Thursday, February 17, 2022

New Paper: Synthetic Populations with Social Networks

When developing geographically explicit agent-based models, one thing we spend a lot of time on is building synthetic populations and then linking the agents in the synthetic population to each other.  To overcome this issue we have a new paper published in "Computational Urban Science " entitled "A method to create a synthetic population with social networks for geographically-explicit agent-based models" In this paper  Na (Richard) Jiang, Hamdi Kavak, Annetta Burger, William Kennedy and myself present a synthetic population generation method that also includes social networks and use the New York Metro as a study site, which covers an area of 262 x 234 km and is home to over 23 million people. 

To show the utility of this method we also present three simple applications (e.g., a disease , a disaster  and a traffic model) which utilize different parts of this synthetic population but are all geographically explicit and use networks in some shape or form. If this sounds of interest, below you can read the abstract from the paper, along with seeing some of the figures from our methodology and example applications. While at the bottom of the post we provide the full citation and a link to the paper. The paper itself also has links to actual code that generates the synthetic population and the resulting datasets and models  (code:; source and resulting synthetic population data: .  


Geographically-explicit simulations have become crucial in understanding cities and are playing an important role in urban science. One such approach is that of agent-based modeling which allows us to explore how agents interact with the environment and each other (e.g., social networks), and how through such interactions aggregate patterns emerge (e.g., disease outbreaks, traffic jams). While the use of agent-based modeling has grown, one challenge remains, that of creating realistic, geographically-explicit, synthetic populations which incorporate social networks. To address this challenge, this paper presents a novel method to create a synthetic population which incorporates social networks using the New York Metro Area as a test area. To demonstrate the generalizability of our synthetic population method and data to initialize models, three different types of agent-based models are introduced to explore a variety of urban problems: traffic, disaster response, and the spread of disease. These use cases not only demonstrate how our geographically-explicit synthetic population can be easily utilized for initializing agent populations which can explore a variety of urban problems, but also show how social networks can be integrated into such populations and large-scale simulations.

Keywords: Synthetic Population Generation, Agent-Based Modeling, New York, Traffic Dynamics, Disease, Disaster

Study Area

Workflow for Generation of Synthetic Population and Networks

Creation of Social Networks: (a) Selected Population; (b) Creation of a Household Network; (c) Creation of Work and Educational Networks for each Member of the Household; (d) The Household, its Networks within the Full Census Tract

Model Component Structure of Population Respond to Disaster event

Agents’ Health Status After 1 Minute of the Disaster Event

Full Reference:

Jiang, N., Crooks, A.T., Kavak, H., Burger, A. and Kennedy, W.G. (2022), A Method to Create a Synthetic Population with Social Networks for Geographically Explicit Agent-Based Models, Computational Urban Science, 2:7. Available at