New lightweight version of UrbanSim, a tool for modeling metropolitan real estate markets
Docmentation will be kept on the github wiki: https://github.com/synthicity/urbansim/wiki
This new code base is an experimental branch of the longstanding UrbanSim project [1] aimed at reducing the learning curve of using the UrbanSim methodology. Redesigned from the ground up, it is a tool to make creating that first urban model as trivial as any other Python package available today.
This UrbanSim package uses no code from the svn repository currently hosted on urbansim.org, though it makes every effort to implement the exact same methodology. We lean heavily on the PyData [2] community to make our work easier - Pandas, HDF5 file storage, and statsmodels are ubiquitous in this work. These Python libraries essentially replace the UrbanSim Dataset class, tools to read and write from other storage, and some of the statistical estimation currently present in UrbanSim.
This makes our task easier as we can focus on urban modeling and leave the infrastructure to the wider Python community. The Pandas [3] library is the vernacular of the new UrbanSim, which is an extremely well documented library that has thousands of questions asked and answered on the internets as well as a printed book [4]. As such, we won't discuss Pandas much here - it is assumed you can find that information elsewhere.
Currently, this framework implements a set of JSON requests to describe urban models. JSON [5] is a very basic lanaguage designed to communicate requests from client to server and back on the internets. Although we don't currently implement a web service for estimating models, this is a clear pathway for the future. Put simply, JSON files are the API by which a user can configure an urban model (though we certainly don't forbid getting your hands dirty and writing a model in Python).
Currently supported models are enough to create a basic 30 year simulation that is the prototypical use case for UrbanSim. These models are 1) hedonic price models 2) MNL and nested location choice models 3) transition models for increasing population, jobs, and development and 4) relocation rate models to specify the movement of population and jobs.
On the other hand, if you're a person who just wants to create the easiest and most straightforward location choice model around and map it, you've also come to the right place.
[4] http://www.amazon.com/Python-Data-Analysis-Wes-McKinney/dp/1449319793
[5] http://en.wikipedia.org/wiki/JSON
Download and install Anaconda Python distribution:
http://www.continuum.io/downloads
Get repository:
>> git clone https://github.com/fscottfoti/urbansim.git
Set environment variables:
>> cd urbansim
>> export PYTHONPATH=$PWD
>> export DATA_HOME=$PWD
!!! Contact me to get sample dataset - example won't run without it. !!!
Run:
>> cd example
>> ./run.sh