The geography of development

Development.
Seen from outer space.

Our mission is to promote interdisciplinary science for economic, social, and environmental sustainability. We integrate insights from spatial data science, machine learning, and development economics to understand and inform the process of sustainable development.

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Carlos MendezAssociate Professor · Nagoya University, Japan

Earth at night, showing the distribution of city lights across Asia and surrounding regions.

Explore the questions

01Map disparities02Explain regional change03Inform 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.

Through the QuaRCS lab—Quantitative Regional and Computational Science—I work with an international research network on sustainable regional development, from growth and inequality to structural change and energy.

Five connected research areas

Sustainable regional development
The geography of development
Regional growth and inequality
Spatial structural change
Spatial energy economics

Explore the QuaRCS lab

NIGHTTIME LIGHTS

Map human activity

Explore the geography of nighttime lights and discover how economic activity is distributed across places.

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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 trends

POPULATION & PLACE

Put places in context

Explore population patterns alongside the geography of light to frame new questions about people and development.

Explore population

NAGOYA, JAPAN / 35° N

02 / BEHIND THE RESEARCH

Economics meets
spatial data science.

I’m Carlos Mendez, Associate Professor of Development Economics at Nagoya University, Japan. I integrate development economics, spatial data science, and applied econometrics to understand and inform sustainable development across regions.

My current research

Geospatial inequality, poverty, and growth interactions
Geospatial big data analytics and impact evaluation
Regional infrastructure and spatial structural change

Download CV Academic profile

03 / RESEARCH IN FOCUS

Recent research

All publications

2026 Economic Modelling

Okun's law and spatial regimes in Indonesia: A machine learning approach

Okun's law varies markedly across Indonesian districts, and growth shocks spill over to neighboring regions — calling for locally tailored, coordinated labor policies.

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2026 Asia-Pacific Journal of Regional Science

Minimum wage differentials and commuting across districts

Minimum wage differentials across districts significantly affect worker commuting probabilities in Indonesia's Greater Jakarta Metropolitan Area.

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2025 Social Indicators Research

Mapping the dimensions of poverty through big data, socioeconomic surveys and machine learning in Cambodia

We use new big data sources, the Cambodia Socio-Economic Survey, and machine learning methods to predict and map multidimensional poverty in Cambodia.

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Recent presentations

All presentations

Date TBA

Predicting and monitoring local development from outer space

2026.11.01

Sustainable Development, Space Technology, and Data Science

2026.10.30

AI-Assisted Learning and Research: Production, Verification, and Tool Integration

04 / TOOLS FOR DISCOVERY

Tools for your next research question.

Explore regional data, compare places, and turn economic questions into reproducible analysis with open research software.

All projects

Project 2026

scspill

A Python package of synthetic control models that drop SUTVA on the donor pool — the treatment is allowed to reach the controls, and every model reports two …

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Project 2026

geometrics

A Python library to explore, analyze, and learn regional growth, convergence, and inequality — with explicit spatial methods, interactive Plotly figures and …

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Project 2026

expdpy

A Python library to explore, analyze, and learn panel data interactively — composable Plotly figures and publication-quality tables, plus three no-code …

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05 / 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.

All tutorials

TUTORIAL 2026

Introduction to Difference-in-Differences (DiD) in Python

Learn Difference-in-Differences (DiD) in Python using PyFixest and Great Tables. Covers the 2x2 design, TWFE regression, inference comparison, …

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TUTORIAL 2023

Spatial Panel Regression in Stata: Cigarette Demand Across US States

Model spatial spillovers in panel data using the Spatial Durbin Model (SDM), Wald specification tests, and dynamic extensions with the xsmle package in Stata

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TUTORIAL 2023

Cross-Sectional Spatial Regression in Stata: Crime in Columbus Neighborhoods

Explore the full taxonomy of cross-sectional spatial models --- OLS, SAR, SEM, SLX, SDM, SDEM, SAC, and GNS --- using the Columbus crime dataset in Stata, …

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QUARCS LAB / NAGOYA