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
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.
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.
01 / THE RESEARCH QUESTIONS
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.
NIGHTTIME LIGHTS
Explore the geography of nighttime lights and discover how economic activity is distributed across places.
Explore the mapCHANGE OVER TIME
Compare luminosity across regions and over time. Start with a pattern, then ask what might explain it.
Explore the trendsPOPULATION & PLACE
Explore population patterns alongside the geography of light to frame new questions about people and development.
Explore population02 / BEHIND THE RESEARCH
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.
QUARCS LAB / NAGOYA
Our lab brings researchers and students together to study regional development through economics, spatial data, and computational methods.
Cesar Echevarria (Peru)PhD student 2024-2027
LIFE AT THE QUARCS LAB
A glimpse of life at the QuaRCS lab through the years.
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03 / RESEARCH IN FOCUS
Okun's law varies markedly across Indonesian districts, and growth shocks spill over to neighboring regions — calling for locally tailored, coordinated labor policies.
Minimum wage differentials across districts significantly affect worker commuting probabilities in Indonesia's Greater Jakarta Metropolitan Area.
We use new big data sources, the Cambodia Socio-Economic Survey, and machine learning methods to predict and map multidimensional poverty in Cambodia.

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.

Invited lecture (in Spanish) on how satellite imagery and spatial data science can measure, predict, and explain uneven development.

Keynote Speaker, Annual Meeting of the Nagoya University Alumni Association, Thailand Branch
04 / TOOLS FOR DISCOVERY
Explore regional data, compare places, and turn economic questions into reproducible analysis with open research software.
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 projectAre poorer regions catching up, and is inequality falling? geometrics answers with spatial methods in Python, Plotly figures, and no-code Streamlit apps.
Explore projectDid 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 project05 / THE OPEN CLASSROOM
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.
Learn Difference-in-Differences (DiD) in Python using PyFixest and Great Tables. Covers the 2x2 design, TWFE regression, inference comparison, …
Start learningModel spatial spillovers in panel data using the Spatial Durbin Model (SDM), Wald specification tests, and dynamic extensions with the xsmle package in Stata
Start learningExplore the full taxonomy of cross-sectional spatial models --- OLS, SAR, SEM, SLX, SDM, SDEM, SAC, and GNS --- using the Columbus crime dataset in Stata, …
Start learning