Open Source

The ranking engine and the calibrated datasets that power GeoRank are published as free, zero-dependency libraries. Inspect the method, cite the data, or build your own tools on the same foundation — on npm, PyPI, and GitHub.

MIT CC-BY-4.0 4 packages · 12 distributions

Why we open-source

Transparent enough to build on

GeoRank ranks places on the factors that matter for relocation — sunshine, cost, climate, tax. Two pieces of that stack are general-purpose enough to stand alone, so we publish them openly:

the ranking algorithm — how raw factors become a single comparable score; and
the calibrated climate datasets — the per-city sunshine, temperature, and rainfall values behind the sunshine, temperature, and rainfall maps.

Each ships for both JavaScript and Python with real tests and no runtime dependencies. The full data pipeline is documented on the methodology page.

Packages

Where to find them

Package	What it is	npm	PyPI	Source
georank	Weighted-criteria ranking engine	npm	PyPI	GitHub
sunshine-hours-by-city	270-city calibrated sunshine dataset	npm	PyPI	GitHub
world-temperature-data	270-city monthly + annual temperature	npm	PyPI	GitHub
world-rainfall-data	270-city annual precipitation	npm	PyPI	GitHub

Package · Algorithm

georank

A generic weighted-criteria ranking engine. Each factor is min-max normalized to 0–100 across your set (missing values fall back to a neutral 50), weighted, and summed into a single 0–100 score — the same method behind the GeoRank rankings and compare tools. Set a factor to invert when lower is better, like cost or tax.

npm install georank
pip install georank

LanguagesJavaScript · Python

LicenseMIT · zero dependencies

npm · PyPI · GitHub

Package · Dataset

sunshine-hours-by-city

Calibrated mean annual sunshine hours for 270 cities, derived from EUMETSAT CM SAF SARAH-3 satellite radiation and ECMWF ERA5, bias-corrected against 56 WMO reference stations (actual ≈ 1.14 × ERA5 − 1,550, R² = 0.82). It is the per-city distribution of the data behind the sunshine map; full provenance is on the methodology page.

npm install sunshine-hours-by-city
pip install sunshine-hours-by-city

Coverage270 cities · CSV bundled

LicenseCC-BY-4.0 (data) · MIT (code)

npm · PyPI · GitHub

Package · Dataset

world-temperature-data

Mean monthly and annual air temperature for 270 cities — twelve monthly normals plus an annual mean, warmest and coldest month — derived from CHELSA v2.1 climatological normals (1981–2010). It is the per-city distribution of the data behind the temperature map; full provenance on the methodology page.

npm install world-temperature-data
pip install world-temperature-data

Coverage270 cities · 12 months · CSV bundled

LicenseCC-BY-4.0 (data) · MIT (code)

npm · PyPI · GitHub

Package · Dataset

world-rainfall-data

Mean annual precipitation (mm) for 270 cities, from CHELSA v2.1 climatological normals (1981–2010) bias-corrected against ~25,000 GHCN-M v4 stations. It is the per-city distribution of the data behind the rainfall map and climate layer.

npm install world-rainfall-data
pip install world-rainfall-data

Coverage270 cities · CSV bundled

LicenseCC-BY-4.0 (data) · MIT (code)

npm · PyPI · GitHub

License & citation

Using this work

Package code is licensed MIT. The sunshine dataset is licensed CC-BY-4.0 — free to use, share, and adapt, including commercially, with attribution. See the recommended citation and the per-package ATTRIBUTION.md for upstream source credits (EUMETSAT, Copernicus/ERA5, CHELSA, GeoNames, WMO).