Boyu Wang
ABSTRACT
Mesa is an open-source agent-based modeling (ABM) framework implemented in the Python programming language, allowing users to build and visualize agent-based models. It has been used in a diverse range of application areas over the years ranging from biology to workforce dynamics. However, there has been no direct support for integrating geographical data from geographical information systems (GIS) into models created with Mesa. Users have had to rely on their own implementations to meet such needs. In this paper we present Mesa-Geo, a GIS extension for Mesa, which allows users to import, manipulate, visualise and export geographical data for ABM. We introduce the main components and functionalities of Mesa-Geo, followed by example applications utilizing geographical data which demonstrates Mesa-Geo's core functionalities and features common to agent-based models. Finally, we conclude with a discussion and outlook on future directions for Mesa-Geo.
- Sameera Abar, Georgios K. Theodoropoulos, Pierre Lemarinier, and Gregory M. P. O'Hare. 2017. Agent Based Modelling and Simulation Tools: a Review of the State-of-Art Software. Computer Science Review 24 (May 2017), 13--33. Google Scholar
Cross Ref
- Taylor Anderson and Suzana Dragicevic. 2018. Geographic Network Automata for Representing Complex Evolving Spatial Systems. In GeoSim '18: Proceedings of the 1st ACM SIGSPATIAL International Workshop on GeoSpatial Simulation. Seattle, WA, 5--8.Google Scholar
- Alessia Antelmi, Gennaro Cordasco, Matteo D'Auria, Daniele De Vinco, Alberto Negro, and Carmine Spagnuolo. 2019. On Evaluating Rust as a Programming Language for the Future of Massive Agent-Based Simulations. In Methods and Applications for Modeling and Simulation of Complex Systems (Communications in Computer and Information Science), Gary Tan, Axel Lehmann, Yong Meng Teo, and Wentong Cai (Eds.). Springer, Singapore, 15--28. Google ScholarCross Ref
- Robert Axelrod. 1997. The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press. Google ScholarCross Ref
- C Michael Barton, Isaac Ullah, Gary Mayer, Sean Bergin, Hessam Sarjoughian, and Helena Mitasova. 2017. MedLanD Modeling Laboratory (Version 1.1.0). CoMSES Computational Model Library. https://www.comses.net/codebases/4609/releases/1.1.0/Google Scholar
- Itzhak Benenson and Paul M. Torrens. 2004. Geosimulation: Automata-Based Modelling of Urban Phenomena. John Wiley & Sons, London, UK.Google Scholar
- Jon Louis Bentley. 1975. Multidimensional Binary Search Trees Used for Associative Searching. Commun. ACM 18, 9 (Sept. 1975), 509--517. Google ScholarDigital Library
- Andrei Borshchev. 2014. Multi-Method Modelling: AnyLogic. In Discrete-Event Simulation and System Dynamics for Management Decision Making. John Wiley & Sons, Ltd, 248--279. Google ScholarCross Ref
- Paul Box. 2002. Spatial Units as Agents: Making the Landscape an Equal Player in Agent-Based Simulations. In Integrating Geographic Information Systems and Agent-Based Modeling Techniques for Understanding Social and Ecological Processes, H Randy Gimblett (Ed.). Oxford University Press, 0. Google ScholarCross Ref
- Paul W. Box. 2001. Kenge GIS - CA class template for Swarm. Natural Resources and Environmental Issues 8 (2001), 6. https://digitalcommons.usu.edu/nrei/vol8/iss1/6Google Scholar
- Daniel G. Brown, Rick Riolo, Derek T. Robinson, Michael North, and William Rand. 2005. Spatial Process and Data Models: Toward Integration of Agent-Based Models and GIS. Journal of Geographical Systems 7, 1 (May 2005), 25--47. Google ScholarCross Ref
- H. Butler, M. Daly, A. Doyle, Sean Gillies, T. Schaub, and Stefan Hagen. 2016. The GeoJSON Format. Technical Report RFC 7946. Internet Engineering Task Force. Google Scholar
