Monday, December 21, 2015

A semester of CSS

For the last few years one of the classes that I have given is the Introduction to Computational Social Science (CSS). This is often the first class many students take within our program and as such its objectives are:
  1. To understand the motivation for the use of computational models in social science theory and research, including some historical aspects (Why conduct computational research in the social sciences?).
  2. To learn about the variety of CSS research programs across the social science disciplines, through a survey of social simulation models (What has CSS accomplished thus far?).
  3. 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 (What is the relation between computational social science.
  4. To provide foundations for more advanced work in subsequent courses or projects for those students who already have or will develop a long-term interest in computational social science.
The course surveys computational approaches such as system dynamics, social network analysis, machine learning, cellular automata, discrete event simulation, agent-based modeling, and microsimulation to study social phenomena with emphasis on complexity theory. 

On thing that all the students need to do during the semester is create a computational model investigating a social science research question. This exercise is often their first model that many students ever create. Below you can see some of this years models. Most of the models where created in NetLogo.  

Thursday, November 19, 2015

Using Shapefiles in NetLogo

Carrying on with the theme of linking GIS to NetLogo for creating agent-based models. We would like to showcase two simple models. Both are based on using a shapefile to create Schelling-inspired segregation model.

In the first model (as shown in the movie below) we use the polygons to be individual agents. In this example, we import a shapefile where each polygon is either a red or blue agent; or unoccupied (grey). The agent evaluates its neighborhood (i.e. surrounding polygons) and if dissatisfied with its neighborhood moves to an unoccupied polygon. More information  about the model and simple tutorial can be found here and the code and data can be downloaded from here.

In the second example (as shown in the movie below), we use attributes from the shapefile to add a specific number of agents to each polygon and again, the agents move if they are dissatisfied  with their current location. This is calculated by the agents examining both their geometrical neighboring polygons; and their 8-connected neighbors. The underlying color of the polygon is based on the which agent group is in the majority (i.e. a red polygon has more red agents than blue agents).

In addition to showing how to work with shapefiles within NetLogo we have also added some statics which you might find useful. The ability to calculate  Moran's I and a segregation index. Further  information  about the model can be found here and the code and data can be downloaded from here.

If you are interested in learning more about GIS and agent-based modeling in NetLogo it is worth checking out Yang Zhou's website Geospatial Computational Social Science.

Tuesday, October 13, 2015

Rainfall Model: NetLogo
Now whats seems like a long time ago, we where inspired to by  the NetLogo Grand Canyon Model and created a similar model in GeoMason. Now we have returned to NetLogo to test its ability to handle GIS data. Below you can see our attempt. The model is based on importing a geotiff of the National Elevation Dataset at 1 arc second from the National Map of Crater Lake, Oregon. After which water is added (which could be considered loosely as agents) and flows from high to low elevations, if the water cannot flow over the surface, it pools up.

The first movie below acts as a verification exercise about the basic functioning of the model (i.e. did we build the model right?). Here we have three different map types. The first being flat, the second being a cone and the third being a hill. The idea with these map types is to ensure the basic functioning of the model is correct.

After we were happy with the model, the Crater Lake example was implemented. As the movie below shows, over time, Crater Lake slowly fills up until the water breaches the caldera rim which allows the water to flow out. An extra addition to the model is the addition of erosion. Whereby as water flows over the surface, it picks up some sediment (in this case 1 unit of elevation) and when it stops moving, it deposits the sediment. As a result the terrain in the area changes. 

While carrying out this exercise, we also thought about testing NetLogo's 3D capacity with respect to creating geographically explicit agent-based models. The movie below shows the results.

More information about the models in this post can been seen on Yang Zhou's website. Also the models can be downloaded from GitHub. We hope you enjoy.

Tenure-Track Assistant Professor, Computational Social Science

Readers of this blog might be interested in the following position.

Tenure-Track Assistant Professor, Computational Social Science 

The George Mason University Computational and Data Sciences (CDS) Department in the College of Science invites applicants for a full-time, tenure-track faculty position at the Assistant Professor level. 

Beginning Fall 2016, this position is intended to primarily support the Computational Social Science (CSS) Program within CDS, including support of the Ph.D. degree in CSS, a master’s degree in interdisciplinary studies, and a CSS certificate. This position will also support undergraduate programs that are currently under development.

Potential for success in both research and teaching are the primary criteria for this position. Applicants should have a promising research record, with a deep knowledge of and interest in computation as applied to one or more of the social sciences. While we are open to expertise in all areas of computational social science, we are particularly interested in social network specialists interested in both theory and data. Applicants must have a Ph.D. (expected completion by August 2016 is acceptable) from an accredited institution.

