Friday, November 17, 2017

New Paper: Social Media and Cancer Campaigns

Continuing our work on geosocial analysis we recently had a paper entitled "Social Media Engagement with Cancer Awareness Campaigns Declined During the 2016 U.S. Presidential Election" published in  World Medical and Health Policy. In the paper we show through the analysis of Twitter and Google Trends, how public engagement with breast cancer and prostate cancer awareness months between 2015 and 2016 changed. Specifically we found that attention to breast cancer and prostate cancer declined in 2016 (during the U.S. presidential election), when compared to 2015. Based on our finding we suggest that future cancer education campaigns—and campaigns for other health issues and policies—would benefit from monitoring the broader issues producing social media engagement, and adjusting their timing or communication strategies to ensure that public engagement with their key messages remains strong even in a crowded social media marketplace. Below you can read the abstract to our paper, see some of our key findings along with the full citation to the paper.

Cancer awareness campaigns compete with other health and social issues for public attention. We examined whether public engagement with breast cancer and prostate cancer declined in 2016 during the U.S. presidential election compared to 2015 on Twitter and Google Trends. We found that attention to breast cancer and prostate cancer declined in 2016 before the election as compared to 2015 in Twitter posts and Google searches. The findings suggest that cancer information seeking behavior, passive exposure to health communication, and active participation in social media about cancer all decreased during the peak weeks of the 2016 election season. Future health promotion initiatives and information dissemination efforts will benefit from monitoring the major issues garnering social media attention and then adjusting their timing or communication strategies to ensure that public engagement with their key policy messages remains strong when emerging news stories capture public interest.

Keywords: Twitter; breast cancer; prostate cancer

Twitter Traffic about Breast Cancer (a) and Prostate Cancer (b) in the Period October 2015–November 2016, and Comparison of October and November Traffic in 2015 and 2016 for Breast Cancer (c) and Prostate Cancer (d). All 2015 Data are Shown in Red and All 2016 Data are Shown in Blue.

Full Reference:
Vraga, E. K., Radzikowski, J., Stefanidis, A., Croitoru, A., Crooks, A.T., Delamater, P., Pfoser, D. and Jacobsen, K. H. (2017). Social Media Engagement with Cancer Awareness Campaigns Declined During the 2016 U.S. Presidential Election. World Medical and Health Policy, 9(4): 456–465. (pdf)

Saturday, November 04, 2017

Call For Papers: Spatial ABMs: Current Practices & Future Trends

Special Issue theme: Spatial Agent-Based Models: Current Practices and Future Trends

Editors: Alison Heppenstall (Leeds) and Andrew Crooks (GMU)

Journal: GeoInformatica

Impact rating: 2.3


Over the last decade the agent-based modeling (ABM) paradigm has provided a new lens for understanding the effects of interactions of individuals and how through such interactions macro structures emerge, both in the social and physical environment of spatial systems. However, such a paradigm has been hindered due to computational power and a lack of large fine scale datasets. Within the last few years we have witnessed a massive increase in computational processing power and storage, combined with the onset of Big Data. Today geographers find themselves in a data rich era. We now have access to a variety of data sources (e.g., social media, mobile phone data, etc.) that tells us how, and when, individuals are using spaces. These data raise several questions: can we effectively use them to understand and model spatial systems as complex entities? How well have ABM approaches lent themselves to simulating the dynamics of spatial processes? What has been, or will be, the influence of Big Data on increasing our ability to understand and simulate spatial systems? What is the appropriate level of spatial analysis and time frame to model spatial phenomena? This special issue will concentrate on the best of current practice and future trends. We are interested in papers that will introduce the reader to:
  • Applications: Well-developed and transparent applications; 
  • Methodological innovations: use of ‘big data’; machine learning methods; calibration and validation methods within agent-based models; 
  • Thought pieces: What is the future of ABM? What do ABMs need to achieve to become as accepted similar to methods from climate change? How have and can agent-based models be used for policy making? 

Indicative deadlines 
  • Abstract (250 words): December 8, 2017 
  • Full Paper: April 30, 2018 

Abstracts must first be submitted directly to the guest editors via email:

Further submission will then be invited based on the content assessed in the abstract. 

Full papers need to be between 5000 – 7000 words in length. Details of the journal submission requirements can be found at :

Tuesday, October 31, 2017

Happy Halloween....

As today is Halloween, I thought I would write a brief post on zombies and how they can be used to demonstrate disease models (even the Centers for Disease Control and Prevention (CDC) has a post about Zombies preparedness). There are several good examples of using zombie outbreaks as demonstrations for the utility of modeling (or just showing how modeling concepts can be applied to the spread of zombies). 

These range from exploring  the spatial and temporal dynamics of a zombie epidemic (e.g. Sander and Topaz, 2014). To that of the work of Alemi et al. (2015), who produced a "danger map" of what would happen if the continental United States  was overrun with zombies (an interactive version is available here and shown below). In their paper, they demonstrate how epidemiological processes akin to a Susceptible-Infected-Recovered (SIR) model (which we have wrote about before) can be used to model the spread of zombies. As its a zombie model, the SIR changes to a SZR model (Munz et al., 2009), where:
 "S represents the susceptible population, the uninfected humans, Z represents the infected state, zombies, and R represents our removed state, in  this  case  zombies  that  have  been  terminated  by  humans (canonically  by  destroying  their  brain  so  as  to  render  them inoperable)." (Alemi et al., 2015)
Zombie Town USA

Below you can see an attempt of modeling a zombie outbreak (only the SI parts) in one of the buildings on the George Mason Fairfax campus utilizing NetLogo (you can download the model code from here).

