GIS and Agent-based modeling aim is to highlight and examine the latest advances in the fields of agent-based modeling, geographical information science (GIS) and other things along the way.
This is a research site focused around my interests in Geographical Information Science (GIS) and Agent-Based Modeling (ABM).
In this paper we discuss SES and how research into them poses many challenges, not least of which are collecting or compiling data at the appropriate scales and aligning social and environmental data to address SES questions. We discuss how SES have been studied using more traditional sources of data (e.g. census data, remote sensing etc.) and explore how social media can be used in the context of SES research. Specifically we ask three specific questions. 1) How can feedback between social and environmental systems be meaningfully studied using social media data? 2) How can using social media data re-frame or compliment current SES research questions and methods? and 3) Are there best practices for collecting and validating social media data for use in SES research? If these questions sound interesting to you, we encourage you to read the abstract below or the full paper.
Social media data provide an unprecedented wealth of information on people’s perceptions, attitudes, and behaviors at fine spatial and temporal scales and over broad extents. Social media data produce insight into relationships between people and the environment at scales that are generally prohibited by the spatial and temporal mismatch between traditional social and environmental data. These data thus have great potential for use in socio-environmental systems (SES) research. However, biases in who uses social media platforms and what they use them for create uncertainty in the potential insights from these data. Here, we describe ways that social media data have been used in SES research, including tracking land-use and environmental changes, natural resource use, and ecosystem service provisioning. We also highlight promising areas for future research and present best practices for SES research using social media data.
Over the last couple of years we have been working on generating synthetic human populations with realistic social networks with respect to the New York mega-city and surrounding region. This is being done for a variety of modeling applications such as the spread of a disease or exploring peoples reactions to disasters (which was a topic of a recent post on Computational Social Science of Disasters).
To this end, at the upcoming International Conference on Social Computing, Behavioral-Cultural Modeling and; Prediction and Behavior Representation in Modeling and Simulation (or SBP-BRiMS for short) we have a short working paper outlining some of our initial efforts to how people might react following a Nuclear Weapon of Mass Destruction (NWMD) event. In the paper we show some preliminary simulation results relating on how we are able to simulate basic commuting patterns and initial movement away from the affected area after the NWMD event (like those in the movies below). By using a synthetic population we are able to create an artificial world populated by agents with sufficient heterogeneity to create realistic movement patterns and the social networks which play a vital role in disaster situations. If you want to know more about this work, feel free to read the abstract blow or read the paper.
Individual connections between human beings often dictate where people go and how they behave, yet their representation through social networks are rarely used as measures of human behavior in agent-based models. Social networks are increasingly used for study of human behavior in disasters, and empirical work has shown that human beings prioritize the safety of themselves and loved ones (i.e., households) before helping neighbors and coworkers. Based on this assumption we have created a set of heuristics for modeling how agents behave in an emergency event and how the individual behavior aggregates into a variety of patterns of life. In this paper will present briefly our agent-based model being used to characterize the population’s reaction to a Nuclear Weapon of Mass Destruction (NWMD) event in the New York City region. Agents are modeled commuting on work-day schedules before the explosion of a small (10Kt) nuclear device. After the explosion, agents respond to signals in their environment and make decisions based on prioritization of safety for themselves and those in their networks. The model methodology demonstrates how social networks can be integrated into an agent-based model and act as a basis for decision-making, and preliminary simulations show how agents potentially respond to a NWMD event with measurable changes in location and network formations over space and time.
Keywords: Agent-Based Model, Human Behavior, Social Networks, Emergency, Disaster Response, Nuclear Weapon of Mass Destruction.
Various patterns of commuting behavior representing daily routines of the individual agents.
Proof of Concept: Commuting to Manhattan - YouTube
Proof of Concept: Agents Reacting to an Explosion - YouTube
Burger, A. G., Kennedy, W.G., Crooks, A.T., Jiang, N. and Guillen-Piazza, D. (2019), Modeling Society Reacting to a Nuclear Weapon of Mass Destruction Event, 2019 International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, Washington DC. (pdf)
"We cannot predict future cities, but we can invent them.
Cities are largely unpredictable because they are complex systems that are more like organisms than machines. Neither the laws of economics nor the laws of mechanics apply; cities are the product of countless individual and collective decisions that do not conform to any grand plan. They are the product of our inventions; they evolve. In Inventing Future Cities, I explore what we need to understand about cities in order to invent their future This book attempts to communicate many of the ideas concerning science, prediction and complexity that have been useful in thinking about cities and urban planning in the last fifty years."
For more info, or to buy Inventing Future Cities, see Mike’s blog.
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.
A sample of Models from CSS 645, Spring 2019 - YouTube
I would like to thank the Students of CSS 645: Spatial Agent-based Models of Human-Environment Interactions for their participation in the class.
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.
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.
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:
Agent Based Models for Spatial Simulation
Multi-Agent Based Spatial Simulation
Big Spatial Data Simulation
Spatial Data/Trajectory Generators
Road Traffic 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
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.
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.
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.
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.
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.
Most of the time when I teach a class instead of setting a final exam, I ask the students to carryout an end of semester research project. In my Introduction to Computational Social Science class this project entails the development of a computational model in an area of interest to the student . The aim of this exercise is to cement what the students have (hopefully) learnt during the semester. I.e.:
to understand the motivation for the use of computational models in social science theory and research;
to learn about the variety of CSS research programs across the social science disciplines;
to understand the distinct contribution that CSS can make by providing specific insights about society, social phenomena at multiple scales, and the nature of social complexity.
Below you can see some of the outputs from these projects this last fall. The models range in type from agent-based models, microsimulation to system dynamics models applied to a variety of topics from voting and political parties, the peer effects of students, urban decline, employment growth and rise and fall of civilizations and many other topics along the way.
A SAMPLE OF MODELS FROM CSS 600: Fall 2018 - YouTube