Urban Analytics for Sustainable Digital Transformation in Cities

This is the most urgent topic in post-pandemic planning to which urban researchers can contribute. After the lockdown in Shanghai from March to May 2022, Nucleic Acid Testing and Polymerase Chain Reaction Sampling Booth Spatio-temporal Optimization Platform needs to be optimized urgently to protect public resources and provide better service to various communities. In this regard, our laboratory and Shanghai Big Data Center had relevant research and produced a report titled, "Spatiotemporal Optimization of PCR Sample Collection Booths in Shanghai".

Projects

Spatiotemporal Optimization of PCR Sample Collection Booths in Shanghai

Spatiotemporal Optimization of PCR Sample Collection Booths in Shanghai

Publications

  1. Wu, L., Zhou, Y., Nie, X., Kim, S. K., Shao, Y.*, & Guan, C.* (2026). Buffering effects of green space on residents' mental wellbeing: A regression discontinuity analysis during COVID containment policy shift. Cities, 168, 106413. (SSCI) https://doi.org/10.1016/j.cities.2025.106413 

  2. Wu, L.*, Zhang, J., Jia, C., & Guan, C.* (2025). How COVID-19 Policy Shift Affects Mental Wellbeing? Assessing the Effect Heterogeneity of Urban Water Body Exposures. Applied Spatial Analysis and Policy, 18(3), 87. (SSCI) https://doi.org/10.1007/s12061-025-09686-y 

  3. Wang, Y., & Guan, C.* (2025). Integrating green space measures into future town planning in Zhejiang, China. Environment and Planning B: Urban Analytics and City Science, 52(4), 913-930. (SCIE) https://doi.org/10.1177/23998083241274913 
  4. Guan, C.*, You, M., Li, Y., Tan, J., & Jenq, C. (2024). Analyzing adverse effects of subway extension on housing prices in affluent urban neighborhoods. Applied Geography, 166, 12, 103265. (SSCI) https://doi.org/10.1016/j.apgeog.2024.103265 
  5. Deng, H. , Hu, Q., Guan, C.* , Chen, Y. , & Menendez, M. (2024). Building a government-owned open data platform for connected and autonomous vehicles. Cities, 149, 16,  104918.(SSCI) https://doi.org/10.1016/j.cities.2024.104918 
  6. Chen, Z. , Liu, Y. , Yan, P.*, & Guan, C. (2024). Two-stage deployment and operation plan of PCR sample collection booths under demand uncertainty. Sustainable Cities and Society, 105, 20, 105315.(SCIE) https://doi.org/10.1016/j.scs.2024.105315 
  7. Wu, L., Zhou, Y., Nie, X., Kim, S., Shao, Y.*, & Guan, C.*(2024). "Unfenced" parks and residents' visit patterns: A regression discontinuity design in Shanghai. Urban Forestry & Urban Greening, 100, 8,  128459.(SCIE&SSCI) https://doi.org/10.1016/j.ufug.2024.128459 

  8.  

    Cai, Z. , Cheng, T.*, Li, Y., Guan, C. *, & Tang, J.  (2024). Spatial Analysis of Hospital Accessibility in Nanjing: The Role of Community Health Service Centers . Applied Spatial Analysis and Policy, 17(3), 1081-1103.(SCIE) https://doi.org/10.1007/s12061-024-09577-8 

