geo4cambodia

Subnational development data for Cambodia, ready to analyse, at every spatial scale.

geo4cambodia is the Python package of the GeoDevelopment Observatory of Cambodia. It loads the observatory's indicators (district GDP per capita, province population, night-time lights, PM2.5, Global Data Lab HDI...) as tidy panels, with boundaries for every scale. It also lets you build your own indicators: link your tables to the same unit IDs, summarise rasters over units, move data across scales and combine datasets. It works out of the box in Google Colab.

!pip install geo4cambodia -q            # in Colab; `pip install geo4cambodia` elsewhere

import geo4cambodia as geo

geo.datasets()                                       # what is available, at which scale and years
gdf = geo.load("gdppc", years=2022, geometry=True)   # 202 districts, with boundaries
gdf.plot(column="y", scheme="quantiles", k=5, legend=True)

What it does

One ID for every unit, at every scale

Every unit has a geo ID that nests by prefix (GADM 4.1 hierarchy): district KH-16-01 lies in province KH-16 (Phnom Penh), which lies in Cambodia KH. Global Data Lab regions (KH-GDL-02) are unions of whole provinces. Each dataset is a long panel keyed by geo_id and year.

Build your own indicator in five lines

import pandas as pd
import geo4cambodia as geo

mine = pd.read_csv("schools_by_province.csv")             # e.g. columns Province, year, schools
mine = geo.link(mine, scale="province", on="Province")     # "BântéayMéanchey" -> KH-01
panel = geo.combine(["env"], scale="province", extra=[mine])
panel["schools_per_10k"] = panel["schools"] / panel["total_GlobPOP_Count"] * 1e4
geo.save(panel, "schools", scale="province", directory="my_data")

From a raster:

lights = geo.zonal("viirs_2020.tif", scale="district", stats=["sum", "mean"], name="ntl", year=2020)

Then explore and model the panel with expdpy:

import expdpy as ex
df, df_def = geo.for_expdpy("hdi")
df = ex.set_labels(df, df_def, set_panel=True)
ex.analyze_panel_table(df, "HDI", ["lnNTLpc"])            # pooled, between, fixed and random effects

Or analyse it spatially with geometrics:

import geometrics as gm
gdf, df, df_dict = geo.for_geometrics("gdppc")
df = gm.set_labels(df, df_dict, set_panel=True)
gm.analyze_beta_convergence(df, "y", gdf=gdf, w=gm.make_weights(gdf)).interpret()   # levels: logged internally

Without the package

Every file is also a plain URL, for R, Stata, Excel or pandas alone:

import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/quarcs-lab/geo4cambodia/main/data/district/gdppc.csv")

Installation

pip install geo4cambodia                    # core: pandas, geopandas, pyarrow, requests, pyyaml
pip install "geo4cambodia[raster]"          # + rasterio, for zonal()
pip install git+https://github.com/quarcs-lab/geo4cambodia   # development version

Python 3.11 or later. The data are not inside the package. It reads them from the GitHub repository, so installation is quick and you always get the files the website links to. For reproducible research, pin a release of the data with geo.load(..., ref="v0.3.0") or the environment variable GEO4CAMBODIA_REF=v0.3.0.

Documentation

• How the data infrastructure works: scales, IDs, metadata, aggregation rules, and recipes for building indicators
• Dataset pages: variable dictionaries, coverage, sources and citations
• Website: indicators, notebooks, web apps and insights

Citation

Mendez, C., Khoun, T., & Poortinga, A. (2026). GeoDevelopment Observatory of Cambodia. https://quarcs-lab.github.io/geo4cambodia/

MIT License. Original data sources are credited on each dataset page.