Call for Papers: ACM SIGSPATIAL GeoSim 2026

The 9th ACM SIGSPATIAL International Workshop on Geospatial Simulation (GeoSim 2026) focuses on all aspects of simulation as a general paradigm to model and predict spatial systems and generate spatial data. New simulation methodologies and frameworks, not necessarily coming from the SIGSPATIAL community, are encouraged to participate. Also, this workshop is of interest to everyone who works with spatial data. The simulation methods that will be presented and discussed in the workshop should find a wide application across the community by producing benchmark datasets that can be parameterized and scaled. Simulated data sets will be made available to the community via the website.

The workshop seeks high-quality full (up to 10 pages) and short (up to 4 pages) papers that will be peer-reviewed. Following the main conference, full papers are limited to 10 pages (excluding references), with up to 2 additional pages after the references to be used for appendices. Once accepted, at least one author is required to register for the workshop and the ACM SIGSPATIAL conference, as well as attend the workshop to present the accepted work which will then appear in the ACM Digital Library.

GeoSim 2026 solicits novel and previously unpublished research on all topics related to geospatial simulation including, but not limited to:

• Applications for Spatial Simulation
• Agent Based Models for Spatial Simulation
• Multi-Agent Based Spatial Simulation
• Big Spatial Data Simulation
• Disease Spread Simulation
• Spatial Data/Trajectory Generators
• Road Traffic Simulation
• Human Mobility Simulation
• Environmental Simulation
• GIS using Spatial Simulation
• Interactive Spatial Simulation
• Spatial Simulation Parallelization and Distribution
• Geo-Social Simulation and Data Generators
• Social Unrest and Riot Prediction using Simulation
• Spatial Analysis based on Simulation
• Behavioral Simulation
• Verifying and Validating Spatial Simulations
• Urban Simulation
• Digital Twin Simulation
• Synthetic Population Simulation

Important Date

Submission deadline

August 15, 2026

Notification

September 15, 2026

Workshop date

November 3, 2026

Submission site

https://easychair.org/conferences/?conf=geosim2026

Workshop website

https://geosim.org/

Paper Format

Manuscripts should be submitted in PDF format and formatted using the ACM camera-ready templates available at https://www.acm.org/publications/proceedings-template. For latex users, please make sure that you choose sigconf type (e.g., \documentclass[sigconf]{acmart}) in your latex file. Submissions to GeoSim are single-blind – i.e., the names and affiliations of the authors should be listed in the submitted version.

Contact

Please contact geosim2026@easychair.org if you have questions about GeoSim 2026.

Call for Papers: ACM SIGSPATIAL GeoSim 2025

The 8th ACM SIGSPATIAL International Workshop on Geospatial Simulation (GeoSim 2025) focuses on all aspects of simulation as a general paradigm to model and predict spatial systems and generate spatial data. New simulation methodologies and frameworks, not necessarily coming from the SIGSPATIAL community, are encouraged to participate. Also, this workshop is of interest to everyone who works with spatial data. The simulation methods that will be presented and discussed in the workshop should find a wide application across the community by producing benchmark datasets that can be parameterized and scaled. Simulated data sets will be made available to the community via the website.

The workshop seeks high-quality full (8-10 pages) and short (up to 4 pages) papers that will be peer-reviewed. Once accepted, at least one author is required to register for the workshop and the ACM SIGSPATIAL conference, as well as attend the workshop to present the accepted work which will then appear in the ACM Digital Library.

GeoSim 2025 solicits novel and previously unpublished research on all topics related to geospatial simulation including, but not limited to:

• Applications for Spatial Simulation
• Agent Based Models for Spatial Simulation
• Multi-Agent Based Spatial Simulation
• Big Spatial Data Simulation
• Disease Spread Simulation
• Spatial Data/Trajectory Generators
• Road Traffic Simulation
• Environmental Simulation
• GIS using Spatial Simulation
• Interactive Spatial Simulation
• Spatial Simulation Parallelization and Distribution
• Geo-Social Simulation and Data Generators
• Social Unrest and Riot Prediction using Simulation
• Spatial Analysis based on Simulation
• Behavioral Simulation
• Verifying and Validating Spatial Simulations
• Urban Simulation
• Digital Twin Simulation
• Synthetic Population Simulation

Important Date

Submission deadline

August 15, 2025

Notification

September 15, 2025

Workshop date

November 3, 2025

Submission site

https://easychair.org/conferences/?conf=geosim2025

Workshop website

https://geosim.org/

Paper Format

Manuscripts should be submitted in PDF format and formatted using the ACM camera-ready templates available at https://www.acm.org/publications/proceedings-template. For latex users, please make sure that you choose sigconf type (e.g., \documentclass[sigconf]{acmart}) in your latex file. Submissions to GeoSim are single-blind – i.e., the names and affiliations of the authors should be listed in the submitted version.

