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 https://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 aZonal
object as input.Workflows
module: A set of standarized workdlows that takes a set of structured inpurs. Examples for Pedesstrain flow simulartiob