Saturday, June 08, 2019

A Semester of CSS 645: Spatial Agent-based Models of Human-Environment Interactions

This last Spring semester I taught a class entitled "Spatial Agent-based Models of Human-Environment Interactions". As with many of my courses, students were expected to complete a end of semester project, in this case, develop an agent-based model that explores some aspect of related to the course theme of human-environment interactions.  For several of the students this was their first exposure to either agent-based modeling or utilizing geographical information in the modeling process. In the movie below a selection of these projects can be seen. The projects ranged from migration, evacuation modeling during a natural disaster, gerrymandering, the spread of diseases, recidivism, Commons problems to that of urban decline. As can be seen the models ranged from abstract spatial representations to those utilizing geographical information as a foundation of their artificial worlds. Many of the models where created using NetLogo.



I would like to thank the Students of CSS 645: Spatial Agent-based Models of Human-Environment Interactions for their participation in the class.  

Wednesday, May 29, 2019

Postdoctoral Research Fellow in Urban Simulation

The George Mason University, Department of Geography and Geoinformation Science (GGS), in the College of Science invites application for a Postdoctoral Research Fellow position beginning August 1st, 2019. The project, which is supported by the Defense Advanced Research Projects Agency (DARPA), is jointly conducted by Andreas Züfle, Dieter Pfoser, and Andrew Crooks at GMU, and by Carola Wenk at Tulane. George Mason University has a strong institutional commitment to the achievement of excellence and diversity among its faculty and staff, and strongly encourages candidates to apply who will enrich Mason’s academic and culturally inclusive environment.

Responsibilities: 

The primary job responsibilities of this position consist of the design, development and refinement of an agent-based simulation framework of urban areas. For this purpose, we are using the existing Multiagent Simulation Toolkit (MASON) platform (written in Java) that has been developed at GMU. Using MASON, new agent logic will have to be implemented, thus creating agents that use socially plausible rules to traverse their simulated world, and to interact with other agents. This project has started in Spring 2018, and such a simulation has already been developed. A main responsibility will be to implement more complex agent logic efficiently, thus allowing more agents to make more complex decisions, find shortest paths between locations, and interact with their simulated world, at the same time. For this purpose, implemented algorithms will need to be highly parallelizable, thus allowing to scale simulation via distribution among computing clusters located at GMU and Tulane. The successful candidate will also supervise graduate-level research assistants, collaborate with fellow scholars, and promote the department’s accomplishments through publications, presentations, and other public events.

Required Qualifications:
  • Ph.D. in computer science, modeling and simulation, or closely related field;
  • Experience with Agent-Based Modeling and social science simulation;
  • Excellent written communication skills demonstrated by prior publications; and
  • A track record that demonstrates the ability to work well with interdisciplinary research teams.
Preferred Qualifications:
  • Strong programming skills in Java.
More Details: 

For more details and how to apply see: https://jobs.gmu.edu/postings/45722

Friday, May 24, 2019

Call for Papers: GeoSim 2019

https://www.geosim.org/

The 2nd International Workshop on Geospatial Simulation (GeoSim) 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. Example topics include, but are not limited to:
  • Urban Simulation
  • Agent Based Models for Spatial Simulation
  • Multi-Agent Based Spatial Simulation
  • Big Spatial Data 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
  • Applications for Spatial Simulation
The workshop seeks high-quality full (8 pages) and short (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 (which will be in Chicago, Illinois), as well as attend the workshop to present the accepted work which will then appear in the ACM Digital Library. 

This workshop should also be 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.

More information about the workshop along with key dates is available at: https://www.geosim.org/  

https://www.geosim.org/

Wednesday, May 22, 2019

Guest Editorial for Spatial Agent-based Models: Current Practices and Future Trends

Over the last few years we have seen spatial agent-based modeling beginning to bridge the gap from cautious early adoption towards general acceptance within the geographical sciences. One of the key features that has contributed to this is its ability to represent individual characteristics and behaviors.

In order to capture this evolution a while ago, Alison Heppenstall and myself  put out a call for papers that not only asked for papers that looked at current trends in agent-based modeling but also  for those  that highlighted and addressed the advances and challenges that researchers working within the area of spatial agent-based models face. We are happy to say this call is now over and in the current issue of GeoInformatica there are 6 great papers (full citations and links are provided below) and along with a editorial. The papers present not only a great synthesis of the current practices but also several of the key advances and challenges within the realm of spatial agent-based modeling are brought to bare. 

