geo4bolivia

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

geo4bolivia is the Python package of the GeoDevelopment Observatory of Bolivia. It loads the observatory's indicators for Bolivia's 339 municipalities (population, night-time lights, 64 indicators of the Municipal Atlas of the SDGs, satellite embeddings...) 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: every example below runs in a new Colab notebook (replace the placeholder files, such as your own table or raster, with yours).

!pip install -q geo4bolivia mapclassify             # mapclassify: for scheme="quantiles"
import geo4bolivia as geo

geo.datasets()                                       # what is available, at which scale and years
gdf = geo.load("sdg", variables="sdg1_2_mpi", years=2012, geometry=True)   # 339 municipalities
gdf.plot(column="sdg1_2_mpi", 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): municipality BO-04-17-04 (La Paz) lies in province BO-04-17 (Murillo), which lies in department BO-04 (La Paz), which lies in Bolivia BO. INE codes and DS4Bolivia's asdf_id are kept as columns of geo.units(). Each dataset is a long panel keyed by geo_id and year.

Build your own indicator in five lines

!pip install -q geo4bolivia
import pandas as pd
import geo4bolivia as geo

mine = pd.read_csv("schools_by_municipality.csv")                 # e.g. columns mun_id (INE code), year, schools
mine = geo.link(mine, scale="municipality", on="mun_id", key="mun_id")   # 20101 -> BO-04-17-04
panel = geo.combine(["pop"], scale="municipality", extra=[mine])
panel["schools_per_10k"] = panel["schools"] / panel["pop"] * 1e4
geo.save(panel, "schools", scale="municipality", directory="my_data")

From a raster:

!pip install -q "geo4bolivia[raster]"             # [raster] adds rasterio
import geo4bolivia as geo

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

Then explore and model the panel with expdpy:

!pip install -q geo4bolivia expdpy
import numpy as np
import geo4bolivia as geo
import expdpy as ex

panel = geo.combine(["ntl", "pop"], scale="municipality", years=(2012, 2020))
panel["ln_pop"] = np.log(panel["pop"])
df, df_def = geo.for_expdpy(panel, labels={"ln_pop": "Log of population"})
df = ex.set_labels(df, df_def, set_panel=True)
ex.analyze_panel_table(df, "ln_NTLpc", ["ln_pop"])        # pooled, between, fixed and random effects

Or analyse it spatially with geometrics:

!pip install -q geo4bolivia geometrics
import numpy as np
import geo4bolivia as geo
import geometrics as gm

ntl = geo.load("ntl")
ntl["NTLpc"] = np.exp(ntl["ln_NTLpc"])
gdf, df, df_dict = geo.for_geometrics(ntl, labels={"NTLpc": "Night-time lights per capita"})
df = gm.set_labels(df, df_dict, set_panel=True)
gm.analyze_beta_convergence(df, "NTLpc", gdf=gdf, w=gm.make_weights(gdf)).interpret()   # levels: logged internally

Without the package

Every file is also a plain URL, readable with pandas alone (preinstalled in Colab, so nothing to install):

import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/quarcs-lab/geo4bolivia/main/data/municipality/ntl.csv")

Installation

In a Google Colab cell:

!pip install -q geo4bolivia                     # core: pandas, numpy, geopandas, pyarrow, requests, pyyaml (Colab has them)
!pip install -q "geo4bolivia[raster]"           # + rasterio, for zonal()
!pip install -q git+https://github.com/quarcs-lab/geo4bolivia   # 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="v0.1.0") or the environment variable GEO4BOLIVIA_REF=v0.1.0.

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. (2026). GeoDevelopment Observatory of Bolivia. https://quarcs-lab.github.io/geo4bolivia/

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