As an early career researcher, I managed to work in three trajectories, these include; health geography, environmental health, sustainable urban transportation system, land use modelling and applying spatial analytics in the area of ecosystem service evaluation. These trajectories linked with my educational stages, from undergraduate to PhD and Postdoctoral research.
Postdoctoral Research (and co-investigator)
In my postdoctoral research, I focused on public health modelling in relation to the built environment, air pollution and active transportation. I work with the Public Health modelling group at MRC Epidemiology Unit in the University of Cambridge, lead by Dr. James Woodcock. I am working to improve health impact modeling tools for the MEthods and Tools for Assessing the Health Impacts of Transport (METAHIT) and Joining Impact models of transport with spatial measures of the Built Environment (JIBE) projects by integrating spatial data science approaches into existing impact models. Specifically, I am working with big data to model the built environment and air pollution and create a new function to be integrated into an R-package development effort for METAHIT and JIBE models. Additionally, I am partly involved with a computer vision study (using YOLO v4), part of GLASST project, that applies deep learning models to Google Street View (GSV) images to extract transportation mode share information from Global South cities. My aim here is to select the spatial placements and contexts for extracting traffic data from geotagged images and synthesizing multi-source big spatial data (e.g., OpenStreetMap-OSM) to develop deep learning-based prediction model for active travel mode share. Overall, my focus is on harmonizing data, developing new functions to streamline large geo-computation, and integrating spatial outputs with other scenarios provided by other models. In addition to technical skills, as a Co-investigator in JIBE project, I am developing my supervising skills by managing a part-time post-doctoral researcher.
My PhD research focused on the area of application of spatial analytics in understanding the connection between the natural environment (e.g. Green infrastructure) & health-wellbeing. I am analysing high-resolution spatial data to identify how the environment is changing and how such change effect human health. Particularly, I am applying machine learning models with GIS to understand and predict land use changes; modelling the green infrastructure exposure in terms of availability, accessibility and visibility at multiple scales using detailed land use land cover, DEM (LiDAR based), Google Street view data, and building new approaches to monitoring natural environment exposure considering challenges of big geo-spatial data. The main aspect of my PhD is to improve the assessment of natural environment exposure, to guide environment epidemiological research. The PhD is divided into four papers with interconnected objectives and research questions. The PhD is supervised by Professor Sarah Lindley and Dr. Jonny Huck.