We conduct research about
Sustainable Regional DevelopmentThe Geography of DevelopmentRegional Growth and InequalitySpatial Structural ChangeSpatial Energy Economics

Quantitative Regional and Computational Science

Research Projects and Outcomes
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Our mission

The QuaRCS Network promotes interdisciplinary science to foster economic, social, and environmental sustainability.

  • Spatial data science
  • Machine learning
  • Remote sensing
  • Economics
Who we are

ABOUT US

The QuaRCS Network is an international, interdisciplinary research network in Quantitative Regional and Computational Science. We combine development economics, spatial data science, machine learning, and satellite remote sensing to understand and inform the process of sustainable development — in its economic, social, and environmental dimensions — across subnational regions and countries.

The QuaRCS network is part of the UN Sustainable Development Solutions Network (SDSN), mobilizing scientific knowledge and innovation to promote sustainable development worldwide.

QuaRCS Network — interdisciplinary research for sustainable regional development
What we study

Research Areas

Five connected research lines advancing the economic, social, and environmental dimensions of sustainable development.

Sustainable Regional Development

The quantitative geography of sustainable development — remote sensing, spatial econometrics, and machine learning.

The Geography of Development

Mapping the wealth and inequality of subnations from outer space with satellite remote sensing.

Regional Growth and Inequality

Growth, inequality, and convergence across regions and countries through spatial econometrics and machine learning.

Spatial Structural Change

Modeling subnational wealth through spatial spillovers and finding development clusters with machine learning.

Spatial Energy Economics

The spatial dimensions of energy access, poverty, and the low-carbon transition.

How we work

Research methods

State-of-the-art methods from spatial data science, machine learning, remote sensing, and economics.

Modern data science

A modern, reproducible data-science workflow across all our projects.

Applied econometrics

The latest advances in applied econometrics, put to work on real questions.

Machine learning

Clustering algorithms from the unsupervised machine-learning literature.

Spatial econometrics

Exploratory spatial and space-time analysis, dependence, and heterogeneity.

Remote sensing

Satellite imagery and nighttime lights to measure development from outer space.

Bayesian econometrics

Bayesian model averaging and inference under uncertainty.

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Activities

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