Data | Carlos Mendez

Indonesia514

Wed, 10 Jun 2026 00:00:00 +0000

Indonesia514: A Data Science Repository to Study Regional Development across 514 Districts in Indonesia

Welcome to Indonesia514! This project centralizes geospatial data and regional-economic indicators — including GDP, investment, and government spending — for the 514 districts of Indonesia. All datasets share a single common identifier, districtID, so they can be merged into a unified analytical table.

This repository is organized for researchers and data scientists interested in:

Regional Economics: Measuring district-level growth, capital formation, and fiscal policy.
Spatial Analysis: Linking economic indicators to district geometries for mapping and spatial econometrics.
Reproducible Workflows: Streaming open datasets directly into Python via a single join key.

⚙️ Active development. This repository is an early-stage draft. The district boundaries are complete in GeoJSON format and the bilingual website (English / Bahasa Indonesia) is live, but the economic datasets currently ship sample data for 16 districts — the full 514-district series are pending. Interactive dashboards and analytical notebooks are planned and not yet published.

💾 Datasets

Curated datasets organized into modules, all linked by a unique identifier (districtID).

Dataset	File Path	Description	Join Key
GDP	`/gdp/gdp.csv`	District-level gross domestic product (2010–2022).	`districtID`
GFCF	`/gfcf/gfcf.csv`	Gross Fixed Capital Formation, a measure of investment (2010–2022).	`districtID`
Government Spending	`/gs/gs.csv`	Government spending and fiscal-policy distribution (2010–2022).	`districtID`
Spatial Vector	`/maps/mapIdonesia514tp.geojson`	Geometric boundaries (polygons) for all 514 districts.	`districtID`

⚠️ Important Note on Identifiers: The primary key for joining all datasets in this repository is districtID. The GDP, GFCF, and government-spending files currently contain a 16-district sample; the full 514-district data are being added. Always treat districtID consistently (as an int or string) across both dataframes before merging.

🐍 Quick start

Stream the datasets directly from GitHub and merge them on districtID with pandas. You can run the code below in a blank Google Colab scratchpad — no setup or installation required.

import pandas as pd
# -----------------------------------------------------------------------------
# 1. SETUP: Define the raw GitHub URL to stream data directly into Pandas.
# -----------------------------------------------------------------------------
REPO_URL = "https://raw.githubusercontent.com/quarcs-lab/indonesia514/main"
# -----------------------------------------------------------------------------
# 2. LOAD: Read the economic CSVs.
# -----------------------------------------------------------------------------
df_gdp = pd.read_csv(f"{REPO_URL}/gdp/gdp.csv")
df_gfcf = pd.read_csv(f"{REPO_URL}/gfcf/gfcf.csv")
df_gs = pd.read_csv(f"{REPO_URL}/gs/gs.csv")
# -----------------------------------------------------------------------------
# 3. MERGE: Combine the indicators on the common district identifier.
# -----------------------------------------------------------------------------
df = pd.merge(df_gdp, df_gfcf[['districtID', 'gfcf_2022']], on='districtID')
df = pd.merge(df, df_gs[['districtID', 'gs_2022']], on='districtID')

📜 Citation

If you use this repository in your research, please cite it using the following metadata.

APA Format

Mendez, C., Abdulah, R., Arvianto, B., & Leiva, F. (2026). Indonesia514: A data science repository to study regional development in Indonesia. GitHub. https://github.com/quarcs-lab/indonesia514

BibTeX Format

@misc{indonesia5142026,
author = {Mendez, Carlos and Abdulah, Rusli and Arvianto, Bimo and Leiva, Favio},
title = {{Indonesia514}: A Data Science Repository to Study Regional Development in Indonesia},
year = {2026},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/quarcs-lab/indonesia514}}
}

License

This repository is released under the MIT License, permitting broad reuse with proper attribution.

🤝 Contributing

We welcome contributions! If you are adding full-district data, fixing a Coordinate Reference System (CRS) issue, building a new notebook, or integrating fresh indicators, please submit a Pull Request.

DS4Bolivia

Wed, 14 Jan 2026 00:00:00 +0000

DS4Bolivia: A Data Science Repository to Study GeoSpatial Development in Bolivia

Welcome to DS4Bolivia! This project aggregates spatial and socio-economic datasets, interactive dashboards, and computational workflows focused on 339 municipalities of Bolivia. It is designed to bridge the gap between spatial analysis and sustainable development goals (SDGs).