About the Program:

Methodologically, the CSS Program focuses on data-driven social science models using social network and agent-based computational approaches from a complexity perspective. Current faculty members have domain expertise in economics and finance, political science and international relations, geography and geographic information systems, land use and cover change, and public policy. As one of the first programs of its type in the world, CSS has had significant success in both research and professional placement. Our students come from all over the world (the Americas, Europe, Africa, Asia and Australia) and have been placed at a variety of top universities (e.g., University of Oxford, University College London), at government agencies, as well as in the private sector, including start-up companies.

More Information: 

Friday, September 25, 2015

Urban Growth Model in NetLogo

Recently Yang Zhou, a PhD student in the Computational Social Program carried out a partial re-implementation of the SLEUTH urban growth model (without Self Modification). The region of study is Santa Fe, New Mexico. The data was obtained from The National Map. The model demonstrates how several raster layers can be used to initialize a NetLogo model. Hopefully others who want to know how to create spatially explicit models in NetLogo will find this useful. The model and data can be downloaded from Yang's GitHub account.

Also you can export the data to at any time to see how the land cover changes over time. For example in the image below we show the land cover at the initialization of the model (t=0, top) and the land cover at t=10 (bottom).
To find out more about CA models, the movie below by Andreas Flache offers a good introduction:

Mesa: An Agent-Based Modeling Framework in Python

Just a short post to say two of our PhD students, David Masad and Jackie Kazil have been developing an agent-based modeling framework in Python called Mesa.

To quote from David's talk abstract:
"Agent-based modeling is currently a hole in in Python’s robust and growing scientific ecosystem. Mesa is a new open-source package meant to fill that gap. It allows users to quickly create agent-based models using built-in core components (such as agent schedulers and spatial grids) or customized implementations; visualize them using an innovative browser-based interface; and analyze their results using Python’s robust data analysis tools. Its goal is to be a Python 3-based alternative to other popular frameworks based in other languages such as NetLogo, Repast, or MASON."

Below is short presentation outlining Mesa from SciPy 2015:

Thursday, September 17, 2015

Call for papers: Symposium on Human Dynamics Research: Urban Analytics at the 2016 AAG

Call for papers: AAG 2016. San Francisco. 29th March – 2nd April

Symposium on Human Dynamics Research: Urban Analytics

A deluge of new data created by people and machines is changing the way that we understand, organise and model urban spaces. New analytics are required to make sense of these data and to usefully apply findings to real systems. This session seeks to bring together quantitative or mixed methods papers that develop or use new analytics in order to better understand the form, function and future of urban systems. We invite methodological, theoretical and empirical papers that engage with any aspect of urban analytics. Topics include, but are not limited to:
  • New methodologies for tackling large, complex or dirty data sets;
  • Case studies involving analysis of novel or unusual data sources;
  • Policy analysis, predictive analytics, other applications of data;
  • Intensive modelling or simulation applied to urban areas or processes; 
  • Individual-level and agent-based models (ABM) of geographical systems; 
  • Validating and calibrating models with novel data sources; 
  • Ethics of data collected en masse and their use in simulation and analytics.

Please e-mail the abstract and key words with your expression of intent to Nick Malleson ( by 22nd October, 2015 (one week before the AAG session deadline). Please make sure that your abstract conforms to the AAG guidelines in relation to title, word limit and key words and as specified at:

An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and  conclusions.

Timeline summary:

  • 22nd October, 2015: Abstract submission deadline. E-mail Nick Malleson by this date if you are interested in being in this session. Please submit an abstract and key words with your expression of intent.
  • 25th October, 2015: Session finalization and author notification
  • 28th October, 2015: Final abstract submission to AAG, via All participants must register individually via this site. Upon registration you will be given a participant number (PIN). Send the PIN and a copy of your final abstract to Nick Malleson. Neither the organizers nor the AAG will edit the abstracts.
  • 29th October, 2015: AAG registration deadline. Sessions submitted to AAG for approval.


  • Nick Malleson, School of Geography, University of Leeds  
  • Alex Singleton, School of Environmental Sciences, University of Liverpool  
  • Mark Birkin, Director of the University of Leeds Institute for Data Analytics (LIDA)  
  • Paul Longley, Department of Geography, University College London  
  • Andrew Crooks, Department of Computational and Data Sciences, George Mason University.   
  • Seth Spielman, Geography Department, University of Colorado

Thursday, September 03, 2015

Walk this Way

We recently had published in ISPRS International Journal of Geo-Information a paper entitled "Walk this Way: Improving Pedestrian Agent-Based Models through Scene Activity Analysis". In the paper we explore how new data can help inform our agent-based models. Specifically, pedestrian modeling which has been traditionally faced with the challenge of collecting data to calibrate and validate such models of pedestrian movement. Below is the abstract for the paper:
Pedestrian movement is woven into the fabric of urban regions. With more people living in cities than ever before, there is an increased need to understand and model how pedestrians utilize and move through space for a variety of applications, ranging from urban planning and architecture to security. Pedestrian modeling has been traditionally faced with the challenge of collecting data to calibrate and validate such models of pedestrian movement. With the increased availability of mobility datasets from video surveillance and enhanced geolocation capabilities in consumer mobile devices we are now presented with the opportunity to change the way we build pedestrian models. Within this paper we explore the potential that such information offers for the improvement of agent-based pedestrian models. We introduce a Scene- and Activity-Aware Agent-Based Model (SA 2 -ABM), a method for harvesting scene activity information in the form of spatiotemporal trajectories, and incorporate this information into our models. In order to assess and evaluate the improvement offered by such information, we carry out a range of experiments using real-world datasets. We demonstrate that the use of real scene information allows us to better inform our model and enhance its predictive capabilities.

