1.Geospatial flow analytical methods

Many types of spatial movements/flows, including pedestrian flows and vehicle flows, are constrained by and distribute on spatial networks. Given the bias of existing flow pattern analytical methods due to the lack of consideration of the network space where flows occur and distribute, we proposed analytical methods for analyzing density and spatial dependency of network-constrained flows, which provide useful knowledge and references for other studies on the impact of network constrains, distance measures and simulation methods on the detection of spatial patterns of network constrained flows.

Kan, Z., Kwan, M.P. and Tang, L., 2021a Ripley’s K-function for network-constrained flow data. Geographical Analysis.

Tang, L., Kan, Z.*, Zhang, X., et al., 2016. A network kernel density estimation for linear features in space–time analysis of big trace data. International Journal of Geographical Information Science, 30(9), pp.1717-1737.

2.Fine-grained analysis of traffic dynamics and individual exposures to traffic congestions and emissions

My research also addresses environmental challenges using geospatial analytical methods and environmental models. I proposed approaches for estimating travel time and quantifying traffic congestion at each turn at a road intersection using taxis’ GPS trajectories. I further I proposed approaches to estimate vehicular fuel consumption and emissions to quantify the impact of human movement on our environment.

Kan, Z., Kwan, M.P., Liu, D., Tang, L., Chen, Y. and Fang M., 2022. Assessing individual activity-related exposures to traffic congestion using GPS trajectory data. Journal of Transport Geography, 98, 103240.

Kan, Z., Wong, M. S., Zhu, R., 2020. Understanding space-time patterns of vehicular emission flows in urban areas using geospatial technique. Computers, Environment and Urban Systems, 79, 101399.

Kan, Z., Tang, L., Kwan, M.P. et al., 2019. Traffic congestion analysis at the turn level using Taxis’ GPS trajectory data. Computers, Environment and Urban Systems, 74, pp.229-243.

Kan, Z., Tang, L., Kwan, M. P. et al., 2018a. Estimating vehicle fuel consumption and emissions using GPS big data. International Journal of Environmental Research and Public Health, 15(4), pp.566.

Kan, Z., Tang, L., Kwan, M.P., et al., 2018b. Fine-grained analysis on fuel-consumption and emission from vehicles trace. Journal of cleaner production, 203, pp.340-352.

3.COVID-19 risk assessment using individual contact-tracing and large geospatial datasets

I also examined the dynamics of disease transmission and the potential exposures of different socioeconomic groups to disease risks. During the COVID-19 pandemic, identifying the space-time patterns of high-risk areas of COVID-19 transmission is critical for developing targeted intervention measures in response to the pandemic. My studies identified areas with a higher risk of COVID-19 transmission in Hong Kong and analyze the associated built environment and socioeconomic factors using contact tracing data of individual confirmed cases and various geospatial datasets including 3D building datasets, land cover data, and airborne LiDAR data.

Kan, Z., Kwan, M.P., Huang, J., Wong, M.S. and Liu, D., 2021. Comparing the space-time patterns of high-risk areas in different waves of COVID-19 in Hong Kong. Transaction in GIS. 00, 1– 20.

Kan, Z., Kwan, M.P., Wong, M.S., Huang, J. and Liu, D., 2021. Identifying the space-time patterns of COVID-19 risk and their associations with different built environment features in Hong Kong. Science of the Total Environment, 772, 145379.

Huang, J., Kwan, M.P. and Kan, Z., 2021. The superspreading places of COVID-19 and the associated built-environment and socio-demographic features: A study using a spatial network framework and individual-level activity data. Health & Place, 102694.

Huang, J., Kwan, M. P., Kan, Z. et al. 2020. Investigating the relationship between the built environment and relative risk of COVID-19 in Hong Kong. ISPRS International Journal of Geo-Information, 9(11): 624.