Thursday, September 18, 2014

New Paper: ABM Applied to the Spread of Cholera

Cholera transmission through the interaction
of host and the environment
We are pleased to announce we have just had a paper published in Environmental Modelling and Software entitled "An Agent-based Modeling Approach Applied to the Spread of Cholera"

Research highlights include:
  • An agent-based model was developed to explore the spread of cholera.
  • The progress of cholera transmission is represented through a Susceptible-Exposed-Infected-Recovered (SEIR) model. 
  • The model integrates geographical data with agents’ daily activities within a refugee camp.
  • Results show cholera infections are impacted by agents’ movement and source of contamination. 
  • The model has the potential for aiding humanitarian response with respect to disease outbreaks.
Cholera dynamics when rainfall is introduced.

Spatial spread of cholera over the course of a year.

Study area
If the research highlights have not turned you off, the abstract to the paper is below:
"Cholera is an intestinal disease and is characterized by diarrhea and severe dehydration. While cholera has mainly been eliminated in regions that can provide clean water, adequate hygiene and proper sanitation; it remains a constant threat in many parts of Africa and Asia. Within this paper, we develop an agent-based model that explores the spread of cholera in the Dadaab refugee camp in Kenya. Poor sanitation and housing conditions contribute to frequent incidents of cholera outbreaks within this camp. We model the spread of cholera by explicitly representing the interaction between humans and their environment, and the spread of the epidemic using a Susceptible-Exposed-Infected-Recovered model. Results from the model show that the spread of cholera grows radially from contaminated water sources and seasonal rains can cause the emergence of cholera outbreaks. This modeling effort highlights the potential of agent-based modeling to explore the spread of cholera in a humanitarian context."
Finally to aide replication, experimentation or just explore how you can link raster and vector data in GeoMason, we have a dedicated website where you can download executables of the model along with the source code and associated data. Moreover we have provide a really detailed Overview, Design concepts, and Details (ODD) Protocol document of the model.

Full Reference:
Crooks, A.T. and Hailegiorgis, A.B. (2014), An Agent-based Modeling Approach Applied to the Spread of Cholera, Environmental Modelling and Software, 62: 164-177
DOI: 10.1016/j.envsoft.2014.08.027 (pdf)

Wednesday, September 17, 2014

Triangulating Social Multimedia Content for Event Localization

As regular visitors will know, we have been developing our ability to collect and analyze social media. To this end we have just received word from Transactions in GIS that our paper entitled "Triangulating Social Multimedia Content for Event Localization using Flickr and Twitter" has just been accepted. Below is the abstract from the paper:
The analysis of social media content for the extraction geospatial information and event-related knowledge has recently received substantial attention. In this paper we present an approach that leverages the complementary nature of social multimedia content by utilizing heterogeneous sources of social media feeds to assess the impact area of a natural disaster. More specifically, we introduce a novel social multimedia triangulation process that uses jointly Twitter and Flickr content in an integrated two-step process: Twitter content is used to identify toponym references associated with a disaster; this information is then used to provide approximate orientation for the associated Flickr imagery, allowing us to delineate the impact area as the overlap of multiple view footprints. In this approach, we practically crowdsource approximate orientations from Twitter content and use this information to orient accordingly Flickr imagery and identify the impact area through viewshed analysis and viewpoint integration. This approach enables us to avoid computationally intensive image analysis tasks associated with traditional image orientation, while allowing us to triangulate numerous images by having them pointed towards the crowdsourced toponym location. The paper presents our approach and demonstrates its performance using a real-world wildfire event as a representative application case study.
 Our cross-source triangulation framework is outlined in the figure below:

The cross-source triangulation framework.
To demonstrate the benefit of using cross-sourced social media in the triangulation process we applied three modes of the analysis:
  • Mode 1: the impact area was estimated as the overlap of all viewsheds that were generated from all Flickr contribution locations without calculating a reference point or evaluating the Angle Of View (AOV) for each image. Accordingly, in this mode, we use only Flickr data, without constraining the viewshed analysis with any AOV information. 
  • Mode 2: the impact area was estimated by using the centroid of the locations of all Flickr contributions as the reference point for the AOV calculation, followed by a viewshed analysis of each image. Accordingly, in this mode we use only Flickr data, ignoring any toponym information from Twitter. 
  • Mode 3: the impact area was estimated by using the toponym reference, as derived from Twitter, as the reference point for the AOV calculation, followed by a viewshed analysis of each image. Accordingly, in this mode we use Twitter content to orient Flickr data and guide the viewshed analysis.
The figure below shows the result from Mode 3:

A three-dimensional perspective of wildfire location assessment as derived by analysis mode 3.