Keywords: pedestrian modeling; pedestrian tracking; activity monitoring; spatiotemporal trajectories; agent-based modeling
As with many of our models, the source code of the model can be downloaded from here. To give a sense of the model, the movie below shows how agents traverse the scene.

Full Reference: 
Crooks, A.T., Croitoru, A., Lu, X., Wise, S., Irvine, J. and Stefanidis, A. (2015),  Walk this Way: Improving Pedestrian Agent-Based Models through Scene Activity AnalysisISPRS International Journal of Geo-Information, 4(3): 1627-1656. (pdf)

Saturday, August 29, 2015

Summer Research Projects

Over the summer, Arie Croitoru and myself took part for the first time in the George Mason University Aspiring Scientists Summer Internship Program (ASSIP). We worked with three very talented high-school students who over the course of the seven and a half week program produced some excellent research around the areas of agent-based modeling (ABM), volunteered geographical analysis (VGI), social media and text analysis. An overview of their work can be seen below in the posters and abstracts that the students produced at the end of the internship.

End of Summer Research Poster Display
In the first project, Kevin Geng and Varun Talwar explored how online news stories propagate around the world in a project entitled: "MediaPulse: A System Prototype for News Media Aggregation and Analytics". We would also like to thank Trevor Thrall for his assistance and guidance in this project. Below you can read the abstract and see the poster from this work.
"The news media play a major role in shaping the opinions and beliefs of people around the globe. Alongside more traditional media distribution modes, such as the printed press, in recent years the Internet has begun to play a more important role in news distribution. With the emergence of online news, information can now be disseminated across the globe in real-time. By monitoring such online sources, we can, for the first time, obtain valuable insight into the propagation of news around the world over time, and understand how such media is both produced and consumed. However, obtaining and analyzing this data on a massive scale has proven to be challenging. To address this challenge, we present a novel system for the collection and analysis of news articles. Our system has the capability to extract both metadata and text from a large array of online news sources, and analyze it with respect to themes, locations, time, and language. In order to showcase the utility of our system, we selected 440 prominent news sources across the globe, and monitored their RSS feeds every hour using our system. Through this process we collected roughly 30,000 articles per day over the duration of the study period. To demonstrate the analytical capabilities of our system, we present a case study analysis of coverage of the Islamic State of Iraq and Syria (ISIS) using the system that we developed. In particular, we analyze ISIS-related news using both their content and metadata to show how news propagates over time and space, and explore how the sentiment of the coverage varies."

In a second project Rohan Suri developed an agent-based model to explore the spread and  containment of Ebola in a project entitled "A novel computational agent based model for the spread and containment of Ebola Virus Disease". Below is the abstract, poster and a example model run from this project. More information about the model can be found here.
"During the Summer of 2014, the countries of Sierra Leone, Guinea, and Liberia were devastated by an Ebola Virus Disease (EVD) epidemic.  Although it killed more than 10,000 people, little is known about EVD dynamics in a macro population. While various attempts have been made to better understand EVD dynamics, such past attempts at modeling EVD exclude an explicit spatial scale, implied general mixing, and did not consider human-to-human interactions. In view of these limitations, this research aims to develop a novel computational agent based model (ABM) to investigate spatial and temporal EVD spread, and to study the effectiveness of control and prevention measures for EVD. In this model, OpenStreetMap (OSM) data was used to construct the physical environment (e.g., road networks), and a realistic population for the three countries was generated from Landscan data and previous surveys. EVD spread was modeled through explicit agent-to-agent interaction and the use of a Suspected-Infected-Exposed-Recovered (SEIR) model"

Example simulation run:

It was a great learning experience from our side by participating in the ASSIP Program.

Monday, August 03, 2015

Slides from Lipari: GIS and Agent-Based

I have just come back from the Lipari School on Computational Social Science which was focusing on Algorithms, Data, and Models for Social and Urban Systems. While there I gave a class entitled "GIS and Agent-based modeling" split over two parts. Below you can see the abstract for my class and the slides.

