Madina (Arabic for the word 'city') is a package of classes and functions to streamline the representation and analysis of urban networks. The package includes a python implemetation of the Urban Network Analysis Toolbox (Homepage - User Guide). More detailed documentation of the package is available here.

To reference this package in your research, you can cite the paper available on SSRN:

Alhassan, Abdulaziz and Sevtsuk, Andres, Madina Python Package: Scalable Urban Network Analysis for Modeling Pedestrian and Bicycle Trips in Cities. Available at SSRN: https://ssrn.com/abstract=4748255 or http://dx.doi.org/10.2139/ssrn.4748255

@article{alhassan2024madina,
  title={Madina Python Package: Scalable Urban Network Analysis for Modeling Pedestrian and Bicycle Trips in Cities},
  author={Alhassan, Abdulaziz and Sevtsuk, Andres},
  journal={SSRN},
  year={2024},
  publisher={Elsevier}, 
  doi={10.2139/ssrn.4748255},
  url={https://ssrn.com/abstract=4748255}
}

• Organization of data layers using Geopandas
• Creation of topological (Routable) networks from a geometric representaion. Networks are represented using NetworkX
• Insertion of origin and destination nodes from data layers into topological networks
• Creating maps using DeckGL with various streamlined styling options
• Improved implementation of UNA Tools that use multiprocessing and novel path generation algorithoms to enable effecient pedestrian accessibility and flow simulations on large-scale networks.
• Added functionalities for UNA, including percieved segment costs that can account for segment quality attributes, elastic trip generation with respect to destination availability, KNN-Access metrics that allows WalkScore-type access calculations, turn penalties, etc.
• Automated workflows for pedestrian flow simulation in urban environments.
• User-friendly workspace environment that requires minimal coding experience.

The package features a streamlined way to model pedestrian activity in urban areas between pairs of pre-specified origins and destinations. This can be done by following these steps:

• Prepare input data files for the network, and each origin and destination. Place all data in a folder called Cities/city_name/Data
• Fill in the pairing table to specify origin-destination pairs, and specify specific parameters for each pair. Save the filled pairing table in the same Cities/city_name/Data folder
• run the simulation:

from madina.una.betweenness import betweenness_flow_simulation

betweenness_flow_simulation(
    city_name="new_york"
)

• Output would be saved in Cities/city_name/Simulations.
• More instructions on running a pedestrain flow simulation, preparing data and creating the pairing table are found in the documentation here

First, install geopandas through conda in a new environment

conda create -n madina_env -c conda-forge --strict-channel-priority geopandas

Activate the newly created environment

conda activate madina_env

Install Madina through pip

pip install -i https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple madina

Detailed instructions are available in the documentation here.

• Zonal class: This is the main class that the user interacts with. A user would create a Zonal object, populate it with data layers and calls functions to create a network object within a Zonal object.
• Network Class: Created inside a Zonal ovject to represent a network of origins, destinations and 'street' connections. This object is used internally as input to most network algorithms.
• UNA Module: A set of functions implementing the UNA functionalities. Each function tales a Zonal object as input.
• Workflows module: A set of standarized workdlows that takes a set of structured inpurs. Examples for Pedesstrain flow simulartiob