- Howard Butler, Sean Gillies, et al. 2019--. Rtree: Spatial Indexing for Python. https://github.com/Toblerity/rtreeGoogle Scholar
- Stephen Cass. 2022. Top Programming Languages 2022: Python's Still No. 1, but Employers Love to See SQL Skills. IEEE Spectrum (2022). https://spectrum.ieee.org/top-programming-languages-2022Google Scholar
- Moongi Choi and Alexander Hohl. 2021. Investigating Spatiotemporal Indoor Contact Patterns Using ABM and STKDE. In GeoSim '21: Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation. Beijing, China, 1--8.Google Scholar
- Mark Coletti. 2012. The GeoMason Cookbook. (2012). https://cs.gmu.edu/~eclab/projects/mason/extensions/geomason/geomason.pdfGoogle Scholar
- Nick Collier. 2003. RePast: An Extensible Framework for Agent Simulation. http://www2.econ.iastate.edu/tesfatsi/RePastTutorial.Collier.pdfGoogle Scholar
- Nicholson Collier and Michael North. 2013. Parallel Agent-Based Simulation With Repast for High Performance Computing. SIMULATION 89, 10 (Oct. 2013), 1215--1235. Google ScholarCross Ref
- Nicholson Collier and Michael J North. 2004. Repast for Python Scripting. In Proceedings of the Agent 2004 Conference on Social Dynamics: Interaction, Reflexivity and Emergence. 231--237.Google Scholar
- Nicholson T. Collier, Jonathan Ozik, and Eric R. Tatara. 2020. Experiences in Developing a Distributed Agent-Based Modeling Toolkit with Python. In 2020 IEEE/ACM 9th Workshop on Python for High-Performance and Scientific Computing (PyHPC). 1--12. Google ScholarCross Ref
- CoMSES. 2007--2022. CoMSES Catalog. Retrieved May 14, 2022 from https://catalog.comses.net/visualization/Google Scholar
- Andrew Crooks. 2017. Cellular Automata. In International Encyclopedia of Geography. John Wiley & Sons, Ltd, 1--9. Google ScholarCross Ref
- Andrew Crooks and Christian Castle. 2012. The Integration of Agent-Based Modelling and Geographical Information for Geospatial Simulation. In Agent-based Models of Geographical Systems, Alison Heppenstall, Andrew Crooks, Linda M. See, and Michael Batty (Eds.). Springer, New York, NY, 219--252.Google Scholar
- Andrew Crooks, Christian Castle, and Michael Batty. 2008. Key Challenges in Agent-Based Modelling for Geo-Spatial Simulation. Computers, Environment and Urban Systems 32, 6 (Nov. 2008), 417--430. Google ScholarCross Ref
- Andrew Crooks, Nick. Malleson, Ed. Manley, and Alison Heppenstall. 2019. Agent-Based Modelling and Geographical Information Systems: A Practical Primer. Sage, London, UK.Google Scholar
- Andrew Crooks, Nicolas Malleson, Ed Manley, and Alison Heppenstall. 2019. Fundamentals of Geographical Information Systems. In Agent-Based Modelling and Geographical Information Systems: A Practical Primer, Richard Harris, Robert Rojek, John Nightingale, Katherine Haw, and Richard Leigh (Eds.). SAGE, Chapter 5, 95--124.Google Scholar
- George Datseris, Ali R. Vahdati, and Timothy C. DuBois. 2022. Agents.jl: A Performant and Feature-Full Agent-Based Modeling Software of Minimal Code Complexity. SIMULATION (Jan. 2022), 00375497211068820. Google ScholarCross Ref
- Owen Densmore. 2012. AgentScript. https://github.com/backspaces/agentscriptGoogle Scholar
- Environmental Systems Research Institute, Inc. 1998. ESRI Shapefile Technical Description. Technical Report. Environmental Systems Research Institute, Inc, Redlands, CA. https://www.esri.com/library/whitepapers/pdfs/shapefile.pdfGoogle Scholar
- Martin Fleischmann. 2019. momepy: Urban Morphology Measuring Toolkit. Journal of Open Source Software 4, 43 (2019), 1807. Google ScholarCross Ref
- Joël Foramitti. 2021. AgentPy: a Package for Agent-Based Modeling in Python. Journal of Open Source Software 6, 62 (June 2021), 3065. Google ScholarCross Ref
- Nigel Gilbert. 2020. Agent-Based Models. SAGE Publications, Inc. Google ScholarCross Ref