Abstract: Understanding and modeling human behavior is not as simple as it sounds. This is because humans do not just make random decisions, but base actions upon their knowledge and abilities. Moreover, one might think that human behavior is rational, but this is not always the case: decisions can also be based on emotions (e.g. happiness, anger, fear). Emotions can influence decision-making by altering our perceptions about the environment and future evaluations. The question therefore is how we model human behavior? Over the last decade, one of the dominant ways of modeling human behavior in its many shapes and forms has been through agent-based modeling (ABM). In this workshop I will provide a general overview of what agents are, why there is a need for agent-based models for studying cities, and how it links to how we believe societies operate through ideas of complexity theory. I will sketch out how geographical information systems (GIS) can be used to create spatially explicit agent-based models, before reviewing a range of applications where agent-based models have been developed using geographical data. The workshop concludes with an overview of challenges modelers face when using agent-based models to study geographical problems with a special emphasis on cities, and identify future avenues of research relating to big data and social network analysis.

In addition to giving my own class I was also fortunate to attend a diverse set of classes by several other people including  Alessandro Mei, Carlo Ratti, Claudio Cioffi Revilla, Paolo Ferragina, Wulf Daseking and Daniele Quercia. All of which explored how urban systems can be studied from a variety of disciplines. One of the highlights of the summer school for me was interacting with the students and learning about their own research. The school was excellent and the location was perfect for learning and relaxing. 

Friday, June 26, 2015

Computational Social Science for Urban Dynamics

I just came back from a workshop organized by the JASON's that was focusing on decision making from complex modeling. During the workshop I gave a talked entitled "Computational Social Science for Urban Dynamics"

In the talk I introduced, computational social science  (CSS) and why one should use it for studying urban systems. Then I went over a collection of agent-based modeling (ABM) applications from basic examples to more complex ones such as pedestrian movement, border security. I then discussed the rise of crowdsourcing and how this has made new kinds of geographic data available to the computational CSS community. New forms of data range in their characteristics and purpose. One example is Volunteered Geographical Information (VGI), were users purposely contribute Geographic Information (GI) as in the case of OpenStreetMap; another is Ambient Geographic Information (AGI), where the intention of contributors is not necessarily to provide GI, but GI can be derived, as from Twitter.  I then  discuss the opportunities that crowdsourced data provides for ABMs, specifically focusing on how such information gives us a new lens to study the micro interactions of individuals. Through as series of examples I  demonstrated how such data can be integrated into geographically explicit ABMs. By building on these examples I  showcased how the spatial environment and agent populations can be built using crowdsourced information, and highlight how agent behaviors can be informed and validated by such information. Lastly I discussed the challenges associated with this program of research: using such data is not without its difficulties, including gathering or accessing the data, storing the data, analyzing the collected data, and assessing its validity. Overall this talk provides a brief overview of the current state of crowdsourced data-informed ABM.

Please note many of the models and papers cited in the presentation can be found under research or publications sections of this website. Also I would like to acknowledge and say thank you to the GeoSocial team at Mason along with current and former students for providing material and much food for thought for this presentation.

Tuesday, May 26, 2015

A Semester with Spatial Agent-based Models

With the spring semester over, I thought I would show some of the final agent-based modeling projects that were carried out in CSS 645: Spatial Agent-based Models of Human-Environment Interactions. As always I was quite impressed the models and we had a plethora of topics ranging from mobile agent-based models, shopping, pick pocketing, route finding, travel to work, the spatial spread of information, deer management, urban growth etc... What is interesting is that while the majority of models are implemented in NetLogo, more and more are being done in Python.

Something new for this semester is we also tried to reproduce a published model. Below you can see 3 examples of such work. Click here to see a previous post on reproduction and replication. 

Wednesday, April 22, 2015

Leveraging Crowdsourced data for Agent-based modeling: Opportunities, Examples and Challenges

This week I am attending the AAG Annual Meeting in Chicago. While here, we organized 3 sessions entitled "Geosimulation and Big Data: A Marriage made in Heaven or Hell?" in which I presented a paper, co-authored with Sarah Wise: "Leveraging Crowdsourced data for Agent-based modeling: Opportunities, Examples and Challenges." The abstract is below:
The rise of crowdsourcing has made new kinds of data available to the  geographical community. New forms of data range in their characteristics and purpose. One example is Volunteered Geographical Information (VGI), were users purposely contribute Geographic Information (GI) as in the case of OpenStreetMap; another is Ambient Geographic Information (AGI), where the intention of contributors is not necessarily to provide GI, but GI can be derived, as from Twitter. While much progress has been made in utilizing these new sources of data in GIScience, they have only recently begun to be integrated into agent-based models (ABM). This paper will discuss the opportunities that crowdsourced data provides for ABMs, specifically focusing on how such information gives us a new lens to study the micro-interactions of individuals. Through as series of examples we will demonstrate how such data can be integrated into geographically explicit ABMs. By building on these examples we will showcase how the spatial environment and agent populations can be built using crowdsourced information, and highlight how agent behaviors can be informed and validated by such information. We further discuss the challenges associated with this program of research: using such data is not without its difficulties, including gathering or accessing the data, storing the data, analyzing the collected data, and assessing its validity. Together, this work provides a brief overview of the current state of crowdsourced data-informed ABM. 
If you like what is written above, you can have a flick through the slides from the talk or check out one of the movies:

Tuesday, March 31, 2015

Exploring Creativity and Urban Development with Agent-Based Modeling

There is considerable debate about "creative cities" and relatively few agent-based models that explore such ideas from the bottom up. To that end we have recently published a paper in the Journal of Artificial Societies and Social Simulation entitled: "Exploring Creativity and Urban Development through Agent-Based Modeling"

In the paper we introduce the Creative City Model, an exploratory ABM to simulate the theoretical relationship between land-use regulation, urban mobility and societal tolerance on the economic performance of cities. The model is based on simplified assumptions from our empirically informed understanding of urban morphology, economic geography and the diffusion of creativity from human interactions.  It contributes to the growing literature exploring the dynamic socioeconomic processes underlying urban economic growth through computer simulation. Specifically the model offers a new lens to view the diffusion of creativity through knowledge spillovers under various scenarios from the bottom up. Through experimentation, the model suggests the existence of tradeoffs between the desire for social equity, estimated via rent affordability, and the rapid diffusion of creativity. Below you can find the abstract of the paper.

Scholars and urban planners have suggested that the key characteristic of leading world cities is that they attract the highest quality human talent through educational and professional opportunities. They offer enabling environments for productive human interactions and the growth of knowledge-based industries which drives economic growth through innovation. Both through hard and soft infrastructure, they offer physical connectivity which fosters human creativity and results in higher income levels. When combined with population density, socioeconomic diversity and societal tolerance; the elevated interaction intensity improves productivity. In many developing country cities however, rapid urbanization is increasing sprawl and causing deteriorating in public service standards. We further explore these insights by creating a stylized agent-based model where heterogeneous and independent decision-making agents interact under the following scenarios: (1) improved urban transportation investments; (2) mixed land-use regulations; and (3) reduced residential segregation. We find that any combination of scenarios resulting in conditions of intense human interaction results in greater economic growth. However, model results also demonstrate a clear trade-off between rapid economic progress and socioeconomic equity mainly due to the crowding out of low- and middle-income households from clusters of creativity. 

Key Words: Agent-Based Modeling; Developing Countries; Urban; Segregation; Land-use; Transportation
The movie below shows a typical simulation run of the model.

Further details about the model along with its ODD is available from the OpenABM website (click here).

Full Reference:
Malik, A.A., Crooks, A.T., Root, H.L. and Swartz, M. (2015), Exploring Creativity and Urban Development through Agent-Based Modeling, Journal of Artificial Societies and Social Simulation. 18 (2): 12. Available at

Friday, March 27, 2015

Bumble Bee Colonies

When reading papers about agent-based models / individual-based models, I am always curious if the model can be reproduced from the description in said paper. Specifically whether there is sufficient information in the paper to reproduce the model and the results. Often we task students with such a task as a learning exercise and I thought it would be nice to show you one such example done by Dale Brearcliffe.

The model that was reproduced is entitled "The Ontogeny of the Interaction Structure in Bumble Bee Colonies: A MIRROR Model" by Hogeweg and Hesper (1983) which explored whether or not an individual-oriented model of population dynamics and simple bumble bee behaviors could produce the ontogeny of the social interaction of the colony? The original model used MIRSYS, a program written   in INTERLISP. Dale was able to take the paper and reproduce the model using NetLogo, but not replicate the results (click here to read more and download the model).

Graphical User Interface of Reproduced  Model

Nest composition and development in the original model (Source: Hogeweg and Hesper, 1983).
Nest composition and development in the reproduced model.

Full Reference:
Hogeweg, P. and Hesper, B. (1983), 'The Ontogeny of the Interaction Structure in Bumble Bee Colonies: A MIRROR Model', Behavioral Ecology and Sociobiology, 12(4): 271-283. 

For those interested in reproduction and replication have a look at the following articles:
Axtell, R., Axelrod, R., Epstein, J.M. and Cohen, D. (1996), 'Aligning Simulation Models: A Case Study and Results', Computational and Mathematical Organization Theory, 1(2): 123-141. 
Drummond, C. (2009), 'Replicability is Not Reproducibility: Nor is it Good Science', Proceedings of the 4th Workshop on Evaluation Methods for Machine Learning at the 26th International Conference on Machine Learning, Montreal, Canada.
Wilensky, U. and Rand, W. (2007), 'Making Models Match: Replicating an Agent-Based Model', Journal of Artificial Societies and Social Simulation, 10(4): 2, Available at

Thursday, March 26, 2015

Collective Behavior of In-group Favoritism

We just had a paper accepted in Advances in Complex Systems entitled "The Effect of In-group Favoritism on the Collective Behavior of Individuals' Opinions." In the paper we develop and an agent-based model to explore how individuals interact and how more  collective behaviors emerge (e.g. reaching a consensus or the spreading of opinions). The abstract of the paper is as follows:

