban development after China’s economic reform. The coastal cities of the YRD had higher rates of increase of the built-up areas. By contrast, the border cities of the TNP had lower rates of increase. Finally, this paper finds that the socialist market economy exhibited more efficiency in promoting urban expansion and economic development. The results are significant for generating balanced and sustainable regional development strategies in the future. [ABSTRACT FROM AUTHOR]

Background: A large number (n = 460) of A(H7N9) human infections have been reported in China from March 2013 through December 2014, and H7N9 outbreaks in humans became an emerging issue for China health, which have caused numerous disease outbreaks in domestic poultry and wild bird populations, and threatened human health severely. The aims of this study were to investigate the directional trend of the epidemic and to identify the significant presence of spatial-temporal clustering of influenza A(H7N9) human cases between March 2013 and December 2014.Methods: Three distinct epidemic phases of A(H7N9) human infections were identified in this study. In each phase, standard deviational ellipse analysis was conducted to examine the directional trend of disease spreading, and retrospective space-time permutation scan statistic was then used to identify the spatio-temporal cluster patterns of H7N9 outbreaks in humans.Results: The ever-changing location and the increasing size of the three identified standard deviational ellipses showed that the epidemic moved from east to southeast coast, and hence to some central regions, with a future epidemiological trend of continue dispersing to more central regions of China, and a few new human cases might also appear in parts of the western China. Furthermore, A(H7N9) human infections were clustering in space and time in the first two phases with five significant spatio-temporal clusters (p < 0.05), but there was no significant cluster identified in phase III.Conclusions: There was a new epidemiologic pattern that the decrease in significant spatio-temporal cluster of A(H7N9) human infections was accompanied with an obvious spatial expansion of the outbreaks during the study period, and identification of the spatio-temporal patterns of the epidemic can provide valuable insights for better understanding the spreading dynamics of the disease in China. [ABSTRACT FROM AUTHOR]

Studies of Erhai Basin indicate that Land use change by human activities in the watershed is the leading cause of regional climate, hydrology, water quality and ecological changes. Therefore, it is necessary to study the relationships between human activities and land use/cover change (LUCC), which is beneficial to offer the scientific decision support for reasonable land planning and land use. Combined with GIS technologies of spatial analysis and using the artificial intelligence algorithm Ant Colony Optimization(ACO) for optimizing, in this paper, we applied the method of Agent-based modeling to establish the spatiotemporal process model of LUCC in order to simulating the dynamic change of land use in whole watershed. Firstly, we made a choice and evaluation for impact factors of land use changes, as well as constructions of the cost of land use change equations in order to construct more reasonable decision rules of land use choice. Then, we have extracted three agents composed by microcosmic and macrocosmic systems which were farm agent, resident agent and government agent. Also, microcosmic rules of decision and behavior were created according to ACO. On the other hand, we have established macrocosmic decision rules according to a resistance coefficient system from the land use planning, as well as a comprehensive decision rule. And then, based on Java language and Repast platform of modeling, the program design, implementation and simulation of model were given in detail. Finally, the validation, calibration and verification of model and analysis of the simulated results were also conducted. Our conclusions from the experiment were three: 1) Ant colony algorithm was more effective in promoting the significant moving and decision of agents, and the simulated results gained better accuracies in both mathematics (up 5.6%) and geometry (up 3.4%) than using a random algorithm. However, the merit of ACO was not suitable for its use in all of land-use types. For an instance, there were no any improvements and sometimes even reduction in accuracy for those land-use types which were less affected by human activities, such as forest, grassland and wetland uses. Thereby, we suggested that ACO was more sensitive to interaction between human and land-use changes, and it was suitable for optimizing human behaviors and decisions of land-use transfer. 2) If the policy on land use was kept unchanged, the major contradiction between human and land in the future ten years should be the persistent reduction of agricultural land (127.64 hm2 cultivated lands and 11.20 hm2 garden lands) and the continuous increase of urbanized land (95.80 hm2). This indicated a big cost of urbanization in Erhai Lake Basin, which also gave a warning of increasing impervious surfaces (IS) produced in future rapid urbanization, and the IS may raise risks of urban non-point source pollution in the future. 3) The fast increasing of wetlands (growth rate of 50.07% was the fastest in the change of all land use types) indicated that the governmental land use policies to ecosystem protection have played a better role in macro control of land resources allocation. From this research, we suggested that the local government should maintain the existing strategies of ecological environment protection to reduce the risk of water pollution in Erhai Lake Basin. The competition in the market economy model of land-resources-commercial should be encouraged to balance the next major conflicts between human activities and land resources. [ABSTRACT FROM AUTHOR]