Nowadays, China is the world’s second largest economy and largest carbon emitter. This paper calculates the carbon emission intensity and the carbon emissions per capita of land use in 30 provinces at the national level in China from 2006 to 2016. A spatial correlation model is used to explore its spatiotemporal features. The results show that (1) China’s land use carbon emissions continued to grow from 2006 to 2016. The spatial heterogeneity of carbon emission intensity of land use initially decreased and then increased during this period. The carbon emission of land use pattern reached a peak in 2015 and the land use carbon emission intensity was relatively lower in east China; (2) southern China accounts for a majority of the total Chinese carbon sink. Better economic structure, land use structure and industrial structure will lead to lower carbon emission intensity of land use; (3) carbon emissions per capita of land use in China are affected not only by land development intensity, urbanization level, and energy consumption structure, but also by the population policy. It is significant to formulate differentiated energy and land use policies according to local conditions. This study not only provides a scientific basis for formulating different carbon emission mitigation policies for the local governments in China, but also provides theoretical reference for other developing countries for sustainable development. It contributes to the better understanding of the land use patterns on carbon emissions in China.

Bicycling is an alternative of urban transport mode, which is significantly influenced by land use. This paper makes an effort to quantify the magnitude and direction of the impact. We first develop a theoretical framework to establish links between land use and bicycle usage. Then, trip data is crawled from Mobike, one of the largest newly emerging, free-floating bike sharing operators in Shenzhen (China), for a total of more than 7.8 million records over 191 consecutive days. And bicycling frequency, travel duration, and riding distance are obtained to be proxies of bicycle usage. Land-use characteristics regarding bicycling are comprehensively indicated by a set of standardized variables including three dimensions, land-use type, land-use mix, land-use connections, and 12 concrete indices. Panel spatial model is applied to quantify the associations at the district level with socioeconomics controlled. Results show that the percentage of green land has a remarkable impact on bicycle usage outcomes and land-use mix is positively associated with bicycling frequency. Density of intersections contributes to longer trip duration. Bicycle lane is a positive facilitator on workdays, while the number of stations is positively related to bicycle usage, especially frequency and distance. These findings provide insight into land use-transport interaction and could be of value to policymakers, planers and practitioners for transport planning while incorporating bicycling-friendly principles.

Interpersonal and intrapersonal variabilities are two important perspectives to understand daily travel behaviors, while only a small number of studies incorporate them for understanding human dynamics. This paper employed a network analysis approach to detecting daily activity-travel patterns of 680 Beijing’s residents within a week and then used a multilevel multinomial logit model to analyze the intrapersonal variability in patterns and the socioeconomic linkages behind them. Results suggest that most activity-travel patterns have significant day-to-day intrapersonal and interpersonal variabilities. This suggests that the application of a typical day of activity-travel behaviors to measure and represent a week’s or even longer-term behaviors may be biased, due to the existence of day-to-day intrapersonal variability. This study also provides a hint for the selection of days of a week to conduct a diary survey for activity pattern mining or travel demand modeling.

Understanding land use change patterns of rural town settlements (RTSs) is crucial for rural and small-town planning; however, few studies have explored pattern mining approaches to RTS trajectory analysis. In this study, we adopted a novel method by building sequence alignment method (SAM) to detect representative trajectory clusters of land use change of 1158 RTSs in seven waves from 1980 to 2015 in Guangdong, China. The results suggest that there are 10 clusters of RTSs with varying trajectories of land use change, implying their differences in the development processes and underlying socioeconomic, demographical, and institutional factors. A spatial distribution map of RTSs shows that stable cultivated ecological and stable ecologically dominant RTSs are distributed in the northern, eastern, and western parts of Guangdong, whereas stable rural construction and stable mixed construction RTSs are mostly located around the provincial boundary. Notably, 73% of the RTSs that have undergone changes in land use types are located in the Pearl River Delta (PRD), including urbanized and agricultural upgraded RTSs. The analysis presented here summarizes the driving forces of the spatial evolution of RTSs, including the location, landforms, industries, and policy factors. This study provides dynamic policy implications to understand longitudinal and sequential spatial restructuring and regional coordinated development in the fast-growing PRD area.

COVID-19 is threatening the whole world. This paper aims to explore the correlation between climatic factors and the morbidity of COVID-19 in Wuhan, China, mainly by using a geographic detector and GWR model. It was found that the response of the morbidity of COVID-19 to meteorological factors in Wuhan is different at different stages. On the whole, the morbidity of COVID-19 has a strong spatial aggregation, mainly concentrated in the central area of Wuhan City. There is a positive correlation between wind speed and the spread of COVID-19, while temperature has a negative correlation. There is a positive correlation between air pressure and the number of COVID-19 cases. Rainfall is not significantly correlated with the spread of COVID-19. It is concluded that wind speed, relative humidity, temperature, and air pressure are important meteorological factors affecting the spread of COVID-19 in Wuhan. Any two variables have greater interaction with the spatial distribution of the incidence rate of COVID-19 than any one factor alone. Those findings not only provide a new insight for the key intervention measures and the optimal allocation of health care resources accordingly but also lay a theoretical foundation for disease prevention, disease intervention and health services.