NIGHTTIME LIGHTS
Map human activity
Explore the geography of nighttime lights and discover how economic activity is distributed across places.
Explore the mapOn the geography of development
Monitoring Local Development
from outer space
Our mission is to promote interdisciplinary science for economic, social, and environmental sustainability. We integrate insights from development economics, spatial data science, and applied econometrics to understand and inform the process of sustainable development.
Carlos MendezAssociate Professor · Nagoya University, Japan
Earth at night VIIRS / 2016 · QuaRCS network
Earth at night · AsiaDrag horizontally or use the arrow keys to rotate. Use the buttons to pause, change region, or zoom.
01Map disparities✦02Explain regional change✦03Inform sustainable development✦
01 / THE RESEARCH QUESTIONS
Why do subnational regions follow different development paths?
Satellite observations reveal patterns. Spatial econometrics, causal inference, and machine learning help us investigate the forces behind them.
The QuaRCS Network is an international and interdisciplinary research network in Quantitative Regional and Computational Science. We integrate insights from economics and spatial data science to understand and inform the process of sustainable development across subnational regions and countries. Our focus spans the economic, social, and environmental dimensions of sustainable development.
Six connected research areas
- Sustainable regional development
- The geography of development
- Regional growth and inequality
- Spatial structural change
- Spatial energy economics
- Natural disasters and infrastructure
CHANGE OVER TIME
Follow regional change
Compare luminosity across regions and over time. Start with a pattern, then ask what might explain it.
Explore the trendsPOPULATION & PLACE
Put places in context
Explore population patterns alongside the geography of light to frame new questions about people and development.
Explore population
02 / BEHIND THE RESEARCH
Economics meets
spatial data science
I’m Carlos Mendez, Associate Professor of Development Economics at Nagoya University, Japan. My research aims to integrate development economics, spatial data science, and applied econometrics to understand and inform the process of sustainable development.
My current research
- Geospatial inequality, poverty, and growth interactions
- Geospatial big data analytics and impact evaluation
- Regional infrastructure, natural disasters, and structural change
QUARCS LAB / NAGOYA
Meet the QuaRCS lab
Our lab brings researchers and students together to study regional development through economics, spatial data, and computational methods.
Abdulah Rusli (Indonesia)PhD student 2023-2027
Bimo Arvianto (Indonesia)PhD student 2025-2028
Cesar Echevarria (Peru)PhD student 2024-2027
Favio Leiva CárdenasPhD student 2023-2027
He Du (China)PhD Student, 2025 to 2028
Laura Constanza Prieto Granados (Colombia)PhD student 2026-2029
Li Xiaomeng (China)PhD student 2025-2028
Mujkanovic Adin (Bosnia and Herzegovina)Master student 2025-2027
Phon Sophat (Cambodia)PhD student 2024-2027
Sour Heng (Cambodia)PhD student 2023-2026LIFE AT THE QUARCS LAB
Shared moments of the QuaRCS lab
A glimpse of life at the QuaRCS lab through the years.
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03 / RESEARCH IN FOCUS
Featured articles
All publications
Harmonized luminosity and economic activity across provinces in China: Cross-sectional differences, regional time series, and inequality dynamics
Satellite images of lights at night can track how China's provinces grow, but the link between light and income shifts over time and weakens during downturns. Newer satellite data measure economic activity more accurately than older data, especially for industry and services.
Mapping the dimensions of poverty through big data, socioeconomic surveys and machine learning in Cambodia
Up-to-date poverty data are scarce in Cambodia. We combine household surveys with satellite data, such as nighttime lights, and machine learning to map, down to the household level, where people lack basics like clean water, sanitation, electricity, and education.
Bayesian average of classical estimates for panel data: Can the puzzle of the shape of the regional Kuznets curve be solved?
Do regional gaps within a country first widen and then narrow as the country gets richer? We find that this inverted-U pattern holds, and that natural resource wealth, farmland, and ethnic inequality are also among the most reliable predictors of regional inequality.
Recent presentations
All presentations
Predicting and monitoring local development from outer space
Predicting fifteen SDG indices for 339 Bolivian municipalities from nighttime lights and AlphaEarth daytime embeddings, and combining both satellite views to monitor local development clusters and spatial development traps.
Sustainable Development, Space Technology, and Data Science
Invited lecture (in Spanish) on how satellite imagery and spatial data science can measure, predict, and explain uneven development.
AI-Assisted Learning and Research: Production, Verification, and Tool Integration
Keynote Speaker, Annual Meeting of the Nagoya University Alumni Association, Thailand Branch
04 / SOFTWARE
Open tools for regional research
Open-source Python packages for exploring panel data, measuring regional convergence and inequality, and estimating causal effects with spillovers.
expdpy
What patterns and relationships hide in your panel data? expdpy reveals them in Python, with Plotly figures, publication tables, and no-code Streamlit apps.
Explore package
geometrics
Are poorer regions catching up, and is inequality falling? geometrics answers with spatial methods in Python, Plotly figures, and no-code Streamlit apps.
Explore package
scspill
Did a policy really work if it also affected the comparison regions? scspill, a Python package, estimates both the true effect and the spillover to them.
Explore package05 / THE OPEN CLASSROOM
Build your research toolkit
Learn to map disparities, analyze regional change, and evaluate policy with practical tutorials in Python, R, and Stata. Work through the methods, then bring them to your own research.
Evaluating the Impact of Infrastructure: A Beginner's Guide to Difference-in-Differences with the Jamuna Bridge
When a major bridge connected millions of people in northwest Bangladesh to the capital in 1998, did the region's economy take off? This beginner-friendly …
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Regional Inequality from Outer Space: Predicting GDP from Nighttime Lights and Building Inequality Indices in Python
Learn how to turn satellite images of nighttime lights into estimates of regional income, then measure how unequal regions are within each country. Step by step …
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Bouncing Back Better? Evaluating the Economic Impact of the Aceh Tsunami
How did the 2004 tsunami affect the economy of Aceh, Indonesia, over the long run? This beginner-friendly Python tutorial uses simulated data, nighttime lights, …
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