Contact

Please contact me or geosim2025@easychair.org if you have questions about GeoSim 2025.

Data-Driven Mobility Models for COVID-19 Simulation

John Pesavento, Andy Chen, Rayan Yu, Joon-Seok Kim, Hamdi Kavak, Taylor Anderson, Andreas Züfle

Abstract

Agent-based models (ABM) play a prominent role in guiding critical decision-making and supporting the development of effective policies for better urban resilience and response to the COVID-19 pandemic. However, many ABMs lack realistic representations of human mobility, a key process that leads to physical interaction and subsequent spread of disease. Therefore, we propose the application of Latent Dirichlet Allocation (LDA), a topic modeling technique, to foot-traffic data to develop a realistic model of human mobility in an ABM that simulates the spread of COVID-19. In our novel approach, LDA treats POIs as “words” and agent home census block groups (CBGs) as “documents” to extract “topics” of POIs that frequently appear together in CBG visits. These topics allow us to simulate agent mobility based on the LDA topic distribution of their home CBG. We compare the LDA based mobility model with competitor approaches including a naive mobility model that assumes visits to POIs are random. We find that the naive mobility model is unable to facilitate the spread of COVID-19 at all. Using the LDA informed mobility model, we simulate the spread of COVID-19 and test the effect of changes to the number of topics, various parameters, and public health interventions. By examining the simulated number of cases over time, we find that the number of topics does indeed impact disease spread dynamics, but only in terms of the outbreak's timing. Further analysis of simulation results is needed to better understand the impact of topics on simulated COVID-19 spread. This study contributes to strengthening human mobility representations in ABMs of disease spread.

Three promising high school students, John Pesavento, Andy Chen, and Rayan Yu, presented our recent research "Data-Driven Mobility Models for COVID-19 Simulation" at the 3rd ACM SIGSPATIAL Workshop on Advances in Resilient and Intelligent Cities (ARIC 2020)! I am a witness to their hard work during Aspiring Scientists Summer Internship Program (ASSIP). These dilligent students were mentored by Dr. Andreas Züfle and Dr. Hamdi Kavak and co-mentored by Dr. Anderson Taylor and myself. The presentation at ARIC 2020 was very clear. Well done!

You can check out interactive heatmaps and more information in John Peasavento's GitHub Page. This research is supported by National Science Foundation and by the Aspiring Scientists Summer Internship Program (ASSIP) at George Mason University.

J. Pesavento, A. Chen, R. Yu, J.-S. Kim, H. Kavak, T. Anderson, and A. Züfle, “Data Driven Mobility Models for COVID-19 Simulation,” In Proceedings of the 3rd ACM SIGSPATIAL Workshop on Advances in Resilient and Intelligent Cities (ARIC 2020), November 2020, pp. 29-38 

Location-Based Social Network Data Generation Based on Patterns of Life

Joon-Seok Kim, Hyunjee Jin, Hamdi Kavak, Ovi Chris Rouly, Andrew Crooks, Dieter Pfoser, Carola Wenk, Andreas Züfle

Abstract

Location-based social networks (LBSNs) have been studied extensively in recent years. However, utilizing real-world LBSN data sets yields several weaknesses: sparse and small data sets, privacy concerns, and a lack of authoritative ground-truth. To overcome these weaknesses, we leverage a large-scale LBSN simulation to create a framework to simulate human behavior and to create synthetic but realistic LBSN data based on human patterns of life. Such data not only captures the location of users over time but also their interactions via social networks. Patterns of life are simulated by giving agents (i.e., people) an array of "needs" that they aim to satisfy, e.g., agents go home when they are tired, to restaurants when they are hungry, to work to cover their financial needs, and to recreational sites to meet friends and satisfy their social needs. While existing real-world LBSN data sets are trivially small, the proposed framework provides a source for massive LBSN benchmark data that closely mimics the real-world. As such, it allows us to capture 100% of the (simulated) population without any data uncertainty, privacy-related concerns, or incompleteness. It allows researchers to see the (simulated) world through the lens of an omniscient entity having perfect data. Our framework is made available to the community. In addition, we provide a series of simulated benchmark LBSN data sets using different synthetic towns and real-world urban environments obtained from OpenStreetMap. The simulation software and data sets, which comprise gigabytes of spatio-temporal and temporal social network data, are made available to the research community.

Please check out the video that I presented at the 21st IEEE International Conference on Mobile Data Management (MDM 2020)!

Source code: https://github.com/gmuggs/pol
LSBN Data: https://osf.io/e24th/
Additional materials: https://mdm2020.joonseok.org


J.-S. Kim, H. Jin, H. Kavak, O. Rouly, A. Crooks, D. Pfoser, C. Wenk, and A. Züfle, “Location-Based Social Network Data Generation Based on Patterns of Life,” In Proceedings of IEEE International Conference on Mobile Data Management (MDM 2020), July 2020, pp. 158-167