Dr Yuchen Li

Profile

I am a Lecturer in Urban Data Science within the School of Geography and a member of the Institute for Spatial Data Science (ISDS) research cluster. I earned my PhD in Geography from The Ohio State University, USA. My PhD dissertation leverages advanced geospatial techniques and datasets to achieve more nuanced understanding of the spatiotemporal dynamics of the opioid overdose crisis and its social and neighborhood determinants. I am an alumnus of the Center for Urban and Regional Analysis (CURA) at Ohio State. Before joining the University of Leeds, I worked as a Research Associate at the MRC Epidemiology Unit, University of Cambridge, where I collaborated with colleagues in the Public Health Modelling group to develop open-source modelling frameworks to understand the complex relationships among the built environment, transportation behavior, and population health outcomes. I was involved in the JIBE and DARe projects, which focused on advancing research in these areas.

Building on David Harvey’s influential concept of "the right to the city," which asserts that all residents should have equal access to urban resources and a role in shaping urban spaces, I am deeply committed to finding new ways to tackle urban challenges, and to contributing solutions that have a meaningful impact on cities and their populations. With a diverse academic background that spans urban data science, geographic information science, public health, and transportation, my work focuses on addressing urban challenges through data-driven approaches. My expertise lies in areas such as spatial analysis and modelling, spatial data mining, (spatial) statistics, transportation simulation, public health modelling, and spatio-temporal analysis. By combining these methods, I aim to advance our understanding of urban environments and enhance urban well-being through innovative analytical and modelling techniques.

Research interests

Trained as a GIScientist and considering myself a health geographer, my research lies at the intersection of environmental health, urban data science, and (spatial) epidemiology. Leveraging advanced methods in geographic information science, remote sensing, and statistical modelling, I aim to address critical questions related to public health and urban systems. Below are the main directions guiding my work:

• Exploring environmental determinants of health at micro level: I study how social and environmental factors shape health outcomes at the individual level, with a focus on isolating the impact of environmental influences by systematically accounting for confoundings, such as demographic and behavioral factors. This analytic framework enables a more precise analysis of health determinants, providing robust evidence that supports targeted public health interventions
• Analysing population-level disease burden: My research extends to large-scale, multi cross-sectional studies to examine disease burden across diverse geographic regions. I utilise spatial statistics, spatial simulation, spatio-temporal analysis, spatial accessibility and optimisation analysis, and modelling techniques to uncover complex interactions among disease distribution, environmental exposures, and health behaviors. This research direction aims to inform policy by identifying effective intervention strategies for addressing public health challenges at scale.
• Broad applications of urban data science: I am also interested and engaged in various applications of urban data science, including traffic modelling, built environment auditing, social and envrionmental inequities, housing market, urban evolution, crime science etc. By diving these urban challenges through data-driven methods, I strive to provide actionable insights that support urban planning and urban well-being.

