Wednesday, October 29, 2014

Happy Halloween: Zombie Agent-Based model

In preparation for Halloween,  last night in class we explored a simple agent-based model of zombie attack. At each time step, a human moves to a nearby unoccupied space, and a zombie moves to the nearest human. If a zombie and an uninoculated human occupy the same space, a fierce battle ensues, in which the probability that the human will kill the zombie is pkH-z, and the probability that the zombie kills the human and converts them to their horrific undead form is pkZ-h. 

Zombies, however, are not attracted to inoculated humans and ignore them. If recovery? is enabled, then there is a chance (given by recoveryRate) that a zombified person will see the errors of their cannibalistic ways and return to human form. All these factors working together provide some interesting population dynamics, illustrated by the “Totals” population count plot on the screen. 

The model is programed in NetLogo and also demonstrates how a simple as .asc file can be used for the spatial envirment for the zombies and humans to interact within.



You can download the code from here.

Tuesday, September 23, 2014

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

http://www.pinterest.com/pin/101753272804937744/
Call for papers: AAG 2015 – Geosimulation and Big Data: A Marriage made in Heaven or Hell?

In recent years, human emotions, intentions, moods and behaviors have been digitized 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 behavioural 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.

We invite 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.
Please e-mail the abstract and key words with your expression of intent to Nick Malleson by 28th October, 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 http://www.aag.org/cs/annualmeeting/call_for_papers. An abstract should be no more than 250 words that describe the presentation’s purpose, methods, and conclusions as well as to include keywords.


Organizers
  • 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
Timeline
  • 28th October, 2014: 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.
  • 31st October, 2014: Session finalization and author notification
  • 3rd November, 2014: Final abstract submission to AAG, via www.aag.org. 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.
  • 5th November, 2014: AAG registration deadline. Sessions submitted to AAG for approval.
 

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.