Dr Won Do Lee
  • Research Associate in Urban Mobility

About

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.

Current Research

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:

  1. Individual mobility patterns: “reconstructing the daily paths and activity-spaces of individuals of different social groups” using combined Big and Small data.
  2. 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.
  3. 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.

Selected Publications

Featured works are listed below (please see Google Scholar for full overview):

Journal Articles