Data-driven Land Use/Land Cover Research
There is a need for innovative methods for mapping land surface because of the high demand for reliable and real-time land use and land cover maps by the scientific community, policy makers, and investors. Meanwhile, the unprecedented growth of geospatial big data from satellite images and unconventional sources such as phone networks usage, credit card transactions and data gathered from geo-located social networks, created opportunities to advance methodologies for land surface mapping.
My research work is focused on studying land use/land cover change through the lens of spatial big data for a variety of outcomes, specifically human health and urban sustainability. My first research goal is to leverage advances in high-performance computing and machine/deep learning for analyzing land use and land cover data at a scale. My second goal is to study emerging spatial-temporal phenomena, such as outbreaks of contagious diseases and the rapid change of Arctic landscapes, in the context of the current increasing trends of land use or land cover spatial heterogeneity and connectivity induced mainly by human activities and movements.
I am also active in engaging different research communities to use cyberinfrastructure platforms for scientific collaboration, sharing big data, and accelerating discoveries, specifically in the circles of Disease Ecology and Cryospheric Science.
The existing of three hubs in a subset of the commuting network of Chicago demonstrates the hierarchical organization of urban land use. My research focuses on studying emergent properties of land use/cover in different contexts such as studying the spread of infectious diseases. (Data credits: census.gov)