Spatial Lifecourse Health was proposed by my supervisor, Prof. Peng Jia in Lancet Planetary Health. It is an emerging framework that integrates life-course theory with spatial thinking to understand how places shape health across the life span, and it is now also applied to infectious diseases such as the COVID-19 pandemic. The top 10 research priorities in spatial lifecourse epidemiology and the ISLE-ReSt reporting standards have been published to guide this field. My PhD project applies Earth observation (Google Earth Engine), geospatial big data (street view image segmentation), and spatial statistical models to health geography and spatial epidemiology, covering obesity, infectious diseases, non-communicable diseases, and air pollution exposure.

1 Obesity and obesogenic environments

We conducted a series of systematic reviews in Obesity Reviews to determine all the obesogenic environmental factors of childhood obesity, covering the natural environment, green space, bike lanes, and speed limits, and further proposed a framework connecting built environments at different geographic contexts—residences, commute routes, and workplaces—with obesity.

• Natural environment
• Green space
• Bike lanes
• Speed limit

The framework paper was published in The Lancet Global Health, and the residence–commute–workplace study was published in Cities

2 COVID-19 pandemic

The COVID-19 pandemic became a natural laboratory for spatial lifecourse health. I built Awesome of COVID-19, a website collecting COVID-19 research resources, and developed a COVID-19 shiny app for spatiotemporal visualization.

Figure 1. Snapshot of the website “Awesome of COVID-19”.

Figure 2. Snapshot of the COVID-19 shiny app.

We also raised awareness of reverse zoonoses (i.e., human–animal transmission of COVID-19), calling for up-to-date methods to improve the control, management, and prevention of cross-species transmission at both individual and regional/national levels.

Figure 3. COVID-19 natural infections of pet, zoo, and livestock animals as of 11 March 2021, mapped onto the number of confirmed human cases.

Trends论文献策疫情中被忽视的“人-动物”感染风险 | Cell Press论文速递

The paper was published in Trends in Ecology & Evolution, and the heterogeneous impacts study was published in Landscape Ecology

Our contributions to COVID-19 epidemic prevention and control were also recognized by the Health Commission of Tumot Left Banner, Inner Mongolia.

Figure 4. The certificate of outstanding contributions to COVID-19 epidemic prevention and control.

3 Health effects of air pollution exposure

Based on the daily 1-km PM2.5 mapping products, we evaluated the short-term health effects of PM2.5 exposure during the Spring Festival in 25 Yangtze River Delta cities using the attributable fraction (AF) and OD matrices, and further extended the exposure–health chain to long-term chemical constituents and diabesity risks in a national cohort, as well as real-time blood pressure responses measured by wearables.

Figure 5. The time variation and OD matrices of AF.

【建成环境与行为研究】春节期间的PM2.5污染短期暴露健康效应评估——以长三角地区25个城市为例 | 上海城市规划

The cohort study was published in The Lancet Regional Health – Western Pacific, and the wearable study was published in Journal of Urban Health

4 Built environment auditing and physical activity assessment

Street view imagery makes it possible to audit built environments at eye level. Through a systematic review of SVI-based built environment auditing tools, we synthesized the audited attributes, the detection models behind them, and their reliability across countries and research groups, and identified standardized tools (such as ANC and MAPS) as the most widely accepted auditing instruments.

Figure 6. An overview of the studies using different built environment auditing tools in different countries, (a) represents the number of these tools within different countries (in parentheses), (b) represents the number of these tools that have been applied by different groups and in various studies.

The review was published in IJGIS

We further developed a bikeability evaluation framework by fusing multi-source spatiotemporal big data, revealing the daily and hourly dynamics of cycling friendliness on Xiamen Island.

Figure 7. The proposed bikeability framework.

Figure 8. Average bikeability of Xiamen Island on December 21st, 22nd, 23rd, 24th, and 25th, 2020; the roads highlighted in red indicate lower levels of bikeability, whereas those in green indicate higher levels.

Figure 9. Average bikeability of Xiamen Island at 6:00, 7:00, 8:00, and 9:00 a.m.

《Int J Appl Earth Obs》发文：基于多源地理空间大数据的时空自行车可行性评估

The paper was published in International Journal of Applied Earth Observation and Geoinformation

5 Population and demographic mapping

Fine-scale population structure is the foundation of exposure and health assessment. We developed a contextualized geographically weighted neural network (CGWNN) for high-resolution population density mapping, and further estimated complete sex and age structures on 100 m grid cells by fusing census data, remote sensing imagery, and geotagged social media data with a multi-output random forest model.

Figure 10. Demographic composition data coverage and the distribution of geotagged social media data in Chengdu City.

The CGWNN paper was published in Applied Geography, and the demographic composition mapping was published in IJGIS