Several common themes will become apparent when reading the articles. All the authors were in agreement that while there has been a noticeable uptake in agent-based modeling, more work is needed to bridge the gap to acceptance as a standard tool within the spatial sciences (e.g. Polhill et al., 2019). Data (variable quality and availability) was an issue that was discussed by almost all of the authors, particularly how to translate high quality data into models to create behavioral rules and the use of novel forms of data to calibrate an empirical model (e.g. Crols and Malleson, 2019). How to represent and simulate behavior in agent-based models was also a recurrent issue with two papers discussing how approaches borrowed from machine learning can be used to improve the representation of behavior (e.g. Runck et al., 2019; Abdulkareem et al., 2019). How to create models that could scale from the micro to macro was another theme with the point being made that current agent-based modeling architectures do not foster models that are easily translatable to a regional or global context (e.g. Taillandier et al., 2019), nor are interactions across scales adequately addressed in most models (e.g. Lippe et al., 2019). The papers also highlight that to cross the bridge from novel tool to full acceptance as a standard tool within the geographical sciences, spatial agent-based modeling still has some way to go. However, the papers in this special issue can therefore be seen as a stepping stone towards this.

Papers in the Special Issue:

Our Editorial:
Heppenstall, A. and Crooks, A.T. (2019), Guest Editorial for Spatial Agent-based Models: Current Practices and Future Trends, GeoInfomatica. 23(2): 243-268 (pdf)

Friday, April 26, 2019

Computational Social Science of Disasters: Opportunities and Challenges

Figure 1: Relation of computational social science of
disasters (CSSD) with other fields.
Past posts have discussed or demonstrated how  computational social science (CSS) (i.e. the study of social science through computational methods) can be utilized explore disasters or diseases but this has not really been  formalized.  To this end, Annetta Burger, Talha Oz, William Kennedy and myself have just had a paper published in Future Internet entitled "Computational Social Science of Disasters: Opportunities and Challenges". In the paper we introduce computational social science of disasters (CSSD). CSSD is defined as an approach to explain the social dynamics of disasters via computational means by adopting the relevant parts of CSS, social sciences in disaster, and crisis informatics as depicted in Figure 1. Specifically, we briefly review the domains and the approaches of each of the traditional social science disciplines to disasters (e.g. sociology, psychology, anthropology, political science, and economics). Next we describe the fields of CSS and crisis informatics before discussing the components of CSSD. We highlight some exemplar studies which capture certain elements of CSSD along with the challenges and opportunities it brings to the study of disasters. If you would like to find out more, below is the abstract to the paper along with the full reference and link to the paper.

Abstract
Disaster events and their economic impacts are trending, and climate projection studies suggest that the risks of disaster will continue to increase in the near future. Despite the broad and increasing social effects of these events, the empirical basis of disaster research is often weak, partially due to the natural paucity of observed data. At the same time, some of the early research regarding social responses to disasters have become outdated as social, cultural, and political norms have changed. The digital revolution, the open data trend, and the advancements in data science provide new opportunities for social science disaster research. We introduce the term computational social science of disasters (CSSD), which can be formally defined as the systematic study of the social behavioral dynamics of disasters utilizing computational methods. In this paper, we discuss and showcase the opportunities and the challenges in this new approach to disaster research. Following a brief review of the fields that relate to CSSD, namely traditional social sciences of disasters, computational social science, and crisis informatics, we examine how advances in Internet technologies offer a new lens through which to study disasters. By identifying gaps in the literature, we show how this new field could address ways to advance our understanding of the social and behavioral aspects of disasters in a digitally connected world. In doing so, our goal is to bridge the gap between data science and the social sciences of disasters in rapidly changing environments.

Keywords: Disasters; Computational Social Science; Crisis Informatics; Disaster Modeling, Web 2.0; Social Media; Big Data; Volunteered Geographical Information; Crowdsourcing.
Figure 2: Interactions of data analysis, computational models, and social theory
in computational social science of disasters.

Full Reference:
Burger, A., Oz, T., Kennedy, W.G. and Crooks, A.T. (2019), Computational Social Science of Disasters: Opportunities and Challenges, Future Internet, 11(5): 103; https://doi.org/10.3390/fi11050103. (pdf)