This repository is organized for researchers and data scientists interested in:

Spatial Econometrics: Understanding regional disparities, growth, and clustering.
Spatial Machine Learning: Utilizing satellite imagery (Earth Observation) for predictive modeling.
Sustainable Development: Tracking SDG indicators at a granular local level.

🖥️ Interactive Geospatial Dashboards

Explore the data without writing code. These applications visualize the space-time dynamics of key development indicators.

Space-time dynamics of population, luminosity, land cover and GDP (2013-2019): Visualize the evolution of population density, night-time lights, land cover changes, and GDP estimates across Bolivian municipalities in 2013 and 2019.

🐍 Cloud-based Computational Notebooks

Step-by-step tutorials to help you reproduce our analysis. These notebooks utilize Python libraries such as GeoPandas and PySAL.

Introduction to Exploratory Spatial Data Analysis (ESDA)
Focus: Learn how to detect spatial clusters and outliers using Global and Local Moran’s I.
Key Concepts: Spatial Autocorrelation, LISA Statistics, Choropleth Mapping.

💾 Spatially-Explicit Datasets

Curated datasets ready for analysis. These files are pre-processed to align with Bolivian municipal boundaries.

SDGs & Satellite Embeddings (2017)
Description: A merged dataset combining socio-economic indicators (SDGs) with high-dimensional feature vectors extracted from satellite imagery.
Use Case:　Training machine learning models to predict poverty or development indices based on visual patterns from space.

📜 Citation

If you use this repository in your research, please cite it using the following metadata.

APA Format

Mendez, C., Gonzales, E., Leoni, P., Andersen, L., Peralta, H. (2026). DS4Bolivia: A Data Science Repository to Study GeoSpatial Development in Bolivia [Data set]. GitHub. https://github.com/quarcs-lab/ds4bolivia

BibTeX Format

@misc{ds4bolivia2026,
author = {Mendez, Carlos and Gonzales, Erick and Leoni, Pedro and Andersen, Lykke and Peralta, Hendrix},
title = {{DS4Bolivia}: A Data Science Repository to Study GeoSpatial Development in Bolivia},
year = {2026},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/quarcs-lab/ds4bolivia}}
}

🚀 Construct your own dataset

The datasets are organized into modules, all linked by a unique identifier (asdf_id).

Dataset Category	File Path	Description	Join Key
Region Names	`/regionNames/regionNames.csv`	Administrative metadata (Municipality names, Department names).	`asdf_id`
Socio-Economic	`/sdg/sdg.csv`	Sustainable Development Goal (SDG) indices and poverty metrics.	`asdf_id`
Satellite Features	`/satelliteEmbeddings/satelliteEmbeddings2017.csv`	Feature vectors (embeddings) extracted from daytime satellite imagery.	`asdf_id`
Spatial Vector	`/maps/bolivia339geoqueryOpt.geojson`	Geometric boundaries (Polygons) for all municipalities.	`asdf_id`

⚠️ Important Note on Identifiers: > The primary key for joining all datasets in this repository is asdf_id.
While mun_id (standard government code) is present in the administrative data, asdf_id ensures consistency across the satellite embeddings and optimized map files provided here. Always ensure this column is treated as an int or string consistently across both dataframes before merging.

You can run the examples below immediately in Google Colab.

Example 1: Integrating Attribute Data

This script demonstrates how to merge the administrative names, socio-economic indicators, and satellite machine learning features into a single analytical dataframe.

import pandas as pd
# -----------------------------------------------------------------------------
# 1. SETUP: Define Source URLs
# We use the raw GitHub URL to stream data directly into Colab/Pandas.
# -----------------------------------------------------------------------------
REPO_URL = "https://raw.githubusercontent.com/quarcs-lab/ds4bolivia/master"
url_names = f"{REPO_URL}/regionNames/regionNames.csv"
url_sdg = f"{REPO_URL}/sdg/sdg.csv"
url_emb = f"{REPO_URL}/satelliteEmbeddings/satelliteEmbeddings2017.csv"
# -----------------------------------------------------------------------------
# 2. LOAD: Read CSVs
# -----------------------------------------------------------------------------
print("Loading datasets...")
df_names = pd.read_csv(url_names)
df_sdg = pd.read_csv(url_sdg)
df_embeddings = pd.read_csv(url_emb)
# -----------------------------------------------------------------------------
# 3. MERGE: Combine Dataframes
# -----------------------------------------------------------------------------
# Step A: Attach SDG data to Names
df_merged_step1 = pd.merge(df_names, df_sdg, on='asdf_id', how='inner')
# Step B: Attach Satellite Embeddings to the result
df_final = pd.merge(df_merged_step1, df_embeddings, on='asdf_id', how='inner')
# -----------------------------------------------------------------------------
# 4. VERIFY
# -----------------------------------------------------------------------------
print(f"Merge Complete.")
print(f"Original Municipalities: {len(df_names)}")
print(f"Final Merged Rows: {len(df_final)}")
print(f"Total Columns: {len(df_final.columns)}")
# Display the first few rows (names + first few embedding columns)
display(df_final[['mun', 'dep', 'index_sdg1', 'A00', 'A01', 'A02']].head())

