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Knowledge of the spatial distribution of populations at finer spatial scales is of significant value and fundamental to many applications such as environmental change, urbanization, regional planning, public health, and disaster management. However, detailed assessment of the population distribution data of countries that have large populations (such as China) and significant variation in distribution requires improved data processing methods and spatialization models. This paper described the construction of a novel population spatialization method by combining land use/cover data and night-light data. Based on the analysis of data characteristics, the method used partial correlation analysis and geographically weighted regression to improve the distribution accuracy and reduce regional errors. China's census data for the years 1990, 2000, and 2010 were assessed. The results showed that the method was better at population spatialization than methods that use only night-light data or land use/cover data and global linear regression. Evaluation of overall accuracies revealed that the coefficient of correlation R-square was >0.90 and increased by >0.13 in the years 1990, 2000, and 2010. Moreover, the local R-square of over 90% of the samples (counties) was higher than the adjusted R-square of the general linear regression model. Furthermore, the gridded population density datasets obtained by this method can be used to analyse spatial-temporal patterns of population density and provide population distribution information with increased accuracy and precision compared to conventional models. [ABSTRACT FROM AUTHOR]

• We propose a new forest prediction model and use actual deforestation data for optimization and verification. • Use the XGBoost method to reconstruct deforestation intensity data. • Deforestation hotspots in China are mainly concentrated in the southern and southeastern regions. • Our model provides certain reference information for forest disaster prevention and forest management. Forests are important for the global carbon cycle, hydrothermal balance, and climate change. Human activities can exert a significant impact on forest ecosystems, thereby having the potential to alter their physical and chemical properties and thus affecting carbon, water, and heat budgets, and climate change. The historical reconstruction of the disturbance of global forests can help us understand the processes and patterns of human activities and global change. In this paper, we construct a deforestation prediction model using a Spearman correlation coefficient and implement the XGBoost method, using Python 3.6, for the reconstruction of deforestation intensity data from 2000 to 2019. Secondly, the selection of the driver indicators is done by using extreme difference regularization to unify the magnitude, and the potential deforestation area risk index is calculated in the form of equal weights. Finally, the actual deforestation data were used for optimization and validation. The model shows that the deforestation hotspots are mainly concentrated in the southern and southeastern regions of China and that there are large differences in deforestation in different provinces. In the future, the fine spatial and temporal patterns of deforestation in China during the historical period can be quantitatively reconstructed, which can provide some reference information for forest disaster prevention and forest management in China. [ABSTRACT FROM AUTHOR]

Eastern China, a populous and economically developed area, is severely affected by various meteorological disasters under the background of global climate change. In recent years, urban waterlogs caused by heavy rainfall has become a major concern threatening urban safety and socioeconomic development. This paper aimed to reveal the spatial pattern of waterlogging risk in 89 prefecture-level cities of Eastern China by a comprehensive analysis on extreme rainfall, socioeconomic exposure and urban adaptability. Hazard intensity index was calculated on 20-year return period value of maximum daily precipitation. Exposure index was based on the total urban population and local gross domestic product, while adaptability index was an integrated evaluation of drainage pipeline density, river network density and greening rate of urban area. Risk assessment was a synthesis judgment of hazard intensity index, exposure index and urban adaptability index, according to which the cities in Eastern China were classified into five risk levels. The six cities with the highest level of waterlogging risk were Shenzhen, Beijing, Maoming, Yangjiang, Shanwei and Dongguan, which indicates relatively stronger intensity of meteorological hazard, higher density of socioeconomic exposure and the weakness of urban adaptability in these cities. The high risk cities would suffer much more losses than other cities in Eastern China when hit by an extreme rainfall. This paper could provide a scientific basis for urban waterlogging risk identification and governance, as well as for technical measures development and capacity building of disaster prevention and reduction. • A research framework and technical methods for urban waterlogging risk assessment were proposed. • An indicator system for waterlogging risk assessment was raised based on hazard, exposure and adaptability factors. • Urban adaptability of waterlogging disaster was evaluated based on drainage pipelines, river networks and greening level. • Taking Eastern China as a pilot, the paper presented a city risk ranking for urban waterlogs. [ABSTRACT FROM AUTHOR]

• The characteristics of residents' livelihood capital, evacuation and relocation willingness were analyzed. • The correlations between residents' livelihood capital, evacuation and relocation willingness were explored. • Livelihood capital is an important influence on residents' willingness to relocate. Evacuation and relocation are the most effective ways for residents to avoid disasters, and can ensure the property and safety of life. Therefore, it is of great significance for regional disaster risk management systems to explore the driving factors of residents' willingness to evacuate and relocate from earthquake-stricken areas. However, few scholars have quantitatively explored the relationships between residents' livelihood capital and their evacuation and relocation willingness, with consideration of residents' livelihood sustainability. According to the research objectives, stratified sampling and equal probability random sampling were used to determine the sample farmers, and the survey data of 327 households in Wenchuan and Lushan earthquake-stricken areas were obtained. This paper analyzed the characteristics of residents' livelihood capital, evacuation and relocation willingness, and used ordinary least squares (OLS) to explore correlations between residents' livelihood capital, evacuation and relocation willingness. The results show that: (1) the livelihood capital of residents is mainly comprised of human capital and social capital. Facing the threat of earthquake disaster, 40.98% and 28.75% of residents would be strongly willing to evacuate or relocate, respectively. (2) Livelihood capital is an important influence on residents' willingness to relocate; the higher the scores of natural capital, the weaker the willingness of residents to relocate. From the perspective of rural households' sustainable livelihoods, this study deepens our understanding of evacuation and relocation behaviors and decision-making by residents of the most earthquake-stricken areas. Thus, it provides useful information for regional disaster risk management. [ABSTRACT FROM AUTHOR]