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README.md
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```python
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import pandas as pd
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from
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```
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- **Spatial unit:** 339 Bolivian municipalities (9 departments).
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- **Time coverage:** population 2001–2020; night-time lights 2012–2020; most SDG variables 2012–2019; satellite embeddings 2017 (simple-mean) and 2017–2025 (population-weighted panel).
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- **Version:** `v20250523`.
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- **Source:** [`quarcs-lab/ds4bolivia`](https://github.com/quarcs-lab/ds4bolivia); satellite embeddings from Google (2017); SDG indices and IMDS from Andersen et al. (2020).
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---
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license: cc-by-4.0
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language:
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- en
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- es
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pretty_name: "ds4bolivia — Bolivian municipalities: SDGs, satellite embeddings & nighttime lights"
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tags:
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- bolivia
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- sdg
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- sustainable-development
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- satellite-embeddings
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- nighttime-lights
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- remote-sensing
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- geospatial
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size_categories:
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- n<1K
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---
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# Bolivian Municipalities — SDGs, Satellite Embeddings & Nighttime Lights
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Socioeconomic, satellite, and geographic measures for Bolivia's **339 municipalities**
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across its **9 departments** — the analysis data for the project *"Predicting the
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Sustainable Development of Bolivian Municipalities with Satellite Embeddings and Machine
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Learning"* (Carlos Mendez, Nagoya University).
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This dataset is a public **mirror** of the `data/` folder in
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[`cmg777/project2026e`](https://github.com/cmg777/project2026e). Every file is keyed on
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`asdf_id` (integer, `0`–`338`), the universal join key across all datasets. To attach
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municipality/department labels, merge any file to `regionNames/regionNames.csv` on `asdf_id`.
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## Contents
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| Path | Description |
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| ---- | ----------- |
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| `ds4bolivia_v20250523.csv` | Analysis-ready **merged master** (339 × 351) — all subfolders joined on `asdf_id`. |
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| `definitions_ds4bolivia_v20250523.csv` | **Data dictionary** for the master file (`varname` → `varlabel`). |
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| `regionNames/` | Administrative IDs & names — the join foundation. |
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| `sdg/` | IMDS + 14 composite SDG indices (0–100 scale). |
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| `sdgVariables/` | 64 individual SDG indicators across all 17 goals. |
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| `satelliteEmbeddings/` | 64-dim Google Satellite Embeddings — simple-mean (2017) and population-weighted (**2017–2025** panel). |
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| `nighttimeLights/` | VIIRS nighttime lights (simple-average & population-weighted, **2017–2021**), plus `rasters/` GeoTIFF. |
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| `predictions/` | Out-of-sample predictions for SDG 1, 7 and 13 (four-view: actual, lights, embeddings, combined) plus space-time forward projections. |
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| `maps/` | Municipality boundary polygons (GeoJSON), keyed by `asdf_id`. |
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| `sdg/`, `regionNames/`, … | Each subfolder ships its own `README.md` with a full variable dictionary. |
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## Provenance & coverage
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- **Spatial unit:** 339 Bolivian municipalities (9 departments); **version** `v20250523`.
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- **Source:** [`quarcs-lab/ds4bolivia`](https://github.com/quarcs-lab/ds4bolivia); the 64-dim
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satellite embeddings are Google Satellite Embeddings (Google Earth Engine); SDG indices and
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the IMDS index follow Andersen et al. (2020).
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- **Time coverage:** population 2001–2020; nighttime lights 2012–2021; most SDG variables 2012–2019;
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satellite embeddings 2017 (simple-mean) and 2017–2025 (population-weighted panel).
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## Load from Python
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Load any file straight from the Hub (no full clone needed):
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```python
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import pandas as pd
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from huggingface_hub import hf_hub_download
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path = hf_hub_download(
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repo_id="cmg777/project2026e",
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repo_type="dataset",
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filename="satelliteEmbeddings/bolivia_pop_weighted_2017.csv",
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)
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df = pd.read_csv(path)
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```
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The companion repo ships a `code/hf_data.py` helper with a `data_path()` function that
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prefers a local copy and otherwise streams the file from this dataset.
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