More complex individual based models have also been created like the one shown below by Horio and Arrowsmith (2015) which was used to showcase how zombies can be used to describe complex adaptive systems and agent-based modeling.

If readers know of any over good Halloween (horror) like models, please let us know.

Alemi, A.A., Bierbaum, M., Myers, C.R. and Sethna, J.P. (2015), 'You Can Run, You Can Hide: The Epidemiology and Statistical Mechanics of Zombies', Physical Review E, 92(5): 052801.
Horio, B. and Arrowsmith, N. (2015), 'The Pedagogy of Zombies', OR/MS Today, 42(5).
Munz, P., Hudea, I., Imad, J. and Smith, R.J. (2009), 'When Zombies Attack!: Mathematical Modelling of an Outbreak of Zombie Infection', in Tchuenche, J.M. and Chiyaka, C. (eds.), Infectious Disease Modelling Research Progress, Nova Science Publishers, Hauppauge, NY, pp. 133-150.
Sander, E. and Topaz, C.M. (2014), 'The Zombie Swarm: Epidemics In The Presence of Social Attraction And Repulsion', in Smith, R. (ed.) Mathematical Modelling of Zombies, University of Ottawa Press, Ottawa, Canada, pp. 265-300.

Friday, October 13, 2017

AAG2018: Innovations in Urban Analytics

Call for Papers, AAG2018: Innovations in Urban Analytics

We welcome paper submissions for our session at the Association of American Geographers Annual Meeting on 10-14 April, 2018, in New Orleans.

Session Description

New forms of data about people and cities, often termed ‘Big’, are fostering research that is disrupting many traditional fields. This is true in geography, and especially in those more technical branches of the discipline such as computational geography / geocomputation, spatial analytics and statistics, geographical data science, etc. These new forms of micro-level data have lead to new methodological approaches in order to better understand how urban systems behave. Increasingly, these approaches and data are being used to ask questions about how cities can be made more sustainable and efficient in the future.

This session will bring together the latest research in urban analytics. We are particularly interested in papers that engage with the following domains:
  • Agent-based modelling (ABM) and individual-based modelling;
  • Machine learning for urban analytics;
  • Innovations in consumer data analytics for understanding urban systems;
  • Real-time model calibration and data assimilation;
  • Spatio-temporal data analysis;
  • New data, case studies, demonstrators, and tools for the study of urban systems;
  • Complex systems analysis;
  • Geographic data mining and visualization;
  • Frequentist and Bayesian approaches to modelling cities.

Please e-mail the abstract and key words with your expression of intent to Nick Malleson ( by 18 October, 2017 (one week before the AAG abstract 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.

For those interested specifically in the interface between research and policy, they might consider submitting their paper to the session “Computation for Public Engagement in Complex Problems” (

Key Dates
  • 18 October, 2017: 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.
  • 23 October, 2017: Session finalization and author notification.
  • 25 October, 2017: Final abstract submission to AAG, via the link above. 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.
  • 8 November, 2017: AAG session organization deadline. Sessions submitted to AAG for approval.
  • 9-14 April, 2018: AAG Annual Meeting.

Session Organizers

Saturday, October 07, 2017

Generation of Realistic Mega-City Populations and Social Networks for ABM

At the upcoming 2017 Annual conference of the Computational Social Science Society of the Americas, Annetta Burger, Talha Oz, William Kennedy and myself have a paper entitled: "Generation of Realistic Mega-City Populations and Social Networks for Agent-Based Modeling". 

In the paper we discuss some of our current work of generating synthetic human populations with realistic social networks with respect to the New York mega-city and surrounding region. Below you can read the abstract of the paper and see our workflow along with some initial results. The full reference to the paper and a link to the pdf can be found at the bottom of the post.

Agent-based modeling is a means for researchers to conduct large-scale computer experiments on synthetic human populations and study their behaviors under different conditions. These models have been applied to questions regarding disease spread in epidemiology, terrorist and criminal activity in sociology, and traffic and commuting patterns in urban studies. However, developing realistic control populations remains a key challenge for the research and experimentation. Modelers must balance the need for representative, heterogeneous populations with the computational costs of developing large population sets. Increasingly these models also need to include the social network relationships within populations that influence social interactions and behavioral patterns. To address this we used a mixed method of iterative proportional fitting and network generation to build a synthesized subset population of the New York megacity and region. Our approach demonstrates how a robust population and social network relevant to specific human behavior can be synthesized for agent-based models. 

Keywords: Agent-based Models, Geographical Systems, Population Synthesis, Social Networks, Megacity.

Full Reference: 
Burger, A., Oz, T., Crooks, A.T. and Kennedy, W.G. (2017). Generation of Realistic Mega-City Populations and Social Networks for Agent-Based Modeling, The Computational Social Science Society of Americas Conference, Santa Fe, NM. (pdf)