tobler
Tobler is a Python library for areal interpolation.
Decision gist · record as of 2026-08-14
Yes. Tobler is actively maintained, has no known vulnerabilities, installs with low friction, and solves a well-defined geospatial problem with multiple methods suited to different data contexts. It is appropriate for anyone working with spatial data across incompatible boundaries or scales. The BSD 3-Clause license is permissive. The only constraint is the Python 3.12+ requirement.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.12 or later.
- Spatial data files (shapefiles or GeoJSON) are needed as input.
- Low friction installation with a pure-Python wheel.
License · maintenance · safety
BSD 3-Clause (permissive) — BSD 3-Clause is permissive and poses no significant restrictions on use, modification, or distribution in most contexts.
last release 2026-04-10 (126 days) · last repo commit 2026-07-30 · 169 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 139,614 downloads/mo, #11,295 on PyPI
Alternatives
Verify before relying
pip install tobler
import tobler
from geopandas import read_file
source = read_file('source_polygons.shp')
target = read_file('target_polygons.shp')
result = tobler.area_weighted(source, target, 'variable_column')- Specific performance characteristics or scalability limits for large datasets are not documented in the fact sheet.
- Whether model-based interpolation methods support custom regression models or only built-in implementations.
- Availability and completeness of API documentation beyond the GitHub repository.
What it is and what it does
Tobler is a geospatial Python library for transferring data from one set of polygonal boundaries to another, solving the common problem of incompatible spatial representations. It implements three families of interpolation methods: area-weighted (simplest, using only geometry overlap), dasymetric (incorporating auxiliary raster or vector data to constrain allocation), and model-based (using statistical relationships with covariates). The package is part of PySAL, the broader spatial data science ecosystem, and leverages shapely and multicore architecture for performance.
Common use cases include harmonizing census data across decennial boundary changes, converting data collected at different administrative scales (e.g., zip codes to census tracts), and aggregating or disaggregating variables to match analysis grids. Each method trades simplicity for accuracy: area-weighted requires only geometry but is susceptible to the modifiable areal unit problem; dasymetric improves estimates by masking inappropriate areas; model-based approaches offer the richest incorporation of auxiliary information but require careful specification and validation.
Use it for
- Standardize historical census data from different time periods to a single boundary representation to overcome decennial redistricting.
- Convert neighborhood-level survey data into a regular grid for spatial analysis or raster-based modeling.
- Allocate zip-code-level demographic or economic variables to census tract boundaries for cross-scale analysis.
- Use satellite imagery or land-use rasters to constrain interpolation of population data to inhabited areas only.
- Estimate small-area statistics by fitting regression models that relate target variables to physical or demographic covariates.
- Aggregate fine-resolution raster predictions back to administrative boundaries for policy reporting.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Tobler is actively maintained, has no known vulnerabilities, installs with low friction, and solves a well-defined geospatial problem with multiple methods suited to different data contexts. It is appropriate for anyone working with spatial data across incompatible boundaries or scales. The BSD 3-Clause license is permissive. The only constraint is the Python 3.12+ requirement.
Install
tobler on PyPI
Before you install
Low friction installation with a pure-Python wheel. The package depends on ten established geospatial and scientific libraries (geopandas, rasterio, scipy, statsmodels, etc.), all widely available. Maintenance is active with recent commits and no archived status.
Requires Python 3.12 or later. Spatial data files (shapefiles or GeoJSON) are needed as input.
License in practice
BSD 3-Clause is permissive and poses no significant restrictions on use, modification, or distribution in most contexts.
Quickstart
pip install tobler
import tobler
from geopandas import read_file
source = read_file('source_polygons.shp')
target = read_file('target_polygons.shp')
result = tobler.area_weighted(source, target, 'variable_column')Verify before relying
- Specific performance characteristics or scalability limits for large datasets are not documented in the fact sheet.
- Whether model-based interpolation methods support custom regression models or only built-in implementations.
- Availability and completeness of API documentation beyond the GitHub repository.
Package facts
| License | BSD 3-Clause permissive |
| Python support | Supports the current Python release >=3.12 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 10 packagesgeopandasjobliblibpysalnumpypandasrasteriorasterstatsscipystatsmodelstqdm |
| Maintenance | Actively maintained 126 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 139,614 / month, #11,295 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Topic :: Scientific/Engineering :: GIS |
Evidence: tobler-0.14.0-py3-none-any.whl
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