Example 2: Integrating Spatial and Attribute Data

This script takes the merged data from Example 1 and attaches it to the municipality geometries (GeoJSON) for spatial analysis and plotting.


import geopandas as gpd
import matplotlib.pyplot as plt
# -----------------------------------------------------------------------------
# 1. LOAD SPATIAL DATA
# We load the optimized GeoJSON file containing municipality boundaries.
# -----------------------------------------------------------------------------
geojson_url = f"{REPO_URL}/maps/bolivia339geoqueryOpt.geojson"
print("Loading GeoJSON map...")
gdf_boundaries = gpd.read_file(geojson_url)
# -----------------------------------------------------------------------------
# 2. SPATIAL DATA PREPARATION
# GeoJSON often loads IDs as objects/strings, while CSVs load as integers.
# -----------------------------------------------------------------------------
# Force 'asdf_id' to integer to match the pandas dataframe
gdf_boundaries['asdf_id'] = gdf_boundaries['asdf_id'].astype(int)
# -----------------------------------------------------------------------------
# 3. ATTRIBUTE JOIN
# Merge the spatial dataframe (gdf) with the attribute dataframe (df_final).
# This creates a 'GeoDataFrame' capable of spatial operations.
# -----------------------------------------------------------------------------
gdf_bolivia = gdf_boundaries.merge(df_final, on='asdf_id', how='inner')
# -----------------------------------------------------------------------------
# 4. VISUALIZATION (Choropleth Map)
# Plot the "No Poverty" SDG Index (SDG 1)
# -----------------------------------------------------------------------------
fig, ax = plt.subplots(1, 1, figsize=(12, 10))
gdf_bolivia.plot(
column='index_sdg1', # Variable to map
cmap='viridis', # Color palette (perceptually uniform)
linewidth=0.1, # Border width
edgecolor='white', # Border color
legend=True,
legend_kwds={'label': "SDG 1 Index (No Poverty)", 'orientation': "horizontal"},
ax=ax
)
ax.set_title("Bolivia: SDG 1 Index by Municipality", fontsize=15)
ax.set_axis_off() # Turn off lat/lon axis numbers for cleaner look
plt.show()

Data sources

SDG indicators are originally contructed by Andersen, L. E., Canelas, S., Gonzales, A., Peñaranda, L. (2020) Atlas municipal de los Objetivos de Desarrollo Sostenible en Bolivia 2020. La Paz: Universidad Privada Boliviana, SDSN Bolivia

🤝 Contributing

We welcome contributions! If you are fixing a Coordinate Reference System (CRS) issue, adding a new spatial model, or uploading fresh data, please submit a Pull Request.

GeoDevelopment Observatory of Cambodia

Mon, 15 Sep 2025 00:00:00 +0000

How to cite this project:

Mendez C., Khoun T., Poortinga A. (2025) GeoDevelopment Observatory of Cambodia.https://bit.ly/gdo-cambodia

The GeoDevelopment Observatory (GDO) of Cambodia provides a public access platform for the analysis, monitoring, and evaluation of sustainable regional development. The observatory integrates environmental, social, and economic indicators—designated as GeoDevelopment Indicators—collected from satellite imagery, ground-based surveys, and administrative records. These multi-dimensional datasets are analyzed using AI-enhanced computational notebooks and specialized web applications within the GeoDevelopment Tools framework. The technical analyses are then converted into GeoDevelopment Insights, which present the data in accessible formats for different user groups. Researchers utilize the platform for conducting empirical analyses, decision-makers access it for policy development and implementation, and citizens engage with it to understand development patterns in their regions. This systematic approach facilitates the transformation of complex sustainability data into usable information for monitoring progress, evaluating interventions, and informing sustainable regional development policies.

Slides by Carlos Mendez

Source: https://bit.ly/gdo-cambodia

Contribute and provide feedback at https://github.com/gdo-cambodia