- Research Associate in Urban Mobility
- Email: email@example.com
Won Do Joined TSU as a Research Associate in Urban Mobility in December 2019. Won Do serves to develop the research project entitled “People-based exposure measurement”, to estimate the branch of (ambient) environmental risks with respect to day-to-day population flows in TSU. He is a quantitative geographer, who enjoys working with a multidisciplinary research domain. Won Do has been collaborating closely with transportation engineers and human geographers in his early academic career. Methodologically, Won Do has worked extensively with geospatial data analysis, geographical data modelling, and Geovisualisation.
Prior to joining TSU, he was a Research Associate at the Crime and Well-being Big Data Centre, at Manchester Metropolitan University (2016-2019). During this time, he was involved in the development of bespoke analytical approaches for policing demand, funded by Greater Manchester Police, which aims to understand the evidence-based policing demand focused on people, places, and their partnerships by applying advanced quantitative methodologies, mainly to employ Geostatistics and machine-learning techniques.
Won Do's key methodological research interest is spatial (or spatio-temporal) statistics and modelling approaches for urban geography and transport using Small and Big data. His research focuses on the geographies of the everyday mobilities of people, goods and information in TSU. To assess how spatio-temporal geographical contexts influence individual mobility, not only experimenting the dynamic measurements, but also coupling with precise insights of interpersonal variability in everyday mobilities.
In line with this idea, he will develop an overarching framework for people-based exposure measurement. To examine the spatio-temporal environmental risks (e.g. air pollutant emission) with respect to individual daily mobility patterns. It is not only estimating the population-at-risk, in terms of time-varying ambient population estimates across space, but also take both social contexts and everyday mobility practices into consideration. It also allows to identify the vulnerable social groups, and calculate spatio-temporal population-at-risk. In order to assess the influence of time-variant exposed population-at-risk upon the spatial and temporal patterning of environmental risks.
To be specific, Won Do will pay attention to three empirical themes:
- Individual mobility patterns: “reconstructing the daily paths and activity-spaces of individuals of different social groups” using combined Big and Small data.
- Estimating spatio-temporal ambient population: “where and how much time people spend while engaged in their activities” in a given urban setting at a particular time.
- People-based exposure measurement: to estimate spatio-temporal population-at-risk, incorporated with the interpersonal variability of time-varying population. To do so, allows us to examine the contexts and interaction effects between individual mobility patterns and neighbourhood effects.
Featured works are listed below (please see Google Scholar for full overview):
- Ellison, M., Bannister, J., Lee, W.D. and Haleem, M.S. (2021) Understanding policing demand and deployment through the lens of the city and with the application of big data. Urban Studies. 004209802098100.
- Lee, W.D., Qian, M. and Schwanen, T. (2021) The association between socioeconomic status and mobility reductions in the early stage of England's COVID-19 epidemic. Health and Place. 102563.
- Haleem, M.S., Lee, W.D., Ellison, M. and Bannister, J. (2020) The 'Exposed' Population, Violent Crime in Public Space and the Night-time Economy in Manchester, United Kingdom. European Journal on Criminal Policy and Research.
- Lee, W.D., Haleem, M.S., Ellison, M. and Bannister, J. (2020) The Influence of Intra-Daily Activities and Settings upon Weekday Violent Crime in Public Spaces in Manchester, UK. European Journal on Criminal Policy and Research.
- Lee, W. Do, Ectors, W., Bellemans, T., Kochan, B., Janssens, D., Wets, G., et al. (2018) Investigating pedestrian walkability using a multitude of Seoul data sources. Transportmetrica B: Transport Dynamics, 6(1): 54-73.
- Ectors, W., Reumers, S., Lee, W. Do, Kochan, B., Janssens, D., Bellemans, T., et al. (2018) Optimizing copious activity type classes based on classification accuracy and entropy retention. Future Generation Computer Systems.
- Hwang, J.H., Kim, H., Cho, S., Bellemans, T., Lee, W. Do, Choi, K., et al. (2017) An examination of the accuracy of an activity-based travel simulation against smartcard and navigation device data. Travel Behaviour and Society, 7: 34-42.
- Ectors, W., Reumers, S., Lee, W. Do, Choi, K., Kochan, B., Janssens, D., et al. (2017) Developing an optimised activity type annotation method based on classification accuracy and entropy indices. Transportmetrica A: Transport Science, 13(8): 742-766.
- Choi, J., Lee, W. Do, Park, W.H., Kim, C., Choi, K. and Joh, C.-H. (2014) Analyzing changes in travel behavior in time and space using household travel surveys in Seoul Metropolitan Area over eight years. Travel Behaviour and Society, 1(1): 3-14.