GEO / Dataset

GEO Dataset

A harmonized, geocoded
school-level database

The GEO Dataset provides open, standardized, school-level administrative census data spanning geocoordinates, personnel, infrastructure resources, and educational outcomes across low- and middle-income countries — built for GeoAI, spatial data science, and development research.

—
countries available
4
data dimensions
80k+
schools
V1
current version
Available countries
Loading coverage layers...

Four joinable dimensions

Every country is structured as four flat CSV files sharing a common oedc_id key. All tables have identical column structure across countries.

📍
Geo
{ISO3}_geo.csv

School identity, geocoordinates with provenance tier, administrative hierarchy (adm0–adm3), GHSL urban/rural classification, and operational status. One row per school — no year column.

👩‍🏫
Personnel
{ISO3}_personnel.csv

Enrollment (total and sex-disaggregated), teaching staff counts, qualified teachers, classrooms, and pupil-teacher ratio. Aligned to UIS headcount definitions. One row per school × year.

🏗️
Resources
{ISO3}_resources.csv

WASH access (JMP basic tier), electricity, internet connectivity and type, computer lab, library, and permanent building status. All binary. One row per school × year.

📊
Outcomes
{ISO3}_outcomes.csv

Promotion, repetition, and dropout rates (UIS reconstructed cohort method), completion rate, and gross intake ratio where available. Expressed as proportions. One row per school × year.

Access the data

All data is released under CC BY 4.0. Please cite the accompanying data descriptor when using the GEO Dataset in published work.

Schema documentation: Full variable definitions, NA rules, and standards anchors for all four dimensions are available in the dataset schema. Per-country harmonization notes documenting source provenance, variable mappings, and known quality issues are in /metadata/{ISO3}_metadata.md.
Country ISO3 Schools Dimensions Year(s) Metadata Download
Loading available countries...

Full dataset bundle and harmonization notes also available on Harvard Dataverse upon V1 publication.

How to cite