Empirical findings from social psychology show that sometimes people show favoritism toward in-group members in order to reach a global consensus, even against individuals' own preferences (e.g., altruistically or deontically). Here we integrate ideas and findings on in-group favoritism, opinion dynamics, and radicalization using an agent-based model entitled cooperative bounded confidence (CBC). We investigate the interplay of homophily, rejection, and in-group cooperation drivers on the formation of opinion clusters and the emergence of extremist, radical opinions. Our model is the first to explicitly explore the effect of in-group favoritism on the macro-level, collective behavior of opinions. We compare our model against the two-dimentional bounded confidence model with rejection mechanism, proposed by Huet et al. (2008), and find that the number of opinion clusters and extremists is reduced in our model. Moreover, results show that group influence can never dominate homophilous and rejecting encounters in the process of opinion cluster formation. We conclude by discussing implications of our model for research on collective behavior of opinions emerging from individuals' interaction. 
 Keywords: Opinion dynamics; in-group favoritism; homophily; radicalization; extremism.

Full reference:
Alizadeh, M., Cioffi-Revilla, C. and Crooks, A.T. (2015), The Effect of In-group Favoritism on the Collective Behavior of Individuals' Opinions, Advances in Complex Systems. DOI: 10.1142/S0219525915500022 (pdf)
The code for the model is available from here.

Friday, March 20, 2015

Lipari School on Computational Social Science

If you are wondering what to do between July 26 and August 1, this summer, you might be interested in this years Lipari School on Computational Social Science which is focusing on Algorithms, Data, and Models for Social and Urban Systems

What will be taught at the summer school and why? To answer the these questions and to quote from the the homepage of the school:
 "Social and urban systems have been the focus of social science theory and research for centuries, but only until recently have computational approaches enabled novel explorations of challenging and enduring research questions and the opening of new frontiers for investigation. What is the role of Computational Social Science in advancing the science of social and urban systems? Which advanced algorithms and data structures play a key role in these investigations? In 2015 our Lipari Summer School in CSS will address questions such as the role of GIS (geographic/geospatial information systems), social media, big social data, agent-based models, network models, and their integration in the study, design, and implementation of social and urban systems. "
The speakers will be:
Special guest speakers will be:
To find out how to apply to attend the summer school click here. Students are encouraged to apply early because enrollment is competitive and limited.

Thursday, February 12, 2015

Village Model
What now seems like a very long long time ago, when I was getting up to speed with Agent-based modeling and GIS, I came across a great edited book entitled "Dynamics in Human and Primate Societies: Agent-Based Modeling of Social and Spatial Processes". 

One chapter in particular that I really enjoyed because of its clarity and use of data was by Kohler et al. (2000) entitled "Be There Then: A Modeling Approach to Settlement Determinants and Spatial Efficiency Among Late Ancestral Pueblo Populations of the Mesa Verde Region, U.S. Southwest". 

The chapter explored the question of why did Pueblo people vary their living arrangements between  compact villages and dispersed hamlets between 901-1287AD? To this day, I use this chapter when I am teaching about early agent-based models. While the initial model was implemented in Swarm, it has now been ported to Repast and developed further by an NSF supported program called Village Ecodynamics Project.

Full Reference:
Kohler, T.A., Kresl, J., Van Wes, Q., Carr, E. and Wilshusen, R.H. (2000), 'Be There Then: A Modeling Approach to Settlement Determinants and Spatial Efficiency Among Late Ancestral Pueblo Populations of the Mesa Verde Region, U.S. Southwest', in Kohler, T.A. and Gumerman, G.J. (eds.), Dynamics in Human and Primate Societies: Agent-Based Modeling of Social and Spatial Processes, Oxford University Press, Oxford, UK, pp. 145-178.

Monday, February 09, 2015

Geosimulation and Big Data: A Marriage made in Heaven or Hell? Schedule

Do you like big data and geosimulation and wondering when to book flights or which sessions to attend at the forthcoming AAG Annual Meeting,  If so, you might like our sessions entitled "Geosimulation and Big Data: A Marriage made in Heaven or Hell? " taking place on Wednesday the 22nd of April 2015.

Abstract of the Sessions:

In recent years, human emotions, intentions, moods and behaviors have been digitised to an extent previously unimagined in the social sciences. This has been in the main due to the rise of a vast array of new data, termed 'Big Data'.  These new forms of data have the potential to reshape the future directions of social science research, in particular the methods that scientists use to model and simulate spatially explicit social systems. Given the novelty of this potential "revolution" and the surprising lack of reliable behavioral insight to arise from Big Data research, it is an opportune time to assess the progress that has been made and consider the future directions of socio-spatial modelling in a world that is becoming increasingly well described by Big Data sources.

