A Million Page Views: Thank you

While I started blogging during my PhD (actually the first real post was from February 21st 2006), for some reason I only started recording statistics about the blog in May 2010. This month marks the milestone of over 1,000,000 page views. So I thought I would write a post that reflects this milestone. 

Initially, I started blogging as a way to keep track of agent-based models (ABM), example applications and toolkits I found interesting and this trend has continued over the years (with a few variations along the way). Many of my initial posts where focused on ways of utilizing agent-based models and integrating geographical information into such models.  However, over time I have also branched out into writing about other areas such as the utility of volunteered geographical information and social media to monitor, analyze and model urban systems and how one can use such data to study the connections between people via social networks. 

From looking at the statistics, since 2010 the most popular post (as you can see from the image below) is that of  Agent-based modeling in ArcGIS  (unfortunately this work is currently not being updated:( ) but it does show an interest in agent-based modeling in more of the mainstream GIS (or at least from some people). The other posts in the top 10 relate to modeling and analyzing urban systems and the people within them in some shape or form including a book book review I did for  JASSS. Perhaps my favorite post in this top 10 is that of Modeling Human Behavior   inspired by a book chapter written Bill Kennedy entitled 'Modelling Human Behavior in Agent-Based Models'.

With respect to the audience of the blog, nearly 48% of page views come from the United States while the reminder come from all around the world (as you can see from from the figure to the left, including France, Russia and the Ukraine). The most popular search terms for people coming to the blog include "agent based modeling", "NetLogo GIS" "NetLogo Examples" along with terms such such as urban analytics and big data. 

Looking at what web browsers and which operating systems people are using to access the site (which takes me back to my Masters thesis when I was working on developing web-mapping features for the Gazetteer for Scotland), Chrome makes up 43% of all page views  followed by Firefox (29%) and IE (16%). While for operating systems, 54% of visitors are using Windows, followed by Macintosh (27%) and Linux (8%).

While I mentioned above my favorite post in the top 10, reflecting on which post I refer most people to, it has to be the one entitled Applications of Agent-based Models because it shows how agent-based models are being used in a variety of settings. Looking back on the evolution of GIS and agent-based modeling since I started blogging, its impressive to see how different toolkits have started to utilize GIS. For example my first post was a hack on how to integrate GIS into NetLogo, from backspaces.net. Since then NetLogo, MASON and other platforms such as GAMA have evolved to allow making it (relatively) easier for the integration and exploration of geographical information and agent-based models. 

Moreover, when I started writing about this, there were very few example GIS and agent-based models (expect from Repast ones) or resources to get up and running with agent-based models but over time this has changed with more and more people sharing their models (thanks to things like GitHub (e.g. mason models, OpenABM)). There has also been a number of good text books written on GIS and ABM (and there is a great one coming soon from us) along with more blogs (e.g. Simulating Complexity) and courses being taught (e.g. Agent-Based Modeling Short Course at SESYNC). Lets hope this growth continues and thank you for reading and visiting this blog. If you would like to share your work on ABM and GIS please feel free to contact me or leave a comment a below.

Online Vaccination Discussion and Communities in Twitter

Continuing on our work of exploring health related issues in social media, Xiaoyi Yuan and myself had a paper accepted at the 9th International Conference on Social Media and Society. In our paper entitled: "Examining Online Vaccination Discussion and Communities in Twitter"  we examined the communication patterns of anti-vaccine and pro-vaccine users on Twitter by studying the retweet network from 660,892 tweets related to the measles, mumps, and rubella (MMR) vaccine published by 269,623 users using supervised learning to identify clusters of users based on their opinions (i.e. a pro-vaccine, anti-vaccine, or neutral user). 

The overall methodology can be seen in Figure 1 and more details can be found in the paper. Our data was collected using the GeoSocial Gauge System, however, since tweets are short and their content diverse, the data corpus needed to be cleaned so that the tweets could then be converted to features (e.g., unigrams or bigrams). After which we were able to use such features for training a variety of classifiers (i.e., logistic regression, support vector machine (linear and non-linear kernel), k-nearest neighbors, nearest centroid, and Naïve Bayes) to identify opinion groups. After this, we moved from on from identifying each user’s opinion to construct a retweet network in order to understand how in-group and cross-group communicate in the committees detected via retweet network. By carrying out this analysis we discovered that pro- and anti-vaccine users retweet predominantly from their own opinion group, while users with neutral opinions are distributed across communities. Below you can read our abstract, see some results from our study and the full reference (and link) to the paper.

