Francisco Rowe

Francisco Rowe

Senior Lecturer in Quantitative Human Geography

University of Liverpool

Biography

Francisco Rowe is a Senior Lecturer in Human Quantitative Geography at the Department of Geography and Planning within the University of Liverpool, lead of the Geographic Data Science Lab. His areas of expertise are: internal & international migration; human mobility; and computational social science. 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 editor of REGION, 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 Spatial Economic Analysis in 2018 and having top articles in the top 10 most read articles in Spatial Economic Analysis (2017), Transportation Research Part C (2018) & Population Studies (2018).

Download my CV.

Interests
  • Human Mobility and Migration
  • Economic Geography and Spatial Inequality
  • Computational Social Science
Education
  • 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

Recent Posts

Projects

*
Using large scale social media data to measure perceptions towards immigration

Using large scale social media data to measure perceptions towards immigration

This project aims to develop analytical methods to monitor public opinions towards immigration using Twitter data and machine learning.

Using Machine Learning to Estimate Global Bilateral Migration Flows

Using Machine Learning to Estimate Global Bilateral Migration Flows

This project aims to generate annual country-to-country migration estimates across the world.

COVID19 Generating actionable evidence for containing misinformation to prevent discrimination

COVID19 Generating actionable evidence for containing misinformation to prevent discrimination

The project aims to generate fundamental and timely evidence for how misinformation and fake news spreads across media platforms.

Sensing global patterns and trajectories of socio-economic inequality

Sensing global patterns and trajectories of socio-economic inequality

This project aims to measure and analyse the evolution of spatial inequality across the world using remote sensing.

Understanding and Predicting the Long-term Labour Market and Migration Trajectories of Immigrants and Their Children in the United Kingdom

Understanding and Predicting the Long-term Labour Market and Migration Trajectories of Immigrants and Their Children in the United Kingdom

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.

Using satellite imagery to measure the evolution of cities

Using satellite imagery to measure the evolution of cities

The project aims to develop and employ analytical approaches to measure the evolution of cities using machine learning and satellite imagery.

Understanding the declining trend in internal migration in Europe

Understanding the declining trend in internal migration in Europe

The project aims to establish the start and pace of the migration decline in 18 European countries.

Recent & Upcoming Talks

Using Machine Learning and Twitter Data to Profile Attitudes Towards Immigration
Immigration is a key ingredient for social cohesion and economic development. Yet, it is often portrayed as a major threat to national identity, values, economic stability and security, resulting in acts of intolerance, discrimination, racism, xenophobia and violent extremism. Understanding how misperceptions towards immigration are formed and shaped is key to address combat mis-representations of immigrants. Typically attitudes towards immigration are studied based on qualitative and nationally representative surveys but they offer low population coverage, coarse geographical resolution and slow data collection. Social media offers dynamic and open space to better understand experiences and public opinion about immigration. While some bias exists, social media data are produced at unprecedented temporal frequency, geographical granularity and is accessible in real time. This paper aims to measure and better understand attitudes towards immigration in Chile using Twitter data. Key findings indicate that negative attitudes emerge from a reduced number of users, and are more commonly manifested and intensify during negative immigrant news reflecting arguments of job competition and stricter immigration regulation. Positive attitudes are expressed by a more diffused number of users and are predominantly express to manifest support during specific events reflecting supportive arguments for immigrants’ human and civil rights.
Using Machine Learning and Twitter Data to Profile Attitudes Towards Immigration

Recent Publications

Quickly discover relevant content by filtering publications.