PySGN: A Python package for constructing synthetic geospatial networks

In previous posts, we have written about the generation of synthetic populations based on real world locations, and how such populations can have various types of networks associated with them. We have also written about network generation techniques in the past and keeping with this line of research, Boyu Wang, Taylor Anderson, Andreas Züfle and myself have a new paper in the Journal of Open Source Software entitled "PySGN: A Python Package for Constructing Synthetic Geospatial Networks"

In this paper we introduce a Python package that can generate geospatial networks which we have called PySGN (Python for Synthetic Geospatial Networks). For readers not familiar with geospatial networks, to quote from the online documentation we have put together:  

Geospatial networks are a type of network where nodes are associated with specific geographic locations. These networks are used to model and analyze spatial relationships and interactions, such as transportation systems, communication networks, and social interactions within geographic constraints. By incorporating spatial data, geospatial networks provide insights into how location influences connectivity and network dynamics.

PySGN generates synthetic geosocial networks that mimic the spatial relationships observed in real‑world networks as it embeds nodes in geographic coordinate space, modifies connection rules to decay with distance, and allows users to incorporate clustering and preferential attachment while respecting spatial constraints. Online we provide examples of creating Geospatial Erdős-Rényi, Watts-Strogatz & Barabási-Albert Networks along with ways to sample points based on a specified bounding box or specific polygon boundaries (examples of which are shown below). 

The package is intended for researchers and practitioners in fields such as urban planning, epidemiology, infrastructure resilience and social science who require robust tools for simulating and analyzing complex geospatial networks. In addition to the paper, we have also made available extensive documentation (along with examples of the various network types) at https://pysgn.readthedocs.io/en/ 

Examples of Geospatial Erdős-Rényi and Watts-Strogatz Networks.

Example of Geospatial Barabási-Albert Network based on different ordering strategies for how nodes are added to the network.

Examples of sampling points based on a bounding box or a specific set of polygons

Full Referece: 

Wang, B., Crooks, A.T., Anderson, T., and Züfle, A. (2026), PySGN: A Python Package for Constructing Synthetic Geospatial Networks. Journal of Open Source Software, 11(119), 9346, https://doi.org/10.21105/joss.09346 (pdf)

A hybrid simulation methodology for identifying and mitigating supply chain disruptions

Durring times of crisis, shocks to supply chains can propagate through the entire economy (e.g., global shortages of critical goods, such as personal protective equipment during COVID-19). At the same time, criminal organizations may disrupt and manipulate licit supply chains for financial gain or political objectives.  Thus there is a strong need for modeling and simulating not only supply chain operations but also malicious actors who may act to disrupt them. 

To explore this we (Abhisekh Rana, Raj Patel, Aman Goswami, Sean Luke, Alok Baveja, Carlotta Domeniconi, Benjamin Melamed, Fred Roberts, Weiwei Chen, Vladimir Menkov, Viswanath Narayan, James Jones, Hamdi Kavak and myself) have a new paper entitled  "Hybrid simulation methodology for identifying and mitigating supply chain disruptions" which was recently published in the Journal of Simulation. 

In the paper we introduce a novel hybrid modeling framework (implemented in MASON) designed to identify vulnerabilities across supply networks. Through the framework, we are able to analyze disruption scenarios  and evaluate mitigation strategies using a pharmaceutical supply chain model (i.e., PharmaSim). As such this paper and proposed framework provides a foundation for simulation-driven planning tools that help organizations anticipate risks and strengthen supply chain resilience.

If this sounds of interest, below we provide the abstract to the paper, some of the figures which show the supply chain we model and the simulation framework along with some results. While at the bottom of the page, you can find the full referece to the paper and a link to it, while the model itself is available at https://github.com/eclab/DES-Supply-Chain-demo. 

Abstract

Global disruptions have shown that shocks to supply chains can quickly ripple through entire economies, highlighting the need to identify vulnerabilities and evaluate mitigation strategies to build resilience. In this paper, we propose a simulation methodology, Hybrid Integrated Supply-Chain Simulation (HISS), to identify and mitigate potential disruptions in supply chains. We demonstrate HISS using a generic pharmaceutical supply chain model including sourcing, outsourcing, production, packaging, and distribution processes, created using MASON’s hybrid modeling capabilities. We classify disruptions from malicious actors and analyze their timing, impact, and scope. The simulation is further extended to modeling mitigation strategies and assessing their efficacy. Extensive optimization allowed us to identify worst-case disruptions and optimized safety stock strategies reduced impacts by a factor of five, while anomaly detection achieved a high recall of 0.966. The modeling approach proposed in this paper provides a basis for planning tools that support resilience and preparedness of supply chains.

Keywords: Hybrid simulation, supply chains modeling, resilience, optimization, evolutionary computation. 

Visual representation of pharmaceutical supply chain (PSC), which was used to code PharmaSim

Time series of daily production flow through the active pharmaceutical ingredient (API) Production node (resilience triangles are shown in red and the number of units on the vertical axis is in millions).

Overview of the software components and their interactions.

Sample time series of numbers of packaged units with anomalies due to (left) a disruption and due to (right) normal fluctuations (the number of units on the vertical axis is in millions).

Full reference:

Rana, A., Patel, R., Goswami, A., Luke, S., Baveja, A., Domeniconi, C., Melamed, B., Roberts, F., Chen, W., Crooks, A.T., Menkov, V., Narayan, V., Jones, J. and Kavak, H. (2026). A hybrid simulation methodology for identifying and mitigating supply chain disruptions. Journal of Simulation, 1–22. https://doi.org/10.1080/17477778.2026.2628944 (pdf)