- Sean Gillies et al. 2007--. Shapely: Manipulation and Analysis of Geometric Objects. https://github.com/shapely/shapelyGoogle Scholar
- Sean Gillies, B Ward, AS Petersen, et al. 2013. Rasterio: Geospatial Raster I/O for Python Programmers. https://github.com/rasterio/rasterioGoogle Scholar
- Samuel Glismann. 2021. Ancillary Services Acquisition Model: Considering Market Interactions in Policy Design. Applied Energy 304 (Dec. 2021), 117697. Google ScholarCross Ref
- GRASS Development Team. 2022. Geographie Resources Analysis Support System (GRASS GIS) Software. Open Source Geospatial Foundation, USA. https://grass.osgeo.orgGoogle Scholar
- Aric Hagberg, Pieter Swart, and Daniel S Chult. 2008. Exploring Network Structure, Dynamics, and Function Using NetworkX. Technical Report LA-UR-08-05495; LA-UR-08-5495. Los Alamos National Lab. (LANL), Los Alamos, NM (United States). https://www.osti.gov/biblio/960616Google Scholar
- Alison Heppenstall, Andrew Crooks, Nick Malleson, Ed Manley, Jiaqi Ge, and Michael Batty. 2021. Future Developments in Geographical Agent-Based Models: Challenges and Opportunities. Geographical Analysis 53, 1 (2021), 76--91.Google ScholarCross Ref
- Alison Heppenstall, Nick Malleson, and Andrew Crooks. 2016. "Space, the Final Frontier": How Good Are Agent-Based Models at Simulating Individuals and Space in Cities? Systems 4, 1 (2016), 9.Google ScholarCross Ref
- Google Inc. 2008--. Protocol Buffers - Google's Data Interchange Format. https://developers.google.com/protocol-buffers/Google Scholar
- Kevin M. Johnston (Ed.). 2013. Agent Analyst: Agent-Based Modeling in ArcGIS. Esri Press, Redlands, CA.Google Scholar
- Kelsey Jordahl. 2014. GeoPandas: Python Tools for Geographic Data. https://github.com/geopandas/geopandasGoogle Scholar
- Jackie Kazil, David Masad, and Andrew Crooks. 2020. Utilizing Python for Agent-Based Modeling: The Mesa Framework. In Social, Cultural, and Behavioral Modeling, Robert Thomson, Halil Bisgin, Christopher Dancy, Ayaz Hyder, and Muhammad Hussain (Eds.). Springer International Publishing, Cham, 308--317. Google ScholarDigital Library
- Jayanta Kshirsagar, Akshay Dewan, and Harshal Hayatnagarkar. 2020. EpiRust: Towards A Framework For Large-scale Agent-based Epidemiological Simulations Using Rust Language. Google ScholarCross Ref
- Gail Langran. 1992. Time in Geographic Information Systems. CRC Press. Google ScholarCross Ref
- Leaflet. 2022. Basemap Providers. Retrieved Aug 24, 2022 from https://leafletjs.com/plugins.html#basemap-providersGoogle Scholar
- Jose Miguel Leon-Blanco, P. L. Gonzalez-R, Jose L. Andrade-Pineda, D. Canca, and M. Calle. 2022. A Multi-Agent Approach to the Truck Multi-Drone Routing Problem. Expert Systems with Applications 195 (June 2022), 116604. Google ScholarDigital Library
- Sean Luke. 2011. Multiagent Simulation and the MASON Library. https://cs.gmu.edu/~eclab/projects/mason/manual.pdfGoogle Scholar
- Sean Luke, Claudio Cioffi-Revilla, Liviu Panait, Keith Sullivan, and Gabriel Balan. 2005. MASON: A Multiagent Simulation Environment. SIMULATION 81, 7 (July 2005), 517--527. Google ScholarDigital Library
- Sean Luke, Robert Simon, Andrew Crooks, Haoliang Wang, Ermo Wei, David Freelan, Carmine Spagnuolo, Vittorio Scarano, Gennaro Cordasco, and Claudio Cioffi-Revilla. 2018. The MASON Simulation Toolkit: Past, Present, and Future. In Proceedings of the 19th International Workshop on Multi-Agent-Based Simulation, Paul Davidsson and Harko Verhagen (Eds.). Stockholm, Sweden, 75--87.Google Scholar
- David J Maguire. 2005. Towards a GIS Platform for Spatial Analysis and Modelling. GIS, Spatial Analysis, and Modelling (2005), 19--39.Google Scholar