Full Reference: 
Panteras, G., Wise, S., Lu, X., Croitoru, A., Crooks, A.T. and Stefanidis, A. (in press), Triangulating Social Multimedia Content for Event Localization using Flickr and Twitter, Transactions in GIS.

Friday, September 12, 2014

Scottish Referendum conversation as seen on Twitter

There is a lot of talk about Scotland's up and coming referendum for independence. If you are interested in what people are saying live on Twitter you can take a look at our GeoSocial Gauge website.

What you will see is a map showing the location of tweets (which you can click on to find more information), along with options for visualizing the intensity of Twitter activity, and whether or not the tweets are negative or positive. You can also see the overall mood of the conversion and the keywords from the tweets. 

To see more of our GeoSocial projects click here.

Call for Papers: New Directions in Geospatial Simulation

New Directions In Geospatial Simulation

The geospatial simulation community has enjoyed steady growth over the past decade as novel and advanced forms of agent-based and cellular automata modeling continue to facilitate the exploration of complex geographic problems facing the world today. It is now an opportune time to consider the future direction of this community and explore ways to leverage geospatial simulation in professional arenas. The aim of these sessions is to bring together researchers utilizing agent-based and cellular automata techniques and associated methodologies to discuss new directions in geospatial simulation. We invite papers that fall into one of the following four categories:
  • Graduate student geospatial simulation research
  • Methodological advances of agent-based or cellular automata modeling
  • New application frontiers in geospatial simulation
  • Approaches for evaluating the credibility of geospatial simulation models
Student papers will be presented in an interactive short paper session with presentations no longer than five minutes and no more than ten slides. Following presentations, students will form a panel that will address questions from the audience as directed by the session moderator. Student presentations will be judged as a part of a Best Student Paper award, the winner of which will receive an award of $500.

All other papers will be placed in one of the following three sessions: (1) Methodological Advances, (2) Novel Applications, or (3) Model Credibility. Each session will be comprised of four speakers followed by a twenty-minute discussion on the session topic.

Please e-mail the abstract and key words with your expression of intent to Chris Bone by October 28, 2014. 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 as well as to include keywords. Full submissions will be given priority over submissions with just a paper title.

Chris Bone, Department of Geography, University of Oregon
Andrew Crooks, Department of Computational Social Science, George Mason University
Alison Heppenstall, School of Geography, University of Leeds
Arika Ligmann-Zielinska, Department of Geography, Michigan State University
David O’Sullivan, Department of Geography, University of California, Berkeley


October 14th, 2014: Second call for papers

October 28th, 2014: Abstract submission and expression of intent to session organizers. E-mail Chris Bone by this date if you are interested in being in this session. Please submit an abstract and key words with your expression of intent. Full submissions will be given priority over submissions with just a paper title.

October 31st, 2014: Session finalization. Session organizers determine session order and content and notify authors.

November 3rd, 2014: 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 Chris Bone. Neither the organizers nor the AAG will edit the abstracts.

November 5th, 2014: AAG registration deadline. Sessions submitted to AAG for approval.

April 21-25, 2014: AAG meeting, Chicago, Illinois, USA.

Friday, August 29, 2014

Herding Sheep
Have you ever wondered how a farmer and a single sheep dog can herd sheep? A recent paper in Journal of the Royal Society Interface explains just how. Using GPS data from collars researchers have developed a computational model which  "reproduces key features of empirical data collected from sheep–dog interaction". The model has two simple rules:
"The first rule: The sheepdog learns how to make the sheep come together in a flock. The second rule: Whenever the sheep are in a tightly knit group, the dog pushes them forwards." (BBC)
The movie below (which accompanies the paper) shows some simulation runs with different numbers of agents. The shepherd (blue) approaches and rounds up the agents/sheep (black dots) and then proceed to herd the group toward the target.

Full Reference:
Strömbom. D. Mann, R. P., Wilson, A. M., Hailes, S., Morton, A. J., Sumpter, D. J. T., King, A. J. (2014) Solving the shepherding problem: Heuristics for herding autonomous, interacting agents. Journal of the Royal Society Interface, 11: 20140719.
Thanks to @Badnetworker for drawing my attention to this.