  9. Hong, A., Baker, L., Curiel, R. , Duminy, J.*, Buswala, B., Guan, C. , & Ravindranath, D. (2023). Reconciling big data and thick data to advance the new urban science and smart city governance. Journal of Urban Affairs, 45(10), 1737-1761.(SSCI) https://doi.org/10.1080/07352166.2021.2021085 
  10. Guan, C., Yan, K., Zhang, B., Li, Y., & Zhang, X. (2023) The Efficacy of Mortgage Lending on Geographically Disparate and Differently Priced Residential Neighborhoods in Shanghai. The Professional Geographer (SSCI) https://doi.org/10.1080/00330124.2022.2148902  
  11. Guan, C., Wang, Y., Keith, M., Li, Y., Cao, G.* (2022) Does local planning of fast-growing medium-sized towns lead to higher urban intensity or to sprawl? Cases from Zhejiang Province. Cities 130, 103869 (SSCI) https://doi.org/10.1016/j.cities.2022.103869
  12. Cai, Z., Guan, C.*, Trinh, A., Zhang, B., Chen, Z., Srinivasan, S., Nielsen, C. (2022) How to leverage urban residents’ satisfactions on self-perceived health? Neighborhood social and built environment effects in Chengdu, China. Sustainability 14(20), 13389. (SSCI) https://doi.org/10.3390/su142013389
  13. Bogdanowicz, A., Guan, C.* (2022) Public sentiment during the COVID-19 pandemic: Dynamic topic modeling of Twitter data in the United States. PLOS ONE (SCIE) https://doi.org/10.1371/journal.pone.0268669
  14. Guan, C.*, Tan, J., Hall, B., Liu, C., Li, Y., Cai, Z. (2022) The effect of the built environment on the COVID-19 pandemic at the initial stage: a county level study of the United States. Sustainability 14(6), 3417. (SCI & SSCI) https://doi.org/10.3390/su14063417
  15. Shi, Y., Yang, J. Keith, M., Song, K., Li, Y., Guan, C.*. (2022) Spatial accessibility patterns to public hospitals in Shanghai: An improved gravity model. The Professional Geographer 74(2), 265-289. (SSCI) https://doi.org/10.1080/00330124.2021.2000445
  16. Yao, Z., Yang, J., Liu, J., Keith, M., Guan, C.* (2021) Comparing Tweet sentiment in megacities using machine learning techniques: In the midst of COVID-19. Cities 116, 103273. (SSCI) https://doi.org/10.1016/j.cities.2021.103273
  17. Liu, C., Liu, Z., Guan, C. (2021) The impacts of the built environment on the incidence rate of COVID-19: A case study of King County, Washington. Sustainable Cities & Society 74, 103144 (SCI) https://doi.org/10.1016/j.scs.2021.103144
  18. Guan, C.*, Rowe, P. (2021) Beyond big versus small: assessing spatial variation of urban neighborhood block structures in high‑density cities. Socio-Ecological Practice Research 3, 37-53. (CNKI) https://doi.org/10.1007/s42532-021-00074-7
  19. Guan, C.*, Tan, J., Peiser, R. (2021) Spatiotemporal effects of proximity to metro extension on housing price dynamics in Manhattan, New York City. Journal of Transport and Land Use 14(1) 1295-1315. (SSCI) https://doi.org/10.5198/jtlu.2021.1915
  20. Guan, C.* and Forsyth, A. (2020) The influence of urban form and socio-demographics on active transport: A 40-neighborhoods study in Chengdu, China. Journal of Transport and Land Use, 13(1), 367-388. (SSCI) https://doi.org/10.5198/jtlu.2020.1697
  21. Guan, C.*, Srinivasan, S., Zhang, B., Da, L., Liu, J., Nielsen, C. (2020) The influence of neighborhood types and accessibility on active transport in China’s growing cities. Transportation Research Part D: Transport and Environment, 80, 102273. (SCI&SSCI) https://doi.org/10.1016/j.trd.2020.102273
  22. Srinivasan, S., Guan, C.*, Nielsen, C. (2020) Built environment, income and travel behavior: Change in the city of Chengdu, China 2005-2016. International Journal of Sustainable Transportation 14:10, 749-760. (SSCI) https://doi.org/10.1080/15568318.2019.1625088
  23. Guan, C. (2019) Spatial distribution of high-rise buildings and its relationship to public transit development in Shanghai. Transport Policy 81, 371-380. (SSCI)  https://doi.org/10.1016/j.tranpol.2018.09.004
  24. Guan, C.*, Srinivasan, S., Nielsen, C. (2019) Does neighborhood form influence low-carbon transportation in China? Transportation Research Part D: Transport and Environment 67, 406-420. (SCI&SSCI) https://doi.org/10.1016/j.trd.2018.12.015
  25. Guan, C.*, Keith, M., Hong, A., Wang, Y. (2019) Designing walkable cities and neighborhoods in the era of urban big data. Urban Planning International 34 (5) 9-15. DOI:10.22217/upi.2019.389
  26. Guan, C.*, Peiser, R. (2018) Accessibility, urban form, and property value: A study of Pudong, Shanghai. Journal of Transport and Land Use, 11(1) 1057-1080. (SSCI) http://dx.doi.org/10.5198/jtlu.2018.1318
  27. Guan, C.*, Rowe, P. (2016). The concept of urban intensity and China’s townization policy: Cases from Zhejiang Province. Cities, 55, 22-41. (SSCI) https://doi.org/10.1016/j.cities.2016.03.012