- Steven Manson, Li An, Keith C. Clarke, Alison Heppenstall, Jennifer Koch, Brittany Krzyzanowski, Fraser Morgan, David O'Sullivan, Bryan C. Runck, Eric Shook, and Leigh Tesfatsion. 2020. Methodological Issues of Spatial Agent-Based Models. Journal of Artificial Societies and Social Simulation 23, 1 (2020), 3. Google ScholarCross Ref
- David Masad and Jacqueline Kazil. 2015. Mesa: An Agent-Based Modeling Framework. In Proceedings of the 14th Python in Science Conference. Austin, TX, 51--58. Google ScholarCross Ref
- Mesa. 2022. Repositories that Depend on Mesa. Retrieved Aug 24, 2022 from https://github.com/projectmesa/mesa/network/dependentsGoogle Scholar
- Mesa. 2022. Sugarscape Constant Growback model. https://github.com/projectmesa/mesa/blob/main/examples/sugarscape_cg/Readme.mdGoogle Scholar
- Alexander Michels, Jeon-Young Kang, and Shaowen Wang. 2020. An Exploration of the Effect of Buyer Preference and Market Composition on the Rent Gradient Using the ALMA Framework. In GeoSim '20: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation. Seattle, WA, 48--51.Google Scholar
- Nelson Minar, Roger Burkhart, Chris Langton, and Manor Askenazi. 1996. The Swarm Simulation System: A Toolkit for Building Multi-Agent Simulations. Technical Report. Santa Fe Institute. http://sfi-edu.s3.amazonaws.com/sfi-edu/production/uploads/sfi-com/dev/uploads/filer/8a/2a/8a2ae001-9ad5-43e6-b7e3-4d951223e9e8/96-06-042.pdfGoogle Scholar
- Jose Marie Antonio Minoza, Vena Pearl Bongolan, and Joshua Frankie Rayo. 2021. COVID-19 Agent-Based Model with Multi-Objective Optimization for Vaccine Distribution. Technical Report arXiv:2101.11400. arXiv. Google Scholar
- Michael J. North, Nicholson T. Collier, Jonathan Ozik, Eric R. Tatara, Charles M. Macal, Mark Bragen, and Pam Sydelko. 2013. Complex Adaptive Systems Modeling with Repast Simphony. Complex Adaptive Systems Modeling 1, 1 (March 2013), 3. Google ScholarCross Ref
- Michael J North, Thomas R Howe, Nick T Collier, and Jerry R Vos. 2005. The Repast Simphony Development Environment. In Proceedings of the Agent 2005 Conference on Generative Social Processes, Models, and Mechanisms, Vol. 13. 15.Google Scholar
- Claudio Nägeli, Martin Jakob, Giacomo Catenazzi, and York Ostermeyer. 2020. Towards Agent-Based Building Stock Modeling: Bottom-up Modeling of Long-Term Stock Dynamics Affecting the Energy and Climate Impact of Building Stocks. Energy and Buildings 211 (March 2020), 109763. Google ScholarCross Ref
- Dawn C. Parker, Steven M. Manson, Marco A. Janssen, Matthew J. Hoffmann, and Peter Deadman. 2003. Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review. Annals of the Association of American Geographers 93, 2 (June 2003), 314--337. Google ScholarCross Ref
- Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay. 2011. Scikit-Learn: Machine Learning in Python. J. Mach. Learn. Res. 12, null (Nov. 2011), 2825--2830.Google Scholar
- PySAL. 2022. PySAL: Python Spatial Analysis Library. Retrieved Aug 24, 2022 from https://pysal.org/Google Scholar
- QGIS Development Team. 2022. QGIS Geographic Information System. QGIS Association. https://www.qgis.orgGoogle Scholar
- Eric Russell and Uri Wilensky. 2008. Consuming Spatial Data in NetLogo Using the GIS Extension. In The Annual Meeting of the Swarm Development Group. Chicago, Il.Google Scholar
- Thomas C. Schelling. 1971. Dynamic Models of Segregation. The Journal of Mathematical Sociology 1, 2 (July 1971), 143--186. Google ScholarCross Ref
- Michael Seid, David Bridgeland, Alexandra Bridgeland, and David M. Hartley. 2021. A Collaborative Learning Health System Agent-Based Model: Computational and Face Validity. Learning Health Systems 5, 3 (July 2021), e10261. Google ScholarCross Ref
- Nikita Smirnov and Sven Tomforde. 2022. Navigation Support for an Autonomous Ferry Using Deep Reinforcement Learning in Simulated Maritime Environments. In 2022 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA). IEEE, 142--149. Google ScholarCross Ref