In these sessions we will have methodological, theoretical and empirical papers that that engage with any aspect of geospatial modelling and the use of Big Data. We are particularly interested in the ways that insight into individual or group behavior can be elucidated from new data sources - including social media contributions, volunteered geographical information, mobile telephone transactions, individually-sensed data, crowd-sourced information, etc. -  and used to improve models or simulations.  Topics include, but are not limited to:
  • Using Big Data to inform individual-based models of geographical systems;
  • Translating Big Data into agent rules;
  • Elucidating behavioral information from diverse data;
  • Improving simulated agent behavior;
  • Validating agent-based models (ABM) with Big Data;
  • Ethics of data collected en masse and their use in simulation.
2192 Geosimulation and Big Data: A Marriage made in Heaven or Hell? (1)

Wednesday, 4/22/2015.
8:00 AM - 9:40 AM.
600a Classroom, University of Chicago Gleacher Center, 6th Floor.

Chair: Nick Malleson 


*Atsushi Nara:
A GPGPU approach for simulating and analyzing human dynamics
*Kira KowalskaJohn Shawe-Taylor and Paul Longley:
 Data-driven modelling of police patrol activity 
*Martin Zaltz Austwick, Gustavo Romanillos Arroyo and Borka Moya-Gomez:
Simulating Rush Hour Bicycle Traffic in Madrid 
*Hai Lan  and Paul Torrens:
Voxel based Cellular Automata with massive cells for Geo-simulation: Ice dynamics simulation in Antarctic locations as example
*Philippe J. Giabbanelli, Thomas Burgoine, Pablo Monsivais and James Woodcock:
Using big data to develop individual-centric models of food behaviours

2292 Geosimulation and Big Data: A Marriage made in Heaven or Hell? (2) 

Wednesday, 4/22/2015.
10:00 AM - 11:40 AM.
600a Classroom, University of Chicago Gleacher Center, 6th Floor.

Chair: Alison Heppenstall


*Kostas Cheliotis:
Coupling Public Space Simulations with Real-Time Data Streams 
*Andrew Crooks and Sarah Wise:
Leveraging Crowdsourced data for Agent-based modeling: Opportunities, Examples and Challenges 
*Ed Manley, Chen Zhong and Michael Batty:
Towards Real-Time Simulation of Transportation Disruption - Building Agent Populations from Big Mobility Data 
*Alison Heppenstall, *Nick Malleson and Andrew Evans:
Evaluating Big Data demographics for population modelling 
Muhammad Adnan, Alistair Leak and *Paul Longley:
Exploring the geo-temporal patterns of Twitter messages

2492 Geosimulation and Big Data: A Marriage made in Heaven or Hell? (3) Discussion Session

Wednesday, 4/22/2015.
1:20 PM - 3:00 PM.
600a Classroom, University of Chicago Gleacher Center, 6th Floor.

Chair: Nick Malleson

*Paul M Torrens and Hai Lan:
Micro big data and geosimulation 
*Mark Birkin:
The Ten Commandments of Big Data 
 2:00 PM to 3:00PM: Discussion


  • Alison Heppenstall, School of Geography, University of Leeds
  • Nick Malleson, School of Geography, University of Leeds
  • Andrew Crooks, Department of Computational Social Science, George Mason University
  • Paul Torrens, Department of Geographical Sciences, University of Maryland
  • Ed Manley, Centre for Advanced Spatial Analysis, University College London

Wednesday, February 04, 2015

Agent-based models in a web browser

Sharing agent-based models over the web is never very easy. You can do it with NetLogo but it requires that your web browser supports  Java 5 (but this is not recommended). One could create a jar file for your model if you are using MASON, for example. But this still requires a number of steps before you see the model running. One way to bypass this is to build the model directly into the web page. While we have highlighted the use of JavaScript for Agent-based modeling in a previous post. Ernesto Carrella, a PhD  candidate from the Department of Computational Social Science has just created some proof of concept agent-based models exploring fishing using Dart which are overlaid on top of Google maps. If you want to find out more, checkout his models over on GitHub.

Saturday, January 31, 2015

New Book Chapter: Agent-based modelling and geographical information systems

Chris Brunsdon and Alex Singleton recently edited a book entitled "Geocomputation: A Practical Primer". The book covers a plethora of topics relating to geocomputation, and to quote from the website: "Chapters provide highly applied and practical discussions of:
      • Visualization and exploratory spatial data analysis
      • Space time modelling
      • Spatial algorithms
      • Spatial regression and statistics
      • Enabling interactions through the use of neogeography "
In the book I contributed a chapter on "Agent-based Models and Geographical Information Systems". Such a topic might not come as a surprise to readers of this blog but I essentially wanted to showcase a series of applications that we have been working on here at George Mason University and moreover, provide a general introduction to agent-based modeling (ABM) and how to link it to geographical information.

The main argument of the chapter is that the ABM paradigm provides a mechanism for understanding the effects of interactions of individuals and through such interactions emergent structures develop, both in the social and physical environment. By coupling agent-based models to geographical information systems (GIS), spatially explicit agent-based models can be created exploring the complexities of our world from the bottom-up. 

