Friday, September 29, 2023

Call for Abstracts: Geosimulations for Addressing Societal Challenges

As part of the The 10th Anniversary Symposium on Human Dynamics Research which will take place at the 2024 American Association of Geographers (AAG) Annual Meeting in Honolulu, Hawaii  between Tuesday, April 16 – Saturday, April 20, 2024 we are organizing a session(s) on Geosimulations for Addressing Societal Challenges. If the session description is of interest, please feel free to submit an abstract (details are below).

Session Description:

There is an urgent need for research that promotes sustainability in an era of societal challenges ranging from climate change, population growth, aging and wellbeing to that of pandemics. These need to be directly fed into policy. We, as a Geosimulation community, have the skills and knowledge to use the latest theory, models and evidence to make a positive and disruptive impact. These include agent-based modeling, microsimulation and increasingly, machine learning methods. However, there are several key questions that we need to address which we seek to cover in this session. For example, What do we need to be able to contribute to policy in a more direct and timely manner? What new or existing research approaches are needed? How can we make sure they are robust enough to be used in decision making? How can geosimulation be used to link across citizens, policy and practice and respond to these societal challenges? What are the cross-scale local trade-offs that will have to be negotiated as we re-configure and transform our urban and rural environments? How can spatial data (and analysis) be used to support the co-production of truly sustainable solutions, achieve social buy-in and social acceptance? And thereby co-produce solutions with citizens and policy makers.

We are particularly interested in presentations that will discuss issues relating to:

  • Agent-based modeling and microsimulation techniques for responding to societal challenges; Agent-based models used for policy formation;
  • Data driven modeling;
  • Utilizing machine modeling for geosimulation;
  • Creating really big models using exascale computation;
  •  Model validation and assessment; 
  • Participatory methods for agent-based modeling;
  • Approaches to connect and share (open source) data and models;
  • Revealing, quantifying, and reducing socio-economic inequalities with Geosimulation.

Next Steps:

If this sounds of interest, please e-mail the abstract and key words with your expression of intent to Richard Jiang ( by November 9th (one week before the AAG session deadline). Please make sure that your abstract conforms to the AAG guidelines in relation to title, word limit and key words and as specified at:

An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and conclusions. 


  • 9th November, 2023: Abstract submission deadline. E-mail Richard Jiang by this date if you are interested in being in this session. Please submit an abstract and key words with your expression of intent.
  • 14th November, 2023: Session finalization and author notification
  • 15th November, 2023: Final abstract submission to AAG, via All participants must register individually via this site. Upon registration you will be given a participant number (PIN). Send the PIN and a copy of your final abstract to Richard Jiang. Neither the organizers nor the AAG will edit the abstracts. 
  • 16th November, 2023: AAG registration deadline. Sessions submitted to AAG for approval.
  • 16th -20th April 2024: AAG in Honolulu.


Thursday, September 07, 2023

Agent-Based Modeling of Consumer Choice

At the upcoming International Conference on Geographic Information Science (GIScience 2023) Boyu Wang and myself have a new paper entitled "Agent-Based Modeling of Consumer Choice by Utilizing Crowdsourced Data and Deep Learning." In the paper we explore how through mining Yelp reviews can inform an agents choices of restaurants. The model itself was created in Mesa and uses Mesa-Geo and  more details about the model can be found at  If this sounds of interest, below you can see the abstract to the paper, some fugues including the graphical user interface of the model and a link to the paper.

Abstract: People’s opinions are one of the defining factors that turn spaces into meaningful places. Online platforms such as Yelp allow users to publish their reviews on businesses. To understand reviewers' opinion formation processes and the emergent patterns of published opinions, we utilize natural language processing (NLP) techniques especially that of aspect-based sentiment analysis methods (a deep learning approach) on a geographically explicit Yelp dataset to extract and categorize reviewers' opinion aspects on places within urban areas. Such data is then used as a basis to inform an agent-based model, where consumers' (i.e., agents') choices are based on their characteristics and preferences. The results show the emergent patterns of reviewers' opinions and the influence of these opinions on others. As such this work demonstrates how using deep learning techniques on geospatial data can help advance our understanding of place and cities more generally.

Keywords: aspect-category sentiment analysis, consumer choice, agent-based modeling, online restaurant reviews.

An overview of proposed agent-based model logic.

Average star rating vs. average sentiment by aspect category for 200 randomly selected restaurants in the City of St. Louis, MO.

The prototype agent-based model (a) with simulated (b) and actual visiting patterns (c).

Full reference:

Wang, B. and Crooks, A.T. (2023), Agent-Based Modeling of Consumer Choice by Utilizing Crowdsourced Data and Deep Learning, in Beecham, R., Long, J.A., Smith, D., Zhao, Q., and Wise, S (eds), Proceedings of the 12th International Conference on Geographic Information Science (GIScience 2023), Dagstuhl Publishing, Dagstuhl, Germany., pp. 81:1-81:6. (pdf)