Francisco Rowe is the lead of the Geographic Data Science Lab and Senior Lecturer in Quantitative Human Geography at the Department of Geography and Planning within the University of Liverpool. His areas of expertise are: internal & international migration; human mobility; and geographic data science. Francisco is featured in the Experts Database of the United Nations Network on Migration and two of his projects on Big Data, machine learning and migration are listed in the Data Innovation Directory of the International Organization for Migration. He has been invited to present his research at the United Nations Population & Development Division in New York and works closely with the Global Migration Data Analysis Centre within International Organization for Migration, the United Nations Economic Commission for Latin America and the Caribbean, the UK2070 Commission, UK’s government organisations, including the Ordnance Survey and the ONS Data Campus, and commercial companies, Geolytix. His work contributed to the United Nations Expert group meeting on `sustainable cities, human mobility and international migration', and the ONS Government Statistical Service Advisory Committee. Francisco is the current managing editor of REGION (2022-present), the journal of the European Regional Science Association (2018-present) and social media editor at the Journal of the Royal Statistical Society Series A (2021-present). The international reach of his research has been recognised by an award for the best paper published in the journal of Geographical Systems (2021) and in Spatial Economic Analysis (2018) and having top articles in the top 10 most read articles in Spatial Economic Analysis (2017-present), Transportation Research Part C (2018-2019) & Population Studies (2018-present).
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PhD in Economic Geography, 2013
University of Queensland
MSc in Regional Science, 2008
Universidad Catolica del Norte
BA in Business Management, with specialisation in Economics, 2007
Universidad Catolica del Norte
This project aims to develop analytical methods to monitor public opinions towards immigration using Twitter data and machine learning.
This project aims to generate annual country-to-country migration estimates across the world.
The project aims to generate fundamental and timely evidence for how misinformation and fake news spreads across media platforms.
This project aims to measure and analyse the evolution of spatial inequality across the world using remote sensing.
This project aims to investigate how the educational and employment trajectories of immigrants and their children in the UK evolve and interact; and, how factors related to their residential environment, early life context and critical life transitions shape these trajectories between 1991-2017.
The project aims to develop and employ analytical approaches to measure the evolution of cities using machine learning and satellite imagery.
The project aims to establish the start and pace of the migration decline in 18 European countries.