Jie Song’s research while affiliated with Taiyuan Normal University and other places

The value of linking urban environment and subjective well-being (SWB) is now well recognized. But whether the geographical context inside and outside the neighborhood has differential influence on long- and short-term SWB remains unclear. Based on the activity perspective, we used survey data from Guangzhou, China, integrating GPS data, portable environmental sensors data to analyze time-weighted and real-time geographical context inside and outside the neighborhood on long- and short-term SWB. The results show that SWB is not only influenced by the neighborhood environment, but also the geographical context outside the neighborhood. Time-weighted geographical environment inside the neighborhood has a higher impact and explanatory ability on long-term SWB, while real-time geographical environment outside the neighborhood has a higher impact and explanatory ability on short-term SWB. This study provides a new understanding for geographies of SWB through the extension of time and space, and also provides reference for more refined urban planning and governance in the future.

Long-term poverty data can support accurate decision-making. This study demonstrates an accurate and reliable method for identifying poverty areas and predicting poverty incidence based on night time light remote-sensing data and machine learning methods. Using data of poverty counties and poverty incidence in Guizhou Province of China as the training dataset, we show how to use machine learning to identify poverty counties and predict poverty incidence in the Yunnan-Guangxi-Guizhou Rocky desertification area. The identification accuracy of poverty-stricken counties was 76.5%. The root mean squared error, mean absolute error, and R² values of the poverty incidence rates were 5.01, 4.04, and 0.60, respectively. Using data from 2015 to verify the trained model, the R² value of the predicted and actual values of poverty incidence reached 0.95. With the progress in machine learning and night light remote sensing, poverty mapping combined with night time lights and machine learning can compensate for the data gap in deprived areas and provide a decision-making basis for sustainable development in poverty-stricken areas.

Fine particulate matter (PM 2.5 ) is harmful to human health. Although the relationship between urban land use and PM 2.5 has been studied in recent years, there has been little consideration of the relationship between land use structure and PM 2.5 spatiotemporal patterns at the microscale. Based on mobile monitoring PM 2.5 data and point of interest data, this paper explored their relationship with a classification and regression tree model. The results showed that PM 2.5 exhibits spatiotemporal heterogeneity at the microscale. The neighborhoods’ land use structure can explain 60.4% of the PM 2.5 spatiotemporal patterns. Transportation and ecology are the two most significant land use types that correlated with PM 2.5 spatiotemporal patterns. Fourteen rules of neighborhood land use structures with different land use types are identified land use structure which leads to different spatiotemporal patterns of PM 2.5 . The higher the PM 2.5 risk, the stronger the correlation with neighborhood land use structure is. The classification and regression tree model can be effectively used to judge the relationship between neighborhood land use structure and PM 2.5 spatiotemporal patterns. The results provide a basis for developing appropriate measures, based on local conditions, to predict PM 2.5 pollution levels at the microscale, and reduce the risk of neighborhood exposure to PM 2.5 .

In recent decades, China has experienced rapid urbanisation and institutional transition. Most studies do not test the complex impact of residential mobility at different life course stages on subjective well‐being in later life, especially in the context of this rapid transition of Chinese society. Using the complete residential mobility history of 772 people aged over 60 years obtained from a survey conducted in Guangzhou in 2016, this study analyses gender differences in the impact of residential mobility on subjective well‐being in later life through accumulation models and sensitive period models of life course. The results show that there is a clear graded relationship (gradient descending) between the accumulation of residential mobility at different stages of the life course and subjective well‐being in later life for men, which supports the accumulation model. Middle age (45–59 years) is the sensitive period for men's residential mobility, which has an independent positive impact on their subjective well‐being in later life. However, there is no evidence to support the accumulation model for women. Middle age (45–59 years) and old age (60+ years) are the sensitive periods for women's residential mobility, with independent negative effects on their subjective well‐being in later life. The mechanism of the different impacts for men and women and the implications are discussed in the context of Chinese society.

PM2.5 pollution poses a serious health risk to residents. In recent years, individual exposure to PM2.5 in daily activities has received much attention, but the impact of different activities at different times and spaces on PM2.5-triggered individual health risks has been ignored. In this paper, the residents’ activity diaries and the data on the spatial-temporal changes in urban PM2.5 concentration obtained through the land-use regression model (LUR) were coupled to dynamically evaluate and analyze the characteristics of Guangzhou residents’ PM2.5 inhaled dose in 24 hours during workdays. The study showed that the residents’ PM2.5 inhaled dose in daily activities is about 940.2 ± 94.5μg (95% CI). This can better reflect the individual differences than the exposure to PM2.5 in daily activities. For every increase of 1μg/day in the exposure to PM2.5, the inhaled dose will increase by 1.5 μg/day. The obvious spatial-temporal characteristics of PM2.5 inhaled dose are closely related to people's daily activities. The inhaled dose is high during the day and low at night, and the individual differences in inhaled dose in the commuting and leisure period is significant but is small during the working and sleeping period; if the individual inhaled dose exceeds the population’s average for accumulated 7 hours in a day, the probability of a high risk of the all-day inhaled dose increases significantly. The inhaled dose at the workplace and residence as well as in light physical activities accounts for a large proportion of the total. However, although the inhaled dose on roads and in other places, and moderate physical activities account for only a small proportion, it has a great impact on the risk level of the individual total inhaled dose. Therefore, the characteristics of PM2.5 inhaled dose and the individual activity should be considered, to propose targeted policies and suggestions from the perspective of both the government and individuals to reduce the health risk caused by PM2.5 pollution.

The impact of settlements isolation and land-use changes on poverty is often overlooked. This study explored the spatial pattern and impact of settlements isolation and land use changes on poverty in Guizhou, a mountainous province of China, based on a Pearson correlation analysis and geographically weighted regression (GWR). The results showed that the incidence of high poverty levels in the counties of Guizhou tended to move from the southwest to the southeast over the period of 2005–2015. Both settlements isolation and land use changes had an impact on poverty, and this effect displayed spatial heterogeneity. With other factors unchanged, a 1 m increase in settlements isolation was associated with a statistically significant 0.58% increase in poverty incidence. Changes in the area of crop land, water, and forest land had a significant impact on poverty. The authors suggest that in the process of resettlement, the distance between settlements should be kept less than 5000 m if possible. The government needs to further improve and implement preferential land use policies, with appropriate increases in the area of built-up land.