- Keith Sullivan, Mark Coletti, and Sean Luke. 2010. GeoMason: Geospatial Support for MASON. Technical Report. Department of Computer Science, George Mason University.Google Scholar
- Asia Air Survey and NextGIS. 2013--. Modules for Land Use Change Simulations. https://github.com/nextgis/molusceGoogle Scholar
- Patrick Taillandier, Benoit Gaudou, Arnaud Grignard, Quang-Nghi Huynh, Nicolas Marilleau, Philippe Caillou, Damien Philippon, and Alexis Drogoul. 2019. Building, Composing and Experimenting Complex Spatial Models With the GAMA Platform. GeoInformatica 23, 2 (April 2019), 299--322. Google ScholarDigital Library
- GRASS Development Team. 2003--2022. r.agent - GRASS GIS Reference Manual. https://grass.osgeo.org/grass80/manuals/addons/r.agent.htmlGoogle Scholar
- Kelly Thorp and Kevin Bronson. 2013. A Model-Independent Open-Source Geospatial Tool for Managing Point-Based Environmental Model Simulations at Multiple Spatial Locations. Environmental Modelling & Software 50 (Dec. 2013), 25--36. Google ScholarDigital Library
- Seth Tisue and Uri Wilensky. 2004. NetLogo: A Simple Environment for Modeling Complexity. In International Conference on Complex Systems, Vol. 21. 16--21.Google Scholar
- Paul M Torrens and Simin Gu. 2021. Real-time Experiential Geosimulation in Virtual Reality With Immersion-emission. In GeoSim '21: Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation. Beijing, China, 19--28.Google Scholar
- Jerry R Vos. 2005. Repast .NET : The Repast Framework Implemented in the .NET Framework. In Annual Conference of the North American Association for Computational Social and Organizational Science (NAACSOS).Google Scholar
- Gerd Wagner and Luis G. Nardin. 2018. Adding Agent Concepts to Object Event Modeling and Simulation. In 2018 Winter Simulation Conference (WSC). 893--904. Google ScholarCross Ref
- Haoliang Wang, Ermo Wei, Robert Simon, Sean Luke, Andrew Crooks, David Freelan, and Carmine Spagnuolo. 2018. Scalability in the MASON Multi-Agent Simulation System. In 2018 IEEE/ACM 22nd International Symposium on Distributed Simulation and Real Time Applications (DS-RT). 1--10. Google ScholarCross Ref
- Uri Wilensky. 1999. NetLogo. http://ccl.northwestern.edu/netlogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL.Google Scholar
- Uri Wilensky. 2006. NetLogo Grand Canyon Model. http://ccl.northwestern.edu/netlogo/models/GrandCanyonGoogle Scholar
- Michael Wooldridge and Nicholas R. Jennings. 1995. Intelligent Agents: Theory and Practice. The Knowledge Engineering Review 10, 2 (1995), 115--152. Google ScholarCross Ref
Index Terms
- Mesa-Geo: A GIS Extension for the Mesa Agent-Based Modeling Framework in Python
Recommendations
Utilizing Python for Agent-Based Modeling: The Mesa Framework
Social, Cultural, and Behavioral ModelingAbstractMesa is an agent-based modeling framework written in Python. Originally started in 2013, it was created to be the go-to tool in for researchers wishing to build agent-based models with Python. Within this paper we present Mesa’s design goals, ...
Geographic Information Systems and Spatial Agent-Based Model Simulations for Sustainable Development
In recent years the interdisciplinary field of Computational Social Science has developed theory and methodologies for building spatial Agent-Based Social Simulation (ABSS) models of human societies that are situated in ecosystems with land cover and ...
Site-specific management of common olive
The goal of this paper was to determine the correlation between image digital information and healthy olive trees management.Many data were gathered from tree and soil. Such as: fruit set, canopy volume (CV), shoot length (SL), trunk diameter (TD), ...
Comments