Figure1: Steps used in our study to unveil the communication patterns of pro-vaccine and anti-vaccine users on Twitter

 Abstract:

Many states in the US allow a “belief exemption” for measles, mumps, and rubella (MMR) vaccines. People’s opinion on whether or not to take the vaccine could have direct consequences in public health— once the vaccine refusal of a group within a population is higher than what herd immunity can tolerate, a disease can transmit fast causing large scale of disease outbreaks. Social media has been one of the dominant communication channels for people to express their opinions of vaccination. Despite governmental organizations’ effects of disseminating information of vaccination benefits, anti-vaccine sentiment is still gaining its momentum, especially on social media. This research investigates the communicative patterns of anti-vaccine and pro-vaccine users on Twitter by studying the retweet network from 660,892 tweets related to MMR vaccine published by 269,623 users after the 2015 California Disneyland measles outbreak. Using supervised learning, we classified the users into anti-vaccination, neutral to vaccination, and pro-vaccination groups. Using a combination of opinion groups and retweet network structural community detection, we discovered that pro- and anti-vaccine users retweet predominantly from their own opinion group, while users with neutral opinions are distributed across communities. For most cross-group communication, it was found that pro-vaccination users were retweeting anti-vaccination users than vice-versa. The paper concludes that anti-vaccine Twitter users are highly clustered and enclosed communities, and this makes it difficult for health organizations to penetrate and counter opinionated information. We believe that this finding may be useful in developing strategies for health communication of vaccination and overcome some the limits of current strategies.

Key Words: Anti-vaccine movement, Twitter, social media, opinion classification

Figure 2: Network visualizations of the four largest communities. A: is colored by the belonging to a specific structural community and; B: is colored by belonging to opinion groups

Figure 3: Distributions of opinion groups in the four largest structural community

Full Reference:

Yuan, X. and Crooks, A.T. (2018), Examining Online Vaccination Discussion and Communities in Twitter, Proceedings of the 9th International Conference on Social Media and Society, Copenhagen, Denmark, pp 197-206. (pdf)

Update: Our paper was selected as best paper at the conference.

MASON Update

At the upcoming Multi-Agent-Based Simulation (MABS) workshop, we have a paper entitled "The MASON Simulation Toolkit: Past, Present, and Future" in which we discuss MASON's development history, its design and (probably more interesting) where MASON is going. This includes:

1. Making it more robust (i.e. easier to run parameter tests), 
2. Making it distributed in order to  run large scale models including geographical explicit ones along for optimization and validation purposes.
3. Making it more coder-friendly by adding code templates that allow users to generate code skeletons for common MASON patterns and a way to easily record outputs and statistics.
4. Making it more community-friendly by hopefully developing a special online repository to enable researchers to distribute models as jar files along with education aids and examples. Relating to this last point we have added a number of example models (code and data) from our own research to GitHub, see: https://github.com/eclab/mason/tree/master/contrib/geomason/sim/app/geo and the data to run the models is either there or here https://cs.gmu.edu/~eclab/projects/mason/extensions/geomason/geodemodata.zip (note this is 1.5 GB).

Below you can read the abstract from the paper along with a link to the paper itself.

Abstract

MASON is a widely-used open-source agent-based simulation toolkit that has been in constant development since 2002. MASON’s architecture was cutting-edge for its time, but advances in computer technology now offer new opportunities for the ABM community to scale models and apply new modeling techniques. We are extending MASON to provide these opportunities in response to community feedback. In this paper we discuss MASON, its history and design, and how we plan to improve and extend it over the next several years. Based on user feedback will add distributed simulation, distributed GIS, optimization and sensitivity analysis tools, external language and development environment support, statistics facilities, collaborative archives, and educational tools.

Keywords: Agent-Based Simulation, Open Source, Library

Full Reference:

Luke, S., Simon, R., Crooks, A.T., Wang, H., Wei, E., Freelan, D., Spagnuolo, C., Scarano, V., Cordasco, G. and Cioffi-Revilla, C. (2018), The MASON Simulation Toolkit: Past, Present, and Future, 19th International Workshop on Multi-Agent-Based Simulation (MABS2018), Stockholm, Sweden. (pdf)

This research is supported by the National Science Foundation (Grant 1727303).

Call for Papers: GeoSim’18

The GeoSim’18 workshop 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. Also, this workshop is 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.

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, as well as attend the workshop to present the accepted work which will then appear in the ACM Digital Library.

Example topics include, but not limited to:

• Applications for Spatial 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
• Geoinformation Systems 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
• Urban Simulation
 

Important Dates:

• Submission deadline: August 20, 2018
• Notification: September 20, 2018
• Workshop date: November 06, 2018

For more information please visit www.geosim.org

Spatial Agent-based Models of Human-Environment Interactions: Spring 2018

During the past spring semester I taught a class entitled "Spatial Agent-based Models of Human-Environment Interactions". As with many of my courses, students are 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. In the movie below is a selection of these projects can be seen. The projects ranged from urban growth, housing markets, the adoption of solar energy, employment opportunities, populations at risk from terrorism, commuting, to the spread of diseases. Many of the models were done in NetLogo, MASON and some in Python including using MESA.




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