Wanlu Chi | Talent, Space, and AI
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Project 01Data-driven exploration

Global Talent Flows

Project pipeline
Framing
Design
Data Analysis
Interpretation
Development

This project maps how skilled and highly educated workers circulate across metropolitan regions, and how those flows relate to wages, sector composition, and institutional contexts. The aim is to make uneven opportunity structures visible rather than treating mobility as a neutral choice set.

§01

Mission

To describe and compare talent circulation across cities using reproducible indicators, and to connect those patterns to debates on spatial inequality and global labour markets.

§02

Core idea

Mobility data rarely speak for themselves. The project treats flows as outcomes of overlapping systems—employer demand, visa and recruitment channels, housing markets, and professional networks—rather than as individual optimisation paths.

  • City-to-city flow matrices and concentration metrics
  • Linkage to sectoral and wage proxies where available
  • Emphasis on interpretability over single “best” rankings
§03

Current emphasis

Work is concentrated on validating sources, harmonising geographic units, and documenting uncertainty in cross-national comparisons before drawing substantive claims.