Metadata-Version: 2.4
Name: geoquetzal
Version: 1.0.0
Summary: Geographic and census data for Guatemala — the tigris equivalent for Guatemalan researchers
Author: Jorge Yass
License-Expression: MIT
Project-URL: Homepage, https://github.com/geoquetzal/geoquetzal
Project-URL: Repository, https://github.com/geoquetzal/geoquetzal
Project-URL: Issues, https://github.com/geoquetzal/geoquetzal/issues
Project-URL: Census Data, https://github.com/geoquetzal/censo2018/releases
Keywords: guatemala,gis,geography,census,geospatial,departamentos,municipios,INE,GADM,mapping,migration,central-america,choropleth,parquet
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: GIS
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: geopandas>=0.12
Requires-Dist: pandas>=1.5
Requires-Dist: requests>=2.28
Requires-Dist: pyarrow>=10.0
Provides-Extra: plotting
Requires-Dist: folium>=0.14; extra == "plotting"
Requires-Dist: matplotlib>=3.6; extra == "plotting"
Requires-Dist: mapclassify>=2.5; extra == "plotting"
Provides-Extra: dev
Requires-Dist: pytest>=7.0; extra == "dev"
Requires-Dist: black>=23.0; extra == "dev"
Requires-Dist: ruff>=0.1; extra == "dev"
Provides-Extra: all
Requires-Dist: geoquetzal[plotting]; extra == "all"
Requires-Dist: contextily>=1.3; extra == "all"
Dynamic: license-file

# 🦜 GeoQuetzal

🌐 **English** | [Leer en español](README.es.md)

**Geographic and census data for Guatemala — the first library of its kind for Central America.**

GeoQuetzal gives Guatemalan researchers programmatic access to administrative boundaries and census microdata, following the same philosophy as [`tigris`](https://github.com/walkerke/tigris)/[`tidycensus`](https://walker-data.com/tidycensus/) for the US and [`geobr`](https://github.com/ipeaGIT/geobr) for Brazil.

```python
import geoquetzal as gq

deptos = gq.departamentos()
deptos.plot(edgecolor="white", figsize=(8, 8))
```

## Why GeoQuetzal?

Working with Guatemalan geographic and census data typically means downloading shapefiles from GADM, cleaning up inconsistent name spellings, downloading census CSVs from INE, figuring out how to join them — and dealing with the fact that GADM spells "Quetzaltenango" as "Quezaltenango" and concatenates "San Marcos" into "SanMarcos".

GeoQuetzal handles all of that. One function call gives you clean, analysis-ready data with consistent INE names and numeric codes that join reliably.

## Installation

```bash
pip install geoquetzal

# With plotting support (matplotlib + folium)
pip install geoquetzal[plotting]

# Everything (adds contextily for basemaps)
pip install geoquetzal[all]
```

**Requirements:** Python 3.9+, geopandas, pandas, requests, pyarrow.

## Datasets

| Dataset | Records | Variables | Download size | Source |
|---|---|---|---|---|
| Boundaries | 22 deptos / 340 municipios | geometry + codes | ~2 MB | GADM v4.1 |
| Emigración | 242,203 | 11 | ~1.6 MB | INE Censo 2018 |
| Hogares | 3,275,931 | 37 | ~38 MB | INE Censo 2018 |
| Vivienda | ~3,300,000 | 11 | ~30 MB | INE Censo 2018 |
| Personas | 14,901,286 | 84 | ~333 MB | INE Censo 2018 |

All census datasets are hosted as Parquet files on GitHub Releases and downloaded on-demand per departamento. After the first download, data loads instantly from a local cache.

## Quick Start

### Administrative Boundaries

```python
import geoquetzal as gq

# Country outline
gq.country()

# All 22 departamentos
deptos = gq.departamentos()

# By name or code (accent-insensitive)
gq.departamentos("Sacatepequez")     # accent-insensitive ✓
gq.departamentos("Sacatepéquez")     # exact spelling ✓
gq.departamentos(3)                  # INE code ✓

# By region
gq.departamentos(region="V - Central")

# Municipios (~340)
gq.municipios("Sacatepequez")                # all municipios in a departamento
gq.municipios(name="Antigua Guatemala")      # single municipio by name
gq.municipios(name=301)                      # single municipio by code

# Guatemala City zone-level polygons
gq.municipios("Guatemala", zonas=True)       # 22 rows, one per zona
```

### Census Microdata

All four census datasets follow the same pattern — filter by departamento or municipio, optionally attach geometry:

```python
from geoquetzal.emigracion import emigracion
from geoquetzal.hogares import hogares
from geoquetzal.vivienda import vivienda
from geoquetzal.personas import personas

# Load all records
df = emigracion()                                   # 242K emigrant records
df = hogares()                                      # 3.2M households
df = vivienda()                                     # 3.3M housing units
df = personas()                                     # 14.9M people

# Filter by departamento (only downloads that departamento's file)
df = hogares(departamento="Huehuetenango")
df = hogares(departamento=13)

# Filter by municipio
df = hogares(municipio="Antigua Guatemala")
df = hogares(municipio=301)

# Attach geometry for mapping
gdf = hogares(departamento="Petén", geometry="municipio")
```

### Explore Variables

Every dataset module includes a `describe()` function:

```python
from geoquetzal.hogares import describe

describe()            # summary table of all 37 variables
describe("PCH4")      # water source — values and labels
describe("PCH15")     # receives remittances
```

