UrbanSim
UrbanSim is a platform for building statistical models of cities and regions. These models help forecast long-range patterns in real estate development, demographics, and related outcomes, under various policy scenarios.
This urbansim Python library is a core component. It contains tools for statistical estimation and simulation; domain-specific logic about housing markets, household relocation, and other processes; and frameworks and utilities for assembling a model.
Project scope
Status: Active
Mission: The UrbanSim Python library provides methods and reusable model components for building self-managed simulations of urban development, household and employment location, real estate markets, and related regional change.
Architecture: UrbanSim is a portable, self-managed Python library designed primarily for conventional CPU-based execution. It provides reusable model components and interfaces that can also be implemented by other execution engines.
The project maintains and develops:
statistical model components used in urban simulation;
location-choice, relocation, transition, and development models;
real-estate feasibility and related urban-development methods;
estimation and simulation utilities;
model APIs and configuration patterns; and
reusable components for assembling regional UrbanSim implementations.
UrbanSim is designed to work with other UDST libraries and with external data, estimation, accessibility, and workflow systems through documented Python interfaces and model specifications.
Development of urban-simulation methods and reusable model components is welcome within this mission and architecture. Material changes to the project’s mission or execution architecture are considered through UDST’s organization-level governance process.
See the UDST Project Directory and Open-source projects and commercial offerings for organization-wide project status and policy.
How it works
Operational UrbanSim models begin with detailed data about a particular region, and then estimate and validate a system of interconnected model components. Full models draw on a number of libraries: not just urbansim, but also Orca for task orchestration, Synthpop for population synthesis, Pandana for network analysis, and so on. Collectively, these make up the Urban Data Science Toolkit (UDST).
UrbanSim models are used by public agencies, consultancies, and researchers in dozens of cities around the U.S. and world. The core platform is open source, but many operational models make use of additional cloud-hosted model building and visualization tools provided by UrbanSim Inc.
Learn More
Installation
UrbanSim requires Python 3.10 or later.
pip install urbansim
conda install urbansim --channel conda-forge
The network accessibility utilities also require Pandana, which is optional: pip install "urbansim[network]".
Technical documentation
Other UDST libraries
Documentation for UrbanCanvas, the UrbanSim cloud platform
Release files for urbansim 3.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| urbansim-3.3.tar.gz | 179.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| urbansim-3.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 253.4 kB
Release files / urbansim-3.3.tar.gz
| Download URL | urbansim-3.3.tar.gz |
|---|---|
| Size | 179.1 kB |
| Tags | Source |
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Transparency logRelease files / urbansim-3.3-py3-none-any.whl
| Download URL | urbansim-3.3-py3-none-any.whl |
|---|---|
| Size | 74.4 kB |
| Tags | Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 16, 2026.
Transparency log