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)

Friday, March 29, 2019

Drafting Agent-Based Modeling into Basketball Analytics

http://scs.org/springsim/
Readers of this blog might find this post a little  out of left field (sorry I could not find a better analogy) as it about basketball and therefore the ball is in your court if you want to keep reading.

At the upcoming SpringSim conference Matthew Oldham and myself  just had a paper accepted entitled "Drafting Agent-Based Modeling into Basketball Analytics" where we take a shot at modeling basketball. Why you might ask? The rational is that sports analytics (SA) is a multi-million dollar industry but to date little attention has been given to agent-based modeling (ABM) even though sports can be viewed as a complex adaptive system (Matthew on his site has a great write up of this). To explore this notion we built an agent-based model (utilizing NetLogo 3D) which captures the basic dynamics of a basketball game. In order to calibrate the processes within the model we utilized 17 seasons (2000 to 2016) of individual game data from the National Basketball Association (NBA). The data collected included; game scores, winning margins, field goal attempts, the percentage of field goals made, rebounds, steals, and turnovers. From the NBA game data, density functions were calculated to aid calibrating certain aspects of the model.  Via a set of experiments, the model indicates that an increased belief in the franchise player (think Michael Jordan) leads to increased scoring action, but a belief in the hot-hand had a minor effect. This results comes from the ability of agent-based models to identify the micro-interaction of agents responsible for generating system level outcomes and thereby, demonstrating the utility of ABM to SA.

Below, you a can read the abstract of the paper, along with some figures outlining the play cycle in the model, some some results of the varying NBA game metrics compared to the model, a movie of the graphical user interface of the model during a representative game. Finally at the end of the post you can find link to the model and the actual paper. 
The growth of sports analytics (SA) has raised numerous research topics across a variety of sports, including basketball. Agent-based modeling (ABM) has great potential to assist and inform SA, but to date it has not been utilized. To support the use of ABM in SA, a model of a basketball game, which considers most fundamentals of play, is presented. Additionally, player behavior is partially predicated on assessing the length of a player’s shooting streak (testing the “hot-hand” effect) and the consideration a team gives to a streak and their franchise player. The model’s output is used to calibrate and validate it against statistics from the National Basketball Association (NBA). Via a set of experiments, the model indicates that an increased belief in the franchise player leads to increased scoring action, but a belief in the hot-hand a minor effect. Thereby, demonstrating the utility of ABM to SA, thus opening a new research field.

Keywords: agent-based modeling, sports analytics, hot-hand effect.
Figure 2: The play cycle of the model.

Figure 3: Distribution of the varying NBA game metrics compared to the model.




The model along with a detailed Overview, Design concepts, and Details (ODD) document can be found at: https://tinyurl.com/ABMBasketBall.

Full Reference:
Oldham, M and Crooks, A.T. (2019) Drafting Agent-Based Modeling into Basketball Analytics, 2019 Spring Simulation Conference (SpringSim’19), Tucson, AZ. (pdf)

Friday, January 18, 2019

Agent-based Modelling and Geographical Information Systems: A Practical Primer

Its been a long time in the making but now "Agent-Based Modelling and Geographical Information Systems: A Practical Primer" has been published by Sage. We (Nicolas Malleson, Ed Manley, Alison Heppenstall and myself) approached this book from two standpoints. First, to provide a synthesis of the underpinning ideas, techniques and frameworks for integrating agent-based modelling and geographical information systems (GIS). Second, building on our experiences of teaching at various levels, to provide a practical set of information for the development of agent-based models for geographical systems.


From these two standpoints we have developed a book that provides a practical primer in the integration of agent-based modelling and geographical information systems. In outlining the subject we cover many examples of geographical phenomena, from linking the individual movements of pedestrians to aggregate patterns of urban growth, to the integration of social networks into modelling mobility. Through this text, we hope  the reader will understand how the field has developed, how agent-based models are different from other modelling approaches, and the future challenges we see lying ahead.
By using sample code and data (all of which can be found on the accompanying website https://www.abmgis.org/) we provide the reader with many of the basic building blocks for constructing agent-based models linked to geographical information systems. Throughout the book we use the software package NetLogo, as it provides an easy route to learn and build agent-based models (although in the appendix we provide links to other models created in other platforms).

For more information visit https://www.abmgis.org/ and if you wish to buy a copy you can: Amazon or Sage Publishing. We hope you enjoy it. 
Figure 6.1 Abstracting from the real world to a series of layers to be used in the artificial world
upon which the agent-based model is based.
Full Reference:
Crooks, A.T., Malleson, N., Manley, E. and Heppenstall, A.J. (2019), Agent-based Modelling and Geographical Information Systems: A Practical Primer, Sage, London, UK.