Representing the world as a series of layers of fixed and
non-fixed objects (adapted from Benenson and Torrens, 2004).

The chapter therefore introduces agent-based models to those interested in geocomputational methods, argues why such models should be used to study geographical problems before discussing how one can use GIS to create geographical explicit agent-based models. Through a series of examples we demonstrate how raster or vector spatial data can be used to model various aspects of our daily lives from that of the micro movement of pedestrians over seconds and minutes, to that of the macro patterns of urban growth over years and decades. By integrating spatial data and agent-based models at different spatial and temporal scales, such a modeling approach provides the flexibility to aid social scientists to explore the complex world that we inhabit.

Many of the models discussed in the chapter were either created in NetLogo or MASON. If you look at my research page you can find the source code to most of these models.  

A sample of application domains for spatial agent-based models discussed in the chapter.

A simple example using MASON on how Agent-based models can be to used explore rush hour congestion:
(A): Road and census data used for model inputs. (B) Zoomed in section of A with agents (red circles)
moving towards Tyson’s Corner and causing traffic jams.

Reference Cited:
Benenson, I. and Torrens, P.M. (2004), Geosimulation: Automata-Based Modelling of Urban Phenomena, John Wiley & Sons, London, UK.
Full Reference to Chapter:
Crooks, A.T. (2015), Agent-based Models and Geographical Information Systems, in Brunsdon, C. and Singleton, A. (eds.), Geocomputation: A Practical Primer, Sage, London, UK, pp. 63-77. (pdf)
Happy reading, and if you cannot find the models discussed in the chapter, feel free to drop me an email and I can point you in the right direction.

Thursday, January 08, 2015

Crowdsourcing Urban Form and Function

We have just had published a new paper entitled: "Crowdsourcing Urban Form and Function" in International Journal of Geographical Information Science which showcases some of our recent work with respect to cities and how new sources of information can be used to study urban morphology at a variety of spatial and temporal scales. Below is the abstract for the paper: 

"Urban form and function have been studied extensively in urban planning and geographic information science. However, gaining a greater understanding of how they merge to define the urban morphology remains a substantial scientific challenge. Towards this goal, this paper addresses the opportunities presented by the emergence of crowdsourced data to gain novel insights into form and function in urban spaces. We are focusing in particular on information harvested from social media and other open-source and volunteered datasets (e.g. trajectory and OpenStreetMap data). These data provide a first-hand account of form and function from the people who define urban space through their activities. This novel bottom-up approach to study these concepts complements traditional urban studies work to provide a new lens for studying urban activity. By synthesizing recent advancements in the analysis of open-source data we provide a new typology for characterizing the role of crowdsourcing in the study of urban morphology. We illustrate this new perspective by showing how social media, trajectory, and traffic data can be analyzed to capture the evolving nature of a city’s form and function. While these crowd contributions may be explicit or implicit in nature, they are giving rise to an emerging research agenda for monitoring, analyzing and modeling form and function for urban design and analysis."
This paper builds and extends considerably our prior work, with respect to crowdsourcing, volunteered and ambient geographic information. In the scope of this paper we use the term ‘urban form’ to refer to the aggregate of the physical shape of the city, its buildings, streets, and all other elements that make up the urban space. In essence, the geometry of the city. In contrast, we use the term ‘urban function’ to refer to the activities that are taking place within this space. To this end we contrast how crowdsourced data can related to more traditional sources of such information both explicitly and implicitly as shown in the table below. 

A typology of implicit and explicit form and function content

In addition, we also discuss in the paper how these new sources of data, which are often at finer resolutions than more authoritative data are allowing us to to customize the we we aggregate the data  at various geographical levels as shown below. Such aggregations can range from building footprints and addresses to street blocks (e.g. for density analysis), or street networks (e.g. for accessibility analysis). For large-scale urban analysis we can revert to the use of zonal geographies or grid systems.  
Aggregation methods for varied scales of built environment analysis

In the application section of the paper we highlight how we can extract implicit form and function from crowdsourced data. The image below for example, shows how we can take information from Twitter, and differentiate different neighborhoods over space and time.

Neighborhood map and topic modeling results showing the mixture of social functions in each area.

Finally in the paper, we outline an emerging research agenda related to the "persistent urban morphology concept" as shown below. Specifically how crowdsourcing is changing how we collect, analyze and model urban morphology. Moreover, how this new paradigm provides a new lens for studying the conceptualization of how cities operate, at much finer temporal, spatial, and social scales than we had been able to study so far.

The persistent urban morphology concept.

We hope you enjoy the paper.

Full Reference:  
Crooks, A.T., Pfoser, D., Jenkins, A., Croitoru, A., Stefanidis, A., Smith, D. A., Karagiorgou, S., Efentakis, A. and Lamprianidis, G. (2015), Crowdsourcing Urban Form and Function, International Journal of Geographical Information Science. DOI: 10.1080/13658816.2014.977905 (pdf)