### Variable Highlights

**Emigración**: sex (`PEI3`), age at departure (`PEI4`), year left (`PEI5`)

**Hogares**: water source (`PCH4`), sanitation (`PCH5`), electricity (`PCH8`), appliances — radio, TV, fridge, internet, car (`PCH9_A`–`PCH9_M`), cooking fuel (`PCH14`), remittances (`PCH15`)

**Vivienda**: housing type (`PCV1`), wall material (`PCV2`), roof (`PCV3`), floor (`PCV5`)

**Personas**: sex (`PCP6`), age (`PCP7`), ethnicity (`PCP12` — Maya/Garífuna/Xinka/Ladino), Mayan linguistic community (`PCP13`), mother tongue (`PCP15`), disability (`PCP16_A`–`PCP16_F`), education (`PCP17_A`), literacy (`PCP22`), tech access — cellphone/computer/internet (`PCP26_A`–`PCP26_C`), employment (`PCP27`), marital status (`PCP34`), fertility (`PCP35`–`PCP39`)

## Mapping Patterns

### Static Choropleth (matplotlib)

```python
import geoquetzal as gq
from geoquetzal.hogares import hogares

df = hogares(departamento="Sacatepequez")
pct_internet = (
    df.groupby("MUNICIPIO")["PCH9_I"]
    .apply(lambda x: (x == 1).mean() * 100)
    .round(1)
    .reset_index(name="pct")
)

munis = gq.municipios("Sacatepequez")
result = munis.merge(pct_internet, left_on="codigo_muni", right_on="MUNICIPIO")
result.plot(column="pct", cmap="YlGnBu", legend=True, edgecolor="white")
```

### Interactive Map (folium)

```python
result.explore(
    column="pct",
    tooltip=["municipio", "pct"],
    tiles="CartoDB positron",
)
```

### Animated Choropleth (Plotly)

```python
import plotly.express as px
import json

deptos = gq.departamentos()
geojson = json.loads(deptos.to_json())
for f in geojson["features"]:
    f["id"] = f["properties"]["codigo_depto"]

fig = px.choropleth(
    agg_df,                         # your aggregated data
    geojson=geojson,
    locations="codigo_depto",
    color="value",
    animation_frame="year",
)
fig.update_geos(fitbounds="locations", visible=False)
fig.show()
```

> **Key rule:** Always aggregate first with pandas, then merge geometry onto the 22 or 340 summary rows. Never use `geometry=` on large microdata — it attaches a polygon to every row and is very slow.

## Coordinate Reference Systems

```python
from geoquetzal.crs import to_gtm, to_utm16n, suggest_crs

deptos = gq.departamentos()
suggest_crs(deptos)              # prints recommendations

deptos_gtm = to_gtm(deptos)     # Guatemala Transverse Mercator (national standard)
deptos_utm = to_utm16n(deptos)   # UTM Zone 16N (good for area/distance)
```

| CRS | EPSG | Use case |
|---|---|---|
| WGS 84 | 4326 | Default from GADM, web maps |
| Guatemala TM (GTM) | ESRI:103598 | National standard, official maps |
| UTM Zone 16N | 32616 | Area and distance calculations |

## How Data Works

**Boundaries** are downloaded from GADM v4.1 on first call and cached locally. GeoQuetzal automatically resolves the many spelling differences between GADM and INE names using numeric codes.

**Census microdata** is partitioned by departamento into Parquet files and hosted on [GitHub Releases](https://github.com/geoquetzal/censo2018/releases). When you request a single departamento, only that file is downloaded (~1–15 MB). Requesting all of Guatemala downloads all 22 files. Everything is cached after the first download.

**Joins** between census data and boundaries always use INE numeric codes (`codigo_depto`, `codigo_muni`), never names — because GADM and INE spell names differently.

## GADM Matching & Diagnostics

GADM v4.1 has 354 polygons for Guatemala (vs INE's 340 municipios). The extras include lake polygons (Lago de Atitlán, Lago de Amatitlán) and Guatemala City split into 22 zone polygons. GeoQuetzal handles all of this automatically.

To check the current matching status:

```python
from geoquetzal.geography import diagnose_matching
results = diagnose_matching()
```

A few newer municipios (Sipacate, Raxruhá, Petatán, etc.) don't exist in GADM v4.1 and won't have boundary polygons until GADM updates.

## Data Sources & Attribution

- **Administrative boundaries**: [GADM v4.1](https://gadm.org) — freely available for academic and non-commercial use
- **Census microdata**: [INE Guatemala](https://censo2018.ine.gob.gt/descarga) — XII Censo Nacional de Población y VII de Vivienda 2018 (public data)
- **Hosted Parquet files**: [github.com/geoquetzal/censo2018](https://github.com/geoquetzal/censo2018/releases)

## Contributing

GeoQuetzal is open source under the MIT license. Contributions are welcome — especially around new datasets, documentation, and example notebooks.

```bash
git clone https://github.com/geoquetzal/geoquetzal.git
cd geoquetzal
pip install -e ".[dev,plotting]"
```

## Author

Created by **Jorge Yass** — online lecturer at Universidad del Valle de Guatemala (UVG) and PhD student at Iowa State University.

Inspired by mentoring a Data Science for Public Good team and the realization that Guatemala (and Central America) had no equivalent to `tigris`, `tidycensus`, or `geobr`.

## License

MIT. Census data is public information from INE Guatemala. GADM boundary data is subject to [GADM's license](https://gadm.org/license.html) (free for academic/non-commercial use).
