top of page

Urban Sensing and Analytics

figure1

Leveraging a variety of sensing data, such as street view and satellite images, the HUB Lab excels in employing advanced techniques, including computer vision and GeoAI, for precise evaluation of the urban environment (e.g., built characteristics, urban greenery, street trees, and pedestrians). Our unwavering dedication to advancing the understanding of the built environment establishes a foundational framework for cutting-edge research at the intersection of environment, behavior, and health. Our contributions aim to foster a profound connection between the planed environment and the well-being of individuals and communities.

Related works

  • Liu, D., Lu, Y.*, & Yang, L. (2024). Exploring non-linear effects of environmental factors on the volume of pedestrians of different ages using street view images and computer vision technology. Travel Behaviour and Society, 36, 100814. (see more details)

​

  • Jiang, Y., Liu, D., Ren, L., Grekousis, G., & Lu, Y.* (2024). Tree abundance, species richness, or species mix? Exploring the relationship between features of urban street trees and pedestrian volume in Jinan, China. Urban Forestry & Urban Greening, 95, 128294. (see more details)

​

  • Wang, R., Zhang, L., Zhou, S., Yang, L., & Lu, Y.* (2024). The availability and visibility of animals moderate the association between green space and recreational walking: Using street view data. Journal of Transport & Health, 34, 101744. (see more details)​

Nexus between Environment, Behavior, and Health

figure2

The HUB Lab specializes in conducting both cross-sectional studies to investigate correlation relationships among environment, behavior, and health, and longitudinal studies to infer causal relationships. This dual expertise allows us to comprehensively demonstrate the complex interplay between environmental factors, individual behaviors, and health outcomes. Our ultimate goal is to establish a systematic and holistic framework that provides valuable insights for achieving healthy cities, offering a comprehensive illustration of the underlying nexus between environment, human behavior, and health.

Related works

  • Zhou, Y., Lu, Y.*, Wei, D., & He, S. (2024). Impacts of social deprivation on mortality and protective effects of greenness exposure in Hong Kong, 1999–2018: A spatiotemporal perspective. Health & Place, 87, 103241. (see more details)

​

  • Yang, H., Lu, Y.*, Wang, J., Zheng, Y., Ruan, Z., & Peng, J. (2023). Understanding post-pandemic metro commuting ridership by considering the built environment: A quasi-natural experiment in Wuhan, China. Sustainable Cities and Society, 96, 104626. (see more details)​

​

  • Wei, D., Lu, Y.*, Wu, X., Ho, H. C., Wu, W., Song, J., & Wang, Y. (2023). Greenspace exposure may increase life expectancy of elderly adults, especially for those with low socioeconomic status. Health & Place, 84, 103142. (see more details)

​

  • Wu, X., Chen W. Y., Zhang, K., & Lu, Y.*, (2023). The dynamic impact of COVID-19 pandemic on park visits: a longitudinal study in the United States. Urban Forestry & Urban Greening, 128154. (see more details)

Geospatial Big Data Mining and Spatial Modelling

figure 3

Drawing on multi-source geospatial big data (e.g., social media data, VGIs, GPS-based data, and remote sensing data), the HUB Lab seeks to employ a diverse set of GIS techniques, such as  spatiotemporal visualization, modelling, and simulation to uncover compelling patterns and phenomenon within cities across space and time. Our research contributes to the elucidation of complex urban dynamics, providing valuable insights for evidence-based decision-making in urban planning and advancing the pursuit of sustainable urban development.

Related works

  • Li, Z., Lu, Y.*, Zhuang, Y., & Yang, L. (2024). Influencing factors of spatial vitality in underground space around railway stations: A case study in Shanghai. Tunnelling and Underground Space Technology, 147, 105730. (see more details)

​

  • Chen, L., Zhao, L., Xiao, Y., & Lu, Y.* (2022). Investigating the spatiotemporal pattern between the built environment and urban vibrancy using big data in Shenzhen, China. Computers, Environment and Urban Systems, 95, 101827. (see more details)

​

  • Zhou, Y., & Lu, Y.* (2023). Spatiotemporal evolution and determinants of urban land use efficiency under green development orientation: Insights from 284 cities and eight economic zones in China, 2005–2019. Applied Geography, 161, 103117. (see more details)

Research Grants

6. PI: Urban Greenness and Urban Residents’ Health: A Novel Method to Assess Street Greenery. University Grants Committee of Hong Kong, General Research Fund, 01/01/21 - (on going).

 

5. PI: Urban Built Environment Optimization Strategies Based on Analysis of Outdoor Activities for School-Aged Children. (基于学龄儿童户外活动分析的城市建成环境优化策略). National Science Foundation of China, General Program, 01/01/18 - 31/12/21.

 

4. Co-PI: Grand Theaters in China from 1998 to 2015: A Study of their History, Public Space, and Design Language. University Grants Committee of Hong Kong, General Research Fund, 01/01/17 - 21/04/21.

 

3. PI: Identifying Physical Activity and Built Environment Factors Associated with Children's School Transportation Modes in Hong Kong. University Grants Committee of Hong Kong, General Research Fund, 01/01/17 - 22/06/20.

​

2. PI: Effect of the Physical Environment on the Walking Behavior of Elderly People Living in High-density Large-scale Building Complex: A Case of Hong Kong Public Housing. University Grants Committee of Hong Kong, General Research Fund, 01/01/16 - 11/06/19.

​

1. PI: A Comprehensive Measurement System and Design Strategies for the Walkability of Urban Community Built Environments. (城市社区建筑环境步行效能的综合度量体系和设计策略). National Science Foundation of China, General Program, 01/01/16 - 31/12/19.

bottom of page