Selected publications

* indicates the corresponding author, ¹ indicates co-first authorship

1. Xu, Y., Chen, R., Du, H., Chen, M., Fu, C., & Li, Y.* (2024). Evaluation of green space influence on housing prices using machine learning and urban visual intelligence, in press, Cities
2. Yu, B., Li, Y., Jia, P., & Yang, S. (2024). Reply to “Investigating the mechanisms of PM2.5's impact on blood pressure: Establishing a three-tier response strategy” by Qiang et al., in press, Journal of Hypertension
3. Huang, Z., Xu, Y., Li, Y.*, Jiang, S., & Chen, R. (2024). Where drinks and danger meet: analyzing the spatial link between bars and crime in Detroit, Applied Geography, 174, 103480. DOI: 10.1016/j.apgeog.2024.103480
4. Xu, Y., Yan, M., Fu, C., Xu, W., Liu, Y., & Li, Y.* (2024). Complex patterns and determinants of regional multiple chronic conditions across the United States, PNAS Nexus, 3(12), pgae513. DOI: 10.1093/pnasnexus/pgae513
5. Dai, S., Li, Y., Stein, A., Yang, S., & Jia, P. (2024). Street view imagery-based built environment auditing tools: a systematic review. International Journal of Geographical Information Science, 38(6), 1136-1157. DOI: 10.1080/13658816.2024.2336034
6. Dai, S., Qiu, G., Li, Y., Yang, S., Yang, S., & Jia, P. (2024). State of the art of lifecourse cohort establishment. China CDC Weekly, 6(14), 300. DOI: 10.46234/ccdcw2024.058
7. Dong, S., Yu, B., Yin, C., Li, Y., Zhong, W., Feng, C., ... & Yang, S. (2024). Associations between PM2. 5 and its chemical constituents and blood pressure: a cross-sectional study. Journal of Hypertension, 42(11), 1897-1905. DOI: 10.1097/HJH.0000000000003795
8. Ye, T., Shao, Y., Cai, C., Li, Y., Yu, B., Qiao, X., ... & Yang, S. (2024). Association of PM2. 5 chemical constituents with general, abdominal and visceral obesity and mediation roles of physical activity. Environmental Sciences Europe, 36(1), 107. DOI: 10.1186/s12302-024-00935-4
9. Qin, K., Wang, Z., Dai, S., Li, Y., Li, M., Li, C., ... & Jia, P. (2024). Spatiotemporal patterns of air pollutants over the epidemic course: a national study in China. Remote Sensing, 16(7), 1298. DOI: 10.3390/rs16071298
10. Fan, Y., Yu, B., Liu, H., Ma, H., Ma, C., Li, Y., ... & Yang, S. (2024). Network analysis of illness perception, stigma, and resilience with cognition in old people living with HIV. Journal of Psychosomatic Research, 177, 111565. DOI: 10.1016/j.jpsychores.2023.111565
11. Cai, C., Zhu, S., Qin, M., Li, X., Feng, C., Yu, B., Dai, S., Qiu, G., Li, Y., ... & Yang, S. (2024). Long-term exposure to PM2. 5 chemical constituents and diabesity: evidence from a multi-center cohort study in China. The Lancet Regional Health–Western Pacific, 47. DOI: 10.1016/j.lanwpc.2024.101100
12. Li, Y.*, Miller, H. J., Hyder, A., & Jia, P. (2023). Understanding the spatiotemporal evolution of opioid overdose events using a regionalized sequence alignment analysis. Social Science & Medicine, 334, 116188. DOI: 10.1016/j.socscimed.2023.116188
13. Yin, C.¹, Peng, N.¹, Li, Y.¹, Shi, Y., Yang, S., & Jia, P. (2023). A review on street view observations in support of the sustainable development goals. International Journal of Applied Earth Observation and Geoinformation, 117, 103205. DOI: 10.1016/j.jag.2023.103205
14. Liu, Y., Xu, Y., Li, Y., & Wei, H. (2023). Identifying the environmental determinants of lung cancer: A case study of Henan, China. GeoHealth, 7(6), e2023GH000794. DOI: 10.1029/2023GH000794
15. Liu, H., Feng, C., Yu, B., Ma, H., Li, Y., Wu, J., ... & Yang, S. (2023). Influences of long‐term care insurance on pulmonary and urinary tract infections among older people with disability. Journal of the American Geriatrics Society, 71(12), 3802-3813. DOI: 10.1111/jgs.18554
16. Li, Y.*, Miller, H. J., Root, E. D., Hyder, A., & Liu, D. (2022). Understanding the role of urban social and physical environment in opioid overdose events using found geospatial data. Health & Place, 75, 102792. DOI: 10.1016/j.healthplace.2022.102792
17. Stiles, J., Li, Y., & Miller, H. J. (2022). How does street space influence crash frequency? An analysis using segmented street view imagery. Environment and Planning B: Urban Analytics and City Science, 49(9), 2467-2483. DOI: 10.1177/23998083221090962
18. Li, Y., Hyder, A., Southerland, L. T., Hammond, G., Porr, A., & Miller, H. J. (2020). 311 service requests as indicators of neighborhood distress and opioid use disorder. Scientific reports, 10(1), 19579. DOI: 10.1038/s41598-020-76685-z
19. Li, Y., & Xie, Y. (2018). A new urban typology model adapting data mining analytics to examine dominant trajectories of neighborhood change: a case of metro detroit. Annals of the American Association of Geographers, 108(5), 1313-1337. DOI: 10